Merge branch 'master' into v3-definition

V3 update - make schema imports available on non-latest API

.ci/update_windows/update.py

@@ -53,16 +53,6 @@ try:
     repo.stash(ident)
 except KeyError:
     print("nothing to stash")  # noqa: T201
-except:
-    print("Could not stash, cleaning index and trying again.")  # noqa: T201
-    repo.state_cleanup()
-    repo.index.read_tree(repo.head.peel().tree)
-    repo.index.write()
-    try:
-        repo.stash(ident)
-    except KeyError:
-        print("nothing to stash.")  # noqa: T201
-
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
 print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:
@@ -76,10 +66,8 @@ if branch is None:
     try:
         ref = repo.lookup_reference('refs/remotes/origin/master')
     except:
-        print("fetching.")  # noqa: T201
-        for remote in repo.remotes:
-            if remote.name == "origin":
-                remote.fetch()
+        print("pulling.")  # noqa: T201
+        pull(repo)
         ref = repo.lookup_reference('refs/remotes/origin/master')
     repo.checkout(ref)
     branch = repo.lookup_branch('master')
@@ -161,4 +149,3 @@ try:
         shutil.copy(stable_update_script, stable_update_script_to)
 except:
     pass
-

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -1,28 +0,0 @@
-As of the time of writing this you need this driver for best results:
-https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
-
-HOW TO RUN:
-
-If you have a AMD gpu:
-
-run_amd_gpu.bat
-
-If you have memory issues you can try disabling the smart memory management by running comfyui with:
-
-run_amd_gpu_disable_smart_memory.bat
-
-IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
-
-You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
-
-
-RECOMMENDED WAY TO UPDATE:
-To update the ComfyUI code: update\update_comfyui.bat
-
-
-TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
-In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
-Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
-
-
-

.ci/windows_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat

@@ -1,2 +1,2 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation
 pause

.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat

@@ -1,3 +0,0 @@
-..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
-pause

.ci/windows_nvidia_base_files/run_nvidia_gpu.bat

@@ -1,3 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
-pause

.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat

@@ -1,3 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
-pause

.gitattributes

@@ -1,3 +1,2 @@
 /web/assets/** linguist-generated
 /web/** linguist-vendored
-comfy_api_nodes/apis/__init__.py linguist-generated

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -8,15 +8,13 @@ body:
         Before submitting a **Bug Report**, please ensure the following:
 
         - **1:** You are running the latest version of ComfyUI.
-        - **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report.
+        - **2:** You have looked at the existing bug reports and made sure this isn't already reported.
         - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
-        `--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.
+        `--disable-all-custom-nodes` command line argument.
         - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
         steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
-        ## Very Important
-
-        Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
+        If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
   - type: checkboxes
     id: custom-nodes-test
     attributes:
@@ -24,7 +22,7 @@ body:
       description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
       options:
         - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
-          required: false
+          required: true
   - type: textarea
     attributes:
       label: Expected Behavior

.github/ISSUE_TEMPLATE/user-support.yml

@@ -18,7 +18,7 @@ body:
         description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
         options:
           - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
-            required: false
+            required: true
     - type: textarea
       attributes:
             label: Your question

.github/PULL_REQUEST_TEMPLATE/api-node.md

@@ -1,21 +0,0 @@
-<!-- API_NODE_PR_CHECKLIST: do not remove -->
-
-## API Node PR Checklist
-
-### Scope
-- [ ] **Is API Node Change**
-
-### Pricing & Billing
-- [ ] **Need pricing update**
-- [ ] **No pricing update**
-
-If **Need pricing update**:
-- [ ] Metronome rate cards updated
-- [ ] Auto‑billing tests updated and passing
-
-### QA
-- [ ] **QA done**
-- [ ] **QA not required**
-
-### Comms
-- [ ] Informed **Kosinkadink**

.github/workflows/api-node-template.yml

@@ -1,58 +0,0 @@
-name: Append API Node PR template
-
-on:
-  pull_request_target:
-    types: [opened, reopened, synchronize, ready_for_review]
-    paths:
-      - 'comfy_api_nodes/**'   # only run if these files changed
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  inject:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Ensure template exists and append to PR body
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const { owner, repo } = context.repo;
-            const number = context.payload.pull_request.number;
-            const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md';
-            const marker = '<!-- API_NODE_PR_CHECKLIST: do not remove -->';
-
-            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number });
-
-            let templateText;
-            try {
-              const res = await github.rest.repos.getContent({
-                owner,
-                repo,
-                path: templatePath,
-                ref: pr.base.ref
-              });
-              const buf = Buffer.from(res.data.content, res.data.encoding || 'base64');
-              templateText = buf.toString('utf8');
-            } catch (e) {
-              core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`);
-              return;
-            }
-
-            // Enforce the presence of the marker inside the template (for idempotence)
-            if (!templateText.includes(marker)) {
-              core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`);
-              return;
-            }
-
-            // If the PR already contains the marker, do not append again.
-            const body = pr.body || '';
-            if (body.includes(marker)) {
-              core.info('Template already present in PR body; nothing to inject.');
-              return;
-            }
-
-            const newBody = (body ? body + '\n\n' : '') + templateText + '\n';
-            await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody });
-            core.notice('API Node template appended to PR description.');

.github/workflows/release-stable-all.yml

@@ -1,78 +0,0 @@
-name: "Release Stable All Portable Versions"
-
-on:
-  workflow_dispatch:
-    inputs:
-      git_tag:
-        description: 'Git tag'
-        required: true
-        type: string
-
-jobs:
-  release_nvidia_default:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA Default (cu130)"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu130"
-      python_minor: "13"
-      python_patch: "11"
-      rel_name: "nvidia"
-      rel_extra_name: ""
-      test_release: true
-    secrets: inherit
-
-  release_nvidia_cu128:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA cu128"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu128"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "nvidia"
-      rel_extra_name: "_cu128"
-      test_release: true
-    secrets: inherit
-
-  release_nvidia_cu126:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA cu126"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu126"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "nvidia"
-      rel_extra_name: "_cu126"
-      test_release: true
-    secrets: inherit
-
-  release_amd_rocm:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release AMD ROCm 7.2"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "rocm72"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "amd"
-      rel_extra_name: ""
-      test_release: false
-    secrets: inherit

.github/workflows/ruff.yml

@@ -21,28 +21,3 @@ jobs:
 
     - name: Run Ruff
       run: ruff check .
-
-  pylint:
-    name: Run Pylint
-    runs-on: ubuntu-latest
-
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
-
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
-
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-
-    - name: Install Pylint
-      run: pip install pylint
-
-    - name: Run Pylint
-      run: pylint comfy_api_nodes

.github/workflows/stable-release.yml

@@ -2,78 +2,28 @@
 name: "Release Stable Version"
 
 on:
-  workflow_call:
-    inputs:
-      git_tag:
-        description: 'Git tag'
-        required: true
-        type: string
-      cache_tag:
-        description: 'Cached dependencies tag'
-        required: true
-        type: string
-        default: "cu129"
-      python_minor:
-        description: 'Python minor version'
-        required: true
-        type: string
-        default: "13"
-      python_patch:
-        description: 'Python patch version'
-        required: true
-        type: string
-        default: "6"
-      rel_name:
-        description: 'Release name'
-        required: true
-        type: string
-        default: "nvidia"
-      rel_extra_name:
-        description: 'Release extra name'
-        required: false
-        type: string
-        default: ""
-      test_release:
-        description: 'Test Release'
-        required: true
-        type: boolean
-        default: true
   workflow_dispatch:
     inputs:
       git_tag:
         description: 'Git tag'
         required: true
         type: string
-      cache_tag:
-        description: 'Cached dependencies tag'
+      cu:
+        description: 'CUDA version'
         required: true
         type: string
-        default: "cu129"
+        default: "128"
       python_minor:
         description: 'Python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
       python_patch:
         description: 'Python patch version'
         required: true
         type: string
-        default: "6"
-      rel_name:
-        description: 'Release name'
-        required: true
-        type: string
-        default: "nvidia"
-      rel_extra_name:
-        description: 'Release extra name'
-        required: false
-        type: string
-        default: ""
-      test_release:
-        description: 'Test Release'
-        required: true
-        type: boolean
-        default: true
+        default: "10"
+
 
 jobs:
   package_comfy_windows:
@@ -92,15 +42,15 @@ jobs:
         id: cache
         with:
           path: |
-            ${{ inputs.cache_tag }}_python_deps.tar
+            cu${{ inputs.cu }}_python_deps.tar
             update_comfyui_and_python_dependencies.bat
-          key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
+          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
       - shell: bash
         run: |
-          mv ${{ inputs.cache_tag }}_python_deps.tar ../
+          mv cu${{ inputs.cu }}_python_deps.tar ../
           mv update_comfyui_and_python_dependencies.bat ../
           cd ..
-          tar xf ${{ inputs.cache_tag }}_python_deps.tar
+          tar xf cu${{ inputs.cu }}_python_deps.tar
           pwd
           ls
 
@@ -115,21 +65,9 @@ jobs:
           echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
           curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
           ./python.exe get-pip.py
-          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
-
-          grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
-          ./python.exe -s -m pip install -r requirements_comfyui.txt
-          rm requirements_comfyui.txt
-
-          sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
-
-          if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
-            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
-            rm ./Lib/site-packages/torch/lib/libprotoc.lib
-            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
-          fi
-
-          cd ..
+          ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
+            sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+            cd ..
 
           git clone --depth 1 https://github.com/comfyanonymous/taesd
           cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/
@@ -142,18 +80,14 @@ jobs:
 
           mkdir update
           cp -r ComfyUI/.ci/update_windows/* ./update/
-          cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
+          cp -r ComfyUI/.ci/windows_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
           cd ..
 
-          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
+          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
 
-      - shell: bash
-        if: ${{ inputs.test_release }}
-        run: |
-          cd ..
           cd ComfyUI_windows_portable
           python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
 
@@ -162,9 +96,10 @@ jobs:
           ls
 
       - name: Upload binaries to release
-        uses: softprops/action-gh-release@v2
+        uses: svenstaro/upload-release-action@v2
         with:
-          files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
-          tag_name: ${{ inputs.git_tag }}
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: ComfyUI_windows_portable_nvidia.7z
+          tag: ${{ inputs.git_tag }}
+          overwrite: true
           draft: true
-          overwrite_files: true

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

.github/workflows/test-ci.yml

@@ -5,7 +5,6 @@ on:
   push:
     branches:
       - master
-      - release/**
     paths-ignore:
       - 'app/**'
       - 'input/**'
@@ -22,15 +21,14 @@ jobs:
       fail-fast: false
       matrix:
         # os: [macos, linux, windows]
-        # os: [macos, linux]
-        os: [linux]
-        python_version: ["3.10", "3.11", "3.12"]
+        os: [macos, linux]
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
         cuda_version: ["12.1"]
         torch_version: ["stable"]
         include:
-          # - os: macos
-          #   runner_label: [self-hosted, macOS]
-          #   flags: "--use-pytorch-cross-attention"
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
           - os: linux
             runner_label: [self-hosted, Linux]
             flags: ""
@@ -75,15 +73,14 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # os: [macos, linux]
-        os: [linux]
+        os: [macos, linux]
         python_version: ["3.11"]
         cuda_version: ["12.1"]
         torch_version: ["nightly"]
         include:
-          # - os: macos
-          #   runner_label: [self-hosted, macOS]
-          #   flags: "--use-pytorch-cross-attention"
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
           - os: linux
             runner_label: [self-hosted, Linux]
             flags: ""

.github/workflows/test-execution.yml

@@ -1,30 +0,0 @@
-name: Execution Tests
-
-on:
-  push:
-    branches: [ main, master, release/** ]
-  pull_request:
-    branches: [ main, master, release/** ]
-
-jobs:
-  test:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
-    runs-on: ${{ matrix.os }}
-    continue-on-error: true
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python      
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-        pip install -r tests-unit/requirements.txt
-    - name: Run Execution Tests
-      run: |
-        python -m pytest tests/execution -v --skip-timing-checks

.github/workflows/test-launch.yml

@@ -2,9 +2,9 @@ name: Test server launches without errors
 
 on:
   push:
-    branches: [ main, master, release/** ]
+    branches: [ main, master ]
   pull_request:
-    branches: [ main, master, release/** ]
+    branches: [ main, master ]
 
 jobs:
   test:
@@ -13,7 +13,7 @@ jobs:
     - name: Checkout ComfyUI
       uses: actions/checkout@v4
       with:
-        repository: "Comfy-Org/ComfyUI"
+        repository: "comfyanonymous/ComfyUI"
         path: "ComfyUI"
     - uses: actions/setup-python@v4
       with:
@@ -32,9 +32,7 @@ jobs:
       working-directory: ComfyUI
     - name: Check for unhandled exceptions in server log
       run: |
-        grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" console_output.log | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" > console_output_filtered.log
-        cat console_output_filtered.log
-        if grep -qE "Exception|Error" console_output_filtered.log; then
+        if grep -qE "Exception|Error" console_output.log; then
           echo "Unhandled exception/error found in server log."
           exit 1
         fi

.github/workflows/test-unit.yml

@@ -2,15 +2,15 @@ name: Unit Tests
 
 on:
   push:
-    branches: [ main, master, release/** ]
+    branches: [ main, master ]
   pull_request:
-    branches: [ main, master, release/** ]
+    branches: [ main, master ]
 
 jobs:
   test:
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-2022, macos-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest]
     runs-on: ${{ matrix.os }}
     continue-on-error: true
     steps:

.github/workflows/update-ci-container.yml

@@ -1,59 +0,0 @@
-name: "CI: Update CI Container"
-
-on:
-  release:
-    types: [published]
-  workflow_dispatch:
-    inputs:
-      version:
-        description: 'ComfyUI version (e.g., v0.7.0)'
-        required: true
-        type: string
-
-jobs:
-  update-ci-container:
-    runs-on: ubuntu-latest
-    # Skip pre-releases unless manually triggered
-    if: github.event_name == 'workflow_dispatch' || !github.event.release.prerelease
-    steps:
-      - name: Get version
-        id: version
-        run: |
-          if [ "${{ github.event_name }}" = "release" ]; then
-            VERSION="${{ github.event.release.tag_name }}"
-          else
-            VERSION="${{ inputs.version }}"
-          fi
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-
-      - name: Checkout comfyui-ci-container
-        uses: actions/checkout@v4
-        with:
-          repository: comfy-org/comfyui-ci-container
-          token: ${{ secrets.CI_CONTAINER_PAT }}
-
-      - name: Check current version
-        id: current
-        run: |
-          CURRENT=$(grep -oP 'ARG COMFYUI_VERSION=\K.*' Dockerfile || echo "unknown")
-          echo "current_version=$CURRENT" >> $GITHUB_OUTPUT
-
-      - name: Update Dockerfile
-        run: |
-          VERSION="${{ steps.version.outputs.version }}"
-          sed -i "s/^ARG COMFYUI_VERSION=.*/ARG COMFYUI_VERSION=${VERSION}/" Dockerfile
-
-      - name: Create Pull Request
-        id: create-pr
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.CI_CONTAINER_PAT }}
-          branch: automation/comfyui-${{ steps.version.outputs.version }}
-          title: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}"
-          body: |
-            Updates ComfyUI version from `${{ steps.current.outputs.current_version }}` to `${{ steps.version.outputs.version }}`
-
-            **Triggered by:** ${{ github.event_name == 'release' && format('[Release {0}]({1})', github.event.release.tag_name, github.event.release.html_url) || 'Manual workflow dispatch' }}
-
-          labels: automation
-          commit-message: "chore: bump ComfyUI to ${{ steps.version.outputs.version }}"

.github/workflows/update-version.yml

@@ -6,7 +6,6 @@ on:
       - "pyproject.toml"
     branches:
       - master
-      - release/**
 
 jobs:
   update-version:

.github/workflows/windows_release_dependencies.yml

@@ -17,19 +17,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "130"
+        default: "128"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "11"
+        default: "10"
 #  push:
 #    branches:
 #      - master
@@ -56,8 +56,7 @@ jobs:
             ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > update_comfyui_and_python_dependencies.bat
 
-            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

.github/workflows/windows_release_dependencies_manual.yml

@@ -1,64 +0,0 @@
-name: "Windows Release dependencies Manual"
-
-on:
-  workflow_dispatch:
-    inputs:
-      torch_dependencies:
-        description: 'torch dependencies'
-        required: false
-        type: string
-        default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
-      cache_tag:
-        description: 'Cached dependencies tag'
-        required: true
-        type: string
-        default: "cu128"
-
-      python_minor:
-        description: 'python minor version'
-        required: true
-        type: string
-        default: "12"
-
-      python_patch:
-        description: 'python patch version'
-        required: true
-        type: string
-        default: "10"
-
-jobs:
-  build_dependencies:
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v4
-        - uses: actions/setup-python@v5
-          with:
-            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
-
-        - shell: bash
-          run: |
-            echo "@echo off
-            call update_comfyui.bat nopause
-            echo -
-            echo This will try to update pytorch and all python dependencies.
-            echo -
-            echo If you just want to update normally, close this and run update_comfyui.bat instead.
-            echo -
-            pause
-            ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2
-            pause" > update_comfyui_and_python_dependencies.bat
-
-            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
-            python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
-            tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
-
-        - uses: actions/cache/save@v4
-          with:
-            path: |
-              ${{ inputs.cache_tag }}_python_deps.tar
-              update_comfyui_and_python_dependencies.bat
-            key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -68,7 +68,7 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
+            cp -r ComfyUI/.ci/windows_base_files/* ./
             cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause

.github/workflows/windows_release_package.yml

@@ -7,19 +7,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "129"
+        default: "128"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "10"
 #  push:
 #    branches:
 #      - master
@@ -64,10 +64,6 @@ jobs:
             ./python.exe get-pip.py
             ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
-
-            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
-            rm ./Lib/site-packages/torch/lib/libprotoc.lib
-            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
             cd ..
 
             git clone --depth 1 https://github.com/comfyanonymous/taesd
@@ -81,12 +77,12 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
+            cp -r ComfyUI/.ci/windows_base_files/* ./
             cp ../update_comfyui_and_python_dependencies.bat ./update/
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
             mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
 
             cd ComfyUI_windows_portable

CODEOWNERS

@@ -1,2 +1,24 @@
 # Admins
-* @comfyanonymous @kosinkadink @guill
+* @comfyanonymous
+
+# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
+# Inlined the team members for now.
+
+# Maintainers
+*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+
+# Python web server
+/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+
+# Node developers
+/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

QUANTIZATION.md

@@ -1,168 +0,0 @@
-# The Comfy guide to Quantization
-
-
-## How does quantization work?
-
-Quantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware.
-
-When simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues:
-- The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values
-- The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits "unused"
-
-By using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling.
-
-```
-absmax = max(abs(tensor))
-scale = amax / max_dynamic_range_low_precision
-
-# Quantization
-tensor_q = (tensor / scale).to(low_precision_dtype)
-
-# De-Quantization
-tensor_dq = tensor_q.to(fp16) * scale
-
-tensor_dq ~ tensor
-```
-
-Given that additional information (scaling factor) is needed to "interpret" the quantized values, we describe those as derived datatypes.
-
-
-## Quantization in Comfy
-
-```
-QuantizedTensor (torch.Tensor subclass)
-  ↓ __torch_dispatch__
-Two-Level Registry (generic + layout handlers)
-  ↓
-MixedPrecisionOps + Metadata Detection
-```
-
-### Representation
-
-To represent these derived datatypes, ComfyUI uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `comfy/quant_ops.py`
-
-A `Layout` class defines how a specific quantization format behaves:
-- Required parameters
-- Quantize method
-- De-Quantize method
-
-```python
-from comfy.quant_ops import QuantizedLayout
-
-class MyLayout(QuantizedLayout):
-    @classmethod
-    def quantize(cls, tensor, **kwargs):
-        # Convert to quantized format
-        qdata = ...
-        params = {'scale': ..., 'orig_dtype': tensor.dtype}
-        return qdata, params
-    
-    @staticmethod
-    def dequantize(qdata, scale, orig_dtype, **kwargs):
-        return qdata.to(orig_dtype) * scale
-```
-
-To then run operations using these QuantizedTensors we use two registry systems to define supported operations. 
-The first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`).
-
-The second registry is layout-specific and allows to implement fast-paths like nn.Linear.
-```python
-from comfy.quant_ops import register_layout_op
-
-@register_layout_op(torch.ops.aten.linear.default, MyLayout)
-def my_linear(func, args, kwargs):
-    # Extract tensors, call optimized kernel
-    ...
-```
-When `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation.
-For any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation.
-
-
-### Mixed Precision
-
-The `MixedPrecisionOps` class (lines 542-648 in `comfy/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how.
-
-**Architecture:**
-
-```python
-class MixedPrecisionOps(disable_weight_init):
-    _layer_quant_config = {}  # Maps layer names to quantization configs
-    _compute_dtype = torch.bfloat16  # Default compute / dequantize precision
-```
-
-**Key mechanism:**
-
-The custom `Linear._load_from_state_dict()` method inspects each layer during model loading:
-- If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype`
-- If the layer name **is** in `_layer_quant_config`: 
-  - Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`)
-  - Load associated quantization parameters (scales, block_size, etc.)
-
-**Why it's needed:**
-
-Not all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality.
-
-The system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode.
-
-
-## Checkpoint Format
-
-Quantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme.
-
-The quantized checkpoint will contain the same layers as the original checkpoint but:
-- The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8.
-- For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe.
-- We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used.
-
-### Scaling Parameters details
-We define 4 possible scaling parameters that should cover most recipes in the near-future:
-- **weight_scale**: quantization scalers for the weights
-- **weight_scale_2**: global scalers in the context of double scaling
-- **pre_quant_scale**: scalers used for smoothing salient weights
-- **input_scale**: quantization scalers for the activations
-
-| Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale |
-|--------|---------------|--------------|----------------|-----------------|-------------|
-| float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) |
-
-You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS).
-
-### Quantization Metadata
-
-The metadata stored alongside the checkpoint contains:
-- **format_version**: String to define a version of the standard
-- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. 
-
-Example:
-```json
-{
-  "_quantization_metadata": {
-    "format_version": "1.0",
-    "layers": {
-      "model.layers.0.mlp.up_proj": "float8_e4m3fn",
-      "model.layers.0.mlp.down_proj": "float8_e4m3fn",
-      "model.layers.1.mlp.up_proj": "float8_e4m3fn"
-    }
-  }
-}
-```
-
-
-## Creating Quantized Checkpoints
-
-To create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`.
-
-### Weight Quantization
-
-Weight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter.
-
-### Calibration (for Activation Quantization)
-
-Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**:
-
-1. **Collect statistics**: Run inference on N representative samples
-2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer
-3. **Compute scales**: Derive `input_scale` from collected statistics
-4. **Store in checkpoint**: Save `input_scale` parameters alongside weights
-
-The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters.

README.md

@@ -39,7 +39,7 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 ## Get Started
 
 #### [Desktop Application](https://www.comfy.org/download)
-- The easiest way to get started.
+- The easiest way to get started. 
 - Available on Windows & macOS.
 
 #### [Windows Portable Package](#installing)
@@ -65,23 +65,19 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
    - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
    - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
-   - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
-   - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
-   - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
-   - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
+   - [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
    - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
-   - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)
 - Video Models
    - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
    - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
    - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/) and [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
    - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
-   - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -108,21 +104,17 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Works fully offline: core will never download anything unless you want to.
-- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview) disable with: `--disable-api-nodes`
+- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
 - [Config file](extra_model_paths.yaml.example) to set the search paths for models.
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
 
 ## Release Process
 
-ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
+ComfyUI follows a weekly release cycle every Friday, with three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
-   - Releases a new stable version (e.g., v0.7.0) roughly every week.
-   - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
-   - Minor versions will be used for releases off the master branch.
-   - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
-   - Commits outside of the stable release tags may be very unstable and break many custom nodes.
+   - Releases a new stable version (e.g., v0.7.0)
    - Serves as the foundation for the desktop release
 
 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
@@ -179,20 +171,10 @@ There is a portable standalone build for Windows that should work for running on
 
 ### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
 
-Simply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\
+Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
-The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
-
-#### Alternative Downloads:
-
-[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
-
-[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
-
-[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
-
 #### How do I share models between another UI and ComfyUI?
 
 See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
@@ -208,13 +190,7 @@ comfy install
 
 ## Manual Install (Windows, Linux)
 
-Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.
-
-Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
-
-torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
-
-### Instructions:
+python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
 
 Git clone this repo.
 
@@ -223,54 +199,48 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 Put your VAE in: models/vae
 
 
-### AMD GPUs (Linux)
-
+### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3```
 
-This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
-
-
-### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
-
-These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
-
-RDNA 3 (RX 7000 series):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/```
-
-RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
-
-RDNA 4 (RX 9000 series):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
 
 ### Intel GPUs (Windows and Linux)
 
-Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
-
-1. To install PyTorch xpu, use the following command:
-
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
-
-This is the command to install the Pytorch xpu nightly which might have some performance improvements:
+(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
+  
+1. To install PyTorch nightly, use the following command:
 
 ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
 
+2. Launch ComfyUI by running `python main.py`
+
+
+(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
+
+1. For Intel® Arc™ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below:
+
+```
+conda install libuv
+pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
+```
+
+For other supported Intel GPUs with IPEX, visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
+
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128```
 
 This is the command to install pytorch nightly instead which might have performance improvements.
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129```
 
 #### Troubleshooting
 
@@ -301,6 +271,12 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
 
+#### DirectML (AMD Cards on Windows)
+
+This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
+
+```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
+
 #### Ascend NPUs
 
 For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
@@ -325,32 +301,6 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step
 1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
 2. Launch ComfyUI by running `python main.py`
 
-
-## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)
-
-**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
-
-### Setup
-
-1. Install the manager dependencies:
-   ```bash
-   pip install -r manager_requirements.txt
-   ```
-
-2. Enable the manager with the `--enable-manager` flag when running ComfyUI:
-   ```bash
-   python main.py --enable-manager
-   ```
-
-### Command Line Options
-
-| Flag | Description |
-|------|-------------|
-| `--enable-manager` | Enable ComfyUI-Manager |
-| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
-| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
-
-
 # Running
 
 ```python main.py```
@@ -401,7 +351,7 @@ Generate a self-signed certificate (not appropriate for shared/production use) a
 
 Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
 
-> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
+> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. 
 <br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
 
 ## Support and dev channel

alembic_db/versions/0001_assets.py

@@ -1,174 +0,0 @@
-"""
-Initial assets schema
-Revision ID: 0001_assets
-Revises: None
-Create Date: 2025-12-10 00:00:00
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-revision = "0001_assets"
-down_revision = None
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # ASSETS: content identity
-    op.create_table(
-        "assets",
-        sa.Column("id", sa.String(length=36), primary_key=True),
-        sa.Column("hash", sa.String(length=256), nullable=True),
-        sa.Column("size_bytes", sa.BigInteger(), nullable=False, server_default="0"),
-        sa.Column("mime_type", sa.String(length=255), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
-        sa.CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
-    )
-    op.create_index("uq_assets_hash", "assets", ["hash"], unique=True)
-    op.create_index("ix_assets_mime_type", "assets", ["mime_type"])
-
-    # ASSETS_INFO: user-visible references
-    op.create_table(
-        "assets_info",
-        sa.Column("id", sa.String(length=36), primary_key=True),
-        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
-        sa.Column("name", sa.String(length=512), nullable=False),
-        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
-        sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
-        sa.Column("user_metadata", sa.JSON(), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
-        sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
-    )
-    op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
-    op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
-    op.create_index("ix_assets_info_name", "assets_info", ["name"])
-    op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
-    op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
-    op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
-
-    # TAGS: normalized tag vocabulary
-    op.create_table(
-        "tags",
-        sa.Column("name", sa.String(length=512), primary_key=True),
-        sa.Column("tag_type", sa.String(length=32), nullable=False, server_default="user"),
-        sa.CheckConstraint("name = lower(name)", name="ck_tags_lowercase"),
-    )
-    op.create_index("ix_tags_tag_type", "tags", ["tag_type"])
-
-    # ASSET_INFO_TAGS: many-to-many for tags on AssetInfo
-    op.create_table(
-        "asset_info_tags",
-        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
-        sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
-        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
-        sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
-    )
-    op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
-    op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
-
-    # ASSET_CACHE_STATE: N:1 local cache rows per Asset
-    op.create_table(
-        "asset_cache_state",
-        sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
-        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("file_path", sa.Text(), nullable=False),  # absolute local path to cached file
-        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
-        sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
-        sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
-        sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
-    )
-    op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
-    op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
-
-    # ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting
-    op.create_table(
-        "asset_info_meta",
-        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("key", sa.String(length=256), nullable=False),
-        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
-        sa.Column("val_str", sa.String(length=2048), nullable=True),
-        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
-        sa.Column("val_bool", sa.Boolean(), nullable=True),
-        sa.Column("val_json", sa.JSON(), nullable=True),
-        sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
-    )
-    op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
-    op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
-    op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
-    op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])
-
-    # Tags vocabulary
-    tags_table = sa.table(
-        "tags",
-        sa.column("name", sa.String(length=512)),
-        sa.column("tag_type", sa.String()),
-    )
-    op.bulk_insert(
-        tags_table,
-        [
-            {"name": "models", "tag_type": "system"},
-            {"name": "input", "tag_type": "system"},
-            {"name": "output", "tag_type": "system"},
-
-            {"name": "configs", "tag_type": "system"},
-            {"name": "checkpoints", "tag_type": "system"},
-            {"name": "loras", "tag_type": "system"},
-            {"name": "vae", "tag_type": "system"},
-            {"name": "text_encoders", "tag_type": "system"},
-            {"name": "diffusion_models", "tag_type": "system"},
-            {"name": "clip_vision", "tag_type": "system"},
-            {"name": "style_models", "tag_type": "system"},
-            {"name": "embeddings", "tag_type": "system"},
-            {"name": "diffusers", "tag_type": "system"},
-            {"name": "vae_approx", "tag_type": "system"},
-            {"name": "controlnet", "tag_type": "system"},
-            {"name": "gligen", "tag_type": "system"},
-            {"name": "upscale_models", "tag_type": "system"},
-            {"name": "hypernetworks", "tag_type": "system"},
-            {"name": "photomaker", "tag_type": "system"},
-            {"name": "classifiers", "tag_type": "system"},
-
-            {"name": "encoder", "tag_type": "system"},
-            {"name": "decoder", "tag_type": "system"},
-
-            {"name": "missing", "tag_type": "system"},
-            {"name": "rescan", "tag_type": "system"},
-        ],
-    )
-
-
-def downgrade() -> None:
-    op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
-    op.drop_table("asset_info_meta")
-
-    op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
-    op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
-    op.drop_constraint("uq_asset_cache_state_file_path", table_name="asset_cache_state")
-    op.drop_table("asset_cache_state")
-
-    op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
-    op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
-    op.drop_table("asset_info_tags")
-
-    op.drop_index("ix_tags_tag_type", table_name="tags")
-    op.drop_table("tags")
-
-    op.drop_constraint("uq_assets_info_asset_owner_name", table_name="assets_info")
-    op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
-    op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
-    op.drop_index("ix_assets_info_created_at", table_name="assets_info")
-    op.drop_index("ix_assets_info_name", table_name="assets_info")
-    op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
-    op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
-    op.drop_table("assets_info")
-
-    op.drop_index("uq_assets_hash", table_name="assets")
-    op.drop_index("ix_assets_mime_type", table_name="assets")
-    op.drop_table("assets")

api_server/routes/internal/internal_routes.py

@@ -58,13 +58,8 @@ class InternalRoutes:
                 return web.json_response({"error": "Invalid directory type"}, status=400)
 
             directory = get_directory_by_type(directory_type)
-
-            def is_visible_file(entry: os.DirEntry) -> bool:
-                """Filter out hidden files (e.g., .DS_Store on macOS)."""
-                return entry.is_file() and not entry.name.startswith('.')
-
             sorted_files = sorted(
-                (entry for entry in os.scandir(directory) if is_visible_file(entry)),
+                (entry for entry in os.scandir(directory) if entry.is_file()),
                 key=lambda entry: -entry.stat().st_mtime
             )
             return web.json_response([entry.name for entry in sorted_files], status=200)

app/assets/api/routes.py

@@ -1,514 +0,0 @@
-import logging
-import uuid
-import urllib.parse
-import os
-import contextlib
-from aiohttp import web
-
-from pydantic import ValidationError
-
-import app.assets.manager as manager
-from app import user_manager
-from app.assets.api import schemas_in
-from app.assets.helpers import get_query_dict
-from app.assets.scanner import seed_assets
-
-import folder_paths
-
-ROUTES = web.RouteTableDef()
-USER_MANAGER: user_manager.UserManager | None = None
-
-# UUID regex (canonical hyphenated form, case-insensitive)
-UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
-
-# Note to any custom node developers reading this code:
-# The assets system is not yet fully implemented, do not rely on the code in /app/assets remaining the same.
-
-def register_assets_system(app: web.Application, user_manager_instance: user_manager.UserManager) -> None:
-    global USER_MANAGER
-    USER_MANAGER = user_manager_instance
-    app.add_routes(ROUTES)
-
-def _error_response(status: int, code: str, message: str, details: dict | None = None) -> web.Response:
-    return web.json_response({"error": {"code": code, "message": message, "details": details or {}}}, status=status)
-
-
-def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
-    return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
-
-
-@ROUTES.head("/api/assets/hash/{hash}")
-async def head_asset_by_hash(request: web.Request) -> web.Response:
-    hash_str = request.match_info.get("hash", "").strip().lower()
-    if not hash_str or ":" not in hash_str:
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    algo, digest = hash_str.split(":", 1)
-    if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    exists = manager.asset_exists(asset_hash=hash_str)
-    return web.Response(status=200 if exists else 404)
-
-
-@ROUTES.get("/api/assets")
-async def list_assets(request: web.Request) -> web.Response:
-    """
-    GET request to list assets.
-    """
-    query_dict = get_query_dict(request)
-    try:
-        q = schemas_in.ListAssetsQuery.model_validate(query_dict)
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_QUERY", ve)
-
-    payload = manager.list_assets(
-        include_tags=q.include_tags,
-        exclude_tags=q.exclude_tags,
-        name_contains=q.name_contains,
-        metadata_filter=q.metadata_filter,
-        limit=q.limit,
-        offset=q.offset,
-        sort=q.sort,
-        order=q.order,
-        owner_id=USER_MANAGER.get_request_user_id(request),
-    )
-    return web.json_response(payload.model_dump(mode="json", exclude_none=True))
-
-
-@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}")
-async def get_asset(request: web.Request) -> web.Response:
-    """
-    GET request to get an asset's info as JSON.
-    """
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        result = manager.get_asset(
-            asset_info_id=asset_info_id,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except ValueError as e:
-        return _error_response(404, "ASSET_NOT_FOUND", str(e), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "get_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content")
-async def download_asset_content(request: web.Request) -> web.Response:
-    # question: do we need disposition? could we just stick with one of these?
-    disposition = request.query.get("disposition", "attachment").lower().strip()
-    if disposition not in {"inline", "attachment"}:
-        disposition = "attachment"
-
-    try:
-        abs_path, content_type, filename = manager.resolve_asset_content_for_download(
-            asset_info_id=str(uuid.UUID(request.match_info["id"])),
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve))
-    except NotImplementedError as nie:
-        return _error_response(501, "BACKEND_UNSUPPORTED", str(nie))
-    except FileNotFoundError:
-        return _error_response(404, "FILE_NOT_FOUND", "Underlying file not found on disk.")
-
-    quoted = (filename or "").replace("\r", "").replace("\n", "").replace('"', "'")
-    cd = f'{disposition}; filename="{quoted}"; filename*=UTF-8\'\'{urllib.parse.quote(filename)}'
-
-    file_size = os.path.getsize(abs_path)
-    logging.info(
-        "download_asset_content: path=%s, size=%d bytes (%.2f MB), content_type=%s, filename=%s",
-        abs_path,
-        file_size,
-        file_size / (1024 * 1024),
-        content_type,
-        filename,
-    )
-
-    async def file_sender():
-        chunk_size = 64 * 1024
-        with open(abs_path, "rb") as f:
-            while True:
-                chunk = f.read(chunk_size)
-                if not chunk:
-                    break
-                yield chunk
-
-    return web.Response(
-        body=file_sender(),
-        content_type=content_type,
-        headers={
-            "Content-Disposition": cd,
-            "Content-Length": str(file_size),
-        },
-    )
-
-
-@ROUTES.post("/api/assets/from-hash")
-async def create_asset_from_hash(request: web.Request) -> web.Response:
-    try:
-        payload = await request.json()
-        body = schemas_in.CreateFromHashBody.model_validate(payload)
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    result = manager.create_asset_from_hash(
-        hash_str=body.hash,
-        name=body.name,
-        tags=body.tags,
-        user_metadata=body.user_metadata,
-        owner_id=USER_MANAGER.get_request_user_id(request),
-    )
-    if result is None:
-        return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist")
-    return web.json_response(result.model_dump(mode="json"), status=201)
-
-
-@ROUTES.post("/api/assets")
-async def upload_asset(request: web.Request) -> web.Response:
-    """Multipart/form-data endpoint for Asset uploads."""
-    if not (request.content_type or "").lower().startswith("multipart/"):
-        return _error_response(415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads.")
-
-    reader = await request.multipart()
-
-    file_present = False
-    file_client_name: str | None = None
-    tags_raw: list[str] = []
-    provided_name: str | None = None
-    user_metadata_raw: str | None = None
-    provided_hash: str | None = None
-    provided_hash_exists: bool | None = None
-
-    file_written = 0
-    tmp_path: str | None = None
-    while True:
-        field = await reader.next()
-        if field is None:
-            break
-
-        fname = getattr(field, "name", "") or ""
-
-        if fname == "hash":
-            try:
-                s = ((await field.text()) or "").strip().lower()
-            except Exception:
-                return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-
-            if s:
-                if ":" not in s:
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                algo, digest = s.split(":", 1)
-                if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                provided_hash = f"{algo}:{digest}"
-                try:
-                    provided_hash_exists = manager.asset_exists(asset_hash=provided_hash)
-                except Exception:
-                    provided_hash_exists = None  # do not fail the whole request here
-
-        elif fname == "file":
-            file_present = True
-            file_client_name = (field.filename or "").strip()
-
-            if provided_hash and provided_hash_exists is True:
-                # If client supplied a hash that we know exists, drain but do not write to disk
-                try:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        file_written += len(chunk)
-                except Exception:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file.")
-                continue  # Do not create temp file; we will create AssetInfo from the existing content
-
-            # Otherwise, store to temp for hashing/ingest
-            uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
-            unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
-            os.makedirs(unique_dir, exist_ok=True)
-            tmp_path = os.path.join(unique_dir, ".upload.part")
-
-            try:
-                with open(tmp_path, "wb") as f:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        f.write(chunk)
-                        file_written += len(chunk)
-            except Exception:
-                try:
-                    if os.path.exists(tmp_path or ""):
-                        os.remove(tmp_path)
-                finally:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file.")
-        elif fname == "tags":
-            tags_raw.append((await field.text()) or "")
-        elif fname == "name":
-            provided_name = (await field.text()) or None
-        elif fname == "user_metadata":
-            user_metadata_raw = (await field.text()) or None
-
-    # If client did not send file, and we are not doing a from-hash fast path -> error
-    if not file_present and not (provided_hash and provided_hash_exists):
-        return _error_response(400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'.")
-
-    if file_present and file_written == 0 and not (provided_hash and provided_hash_exists):
-        # Empty upload is only acceptable if we are fast-pathing from existing hash
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _error_response(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
-
-    try:
-        spec = schemas_in.UploadAssetSpec.model_validate({
-            "tags": tags_raw,
-            "name": provided_name,
-            "user_metadata": user_metadata_raw,
-            "hash": provided_hash,
-        })
-    except ValidationError as ve:
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _validation_error_response("INVALID_BODY", ve)
-
-    # Validate models category against configured folders (consistent with previous behavior)
-    if spec.tags and spec.tags[0] == "models":
-        if len(spec.tags) < 2 or spec.tags[1] not in folder_paths.folder_names_and_paths:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-            return _error_response(
-                400, "INVALID_BODY", f"unknown models category '{spec.tags[1] if len(spec.tags) >= 2 else ''}'"
-            )
-
-    owner_id = USER_MANAGER.get_request_user_id(request)
-
-    # Fast path: if a valid provided hash exists, create AssetInfo without writing anything
-    if spec.hash and provided_hash_exists is True:
-        try:
-            result = manager.create_asset_from_hash(
-                hash_str=spec.hash,
-                name=spec.name or (spec.hash.split(":", 1)[1]),
-                tags=spec.tags,
-                user_metadata=spec.user_metadata or {},
-                owner_id=owner_id,
-            )
-        except Exception:
-            logging.exception("create_asset_from_hash failed for hash=%s, owner_id=%s", spec.hash, owner_id)
-            return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-        if result is None:
-            return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist")
-
-        # Drain temp if we accidentally saved (e.g., hash field came after file)
-        if tmp_path and os.path.exists(tmp_path):
-            with contextlib.suppress(Exception):
-                os.remove(tmp_path)
-
-        status = 200 if (not result.created_new) else 201
-        return web.json_response(result.model_dump(mode="json"), status=status)
-
-    # Otherwise, we must have a temp file path to ingest
-    if not tmp_path or not os.path.exists(tmp_path):
-        # The only case we reach here without a temp file is: client sent a hash that does not exist and no file
-        return _error_response(404, "ASSET_NOT_FOUND", "Provided hash not found and no file uploaded.")
-
-    try:
-        created = manager.upload_asset_from_temp_path(
-            spec,
-            temp_path=tmp_path,
-            client_filename=file_client_name,
-            owner_id=owner_id,
-            expected_asset_hash=spec.hash,
-        )
-        status = 201 if created.created_new else 200
-        return web.json_response(created.model_dump(mode="json"), status=status)
-    except ValueError as e:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        msg = str(e)
-        if "HASH_MISMATCH" in msg or msg.strip().upper() == "HASH_MISMATCH":
-            return _error_response(
-                400,
-                "HASH_MISMATCH",
-                "Uploaded file hash does not match provided hash.",
-            )
-        return _error_response(400, "BAD_REQUEST", "Invalid inputs.")
-    except Exception:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        logging.exception("upload_asset_from_temp_path failed for tmp_path=%s, owner_id=%s", tmp_path, owner_id)
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-
-@ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}")
-async def update_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        body = schemas_in.UpdateAssetBody.model_validate(await request.json())
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.update_asset(
-            asset_info_id=asset_info_id,
-            name=body.name,
-            user_metadata=body.user_metadata,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "update_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}")
-async def delete_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    delete_content = request.query.get("delete_content")
-    delete_content = True if delete_content is None else delete_content.lower() not in {"0", "false", "no"}
-
-    try:
-        deleted = manager.delete_asset_reference(
-            asset_info_id=asset_info_id,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-            delete_content_if_orphan=delete_content,
-        )
-    except Exception:
-        logging.exception(
-            "delete_asset_reference failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    if not deleted:
-        return _error_response(404, "ASSET_NOT_FOUND", f"AssetInfo {asset_info_id} not found.")
-    return web.Response(status=204)
-
-
-@ROUTES.get("/api/tags")
-async def get_tags(request: web.Request) -> web.Response:
-    """
-    GET request to list all tags based on query parameters.
-    """
-    query_map = dict(request.rel_url.query)
-
-    try:
-        query = schemas_in.TagsListQuery.model_validate(query_map)
-    except ValidationError as e:
-        return web.json_response(
-            {"error": {"code": "INVALID_QUERY", "message": "Invalid query parameters", "details": e.errors()}},
-            status=400,
-        )
-
-    result = manager.list_tags(
-        prefix=query.prefix,
-        limit=query.limit,
-        offset=query.offset,
-        order=query.order,
-        include_zero=query.include_zero,
-        owner_id=USER_MANAGER.get_request_user_id(request),
-    )
-    return web.json_response(result.model_dump(mode="json"))
-
-
-@ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags")
-async def add_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        payload = await request.json()
-        data = schemas_in.TagsAdd.model_validate(payload)
-    except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags add.", {"errors": ve.errors()})
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.add_tags_to_asset(
-            asset_info_id=asset_info_id,
-            tags=data.tags,
-            origin="manual",
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "add_tags_to_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags")
-async def delete_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        payload = await request.json()
-        data = schemas_in.TagsRemove.model_validate(payload)
-    except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags remove.", {"errors": ve.errors()})
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.remove_tags_from_asset(
-            asset_info_id=asset_info_id,
-            tags=data.tags,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "remove_tags_from_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.post("/api/assets/seed")
-async def seed_assets_endpoint(request: web.Request) -> web.Response:
-    """Trigger asset seeding for specified roots (models, input, output)."""
-    try:
-        payload = await request.json()
-        roots = payload.get("roots", ["models", "input", "output"])
-    except Exception:
-        roots = ["models", "input", "output"]
-
-    valid_roots = [r for r in roots if r in ("models", "input", "output")]
-    if not valid_roots:
-        return _error_response(400, "INVALID_BODY", "No valid roots specified")
-
-    try:
-        seed_assets(tuple(valid_roots))
-    except Exception:
-        logging.exception("seed_assets failed for roots=%s", valid_roots)
-        return _error_response(500, "INTERNAL", "Seed operation failed")
-
-    return web.json_response({"seeded": valid_roots}, status=200)

app/assets/api/schemas_in.py

@@ -1,264 +0,0 @@
-import json
-from typing import Any, Literal
-
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    conint,
-    field_validator,
-    model_validator,
-)
-
-class ListAssetsQuery(BaseModel):
-    include_tags: list[str] = Field(default_factory=list)
-    exclude_tags: list[str] = Field(default_factory=list)
-    name_contains: str | None = None
-
-    # Accept either a JSON string (query param) or a dict
-    metadata_filter: dict[str, Any] | None = None
-
-    limit: conint(ge=1, le=500) = 20
-    offset: conint(ge=0) = 0
-
-    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
-    order: Literal["asc", "desc"] = "desc"
-
-    @field_validator("include_tags", "exclude_tags", mode="before")
-    @classmethod
-    def _split_csv_tags(cls, v):
-        # Accept "a,b,c" or ["a","b"] (we are liberal in what we accept)
-        if v is None:
-            return []
-        if isinstance(v, str):
-            return [t.strip() for t in v.split(",") if t.strip()]
-        if isinstance(v, list):
-            out: list[str] = []
-            for item in v:
-                if isinstance(item, str):
-                    out.extend([t.strip() for t in item.split(",") if t.strip()])
-            return out
-        return v
-
-    @field_validator("metadata_filter", mode="before")
-    @classmethod
-    def _parse_metadata_json(cls, v):
-        if v is None or isinstance(v, dict):
-            return v
-        if isinstance(v, str) and v.strip():
-            try:
-                parsed = json.loads(v)
-            except Exception as e:
-                raise ValueError(f"metadata_filter must be JSON: {e}") from e
-            if not isinstance(parsed, dict):
-                raise ValueError("metadata_filter must be a JSON object")
-            return parsed
-        return None
-
-
-class UpdateAssetBody(BaseModel):
-    name: str | None = None
-    user_metadata: dict[str, Any] | None = None
-
-    @model_validator(mode="after")
-    def _at_least_one(self):
-        if self.name is None and self.user_metadata is None:
-            raise ValueError("Provide at least one of: name, user_metadata.")
-        return self
-
-
-class CreateFromHashBody(BaseModel):
-    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
-
-    hash: str
-    name: str
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-
-    @field_validator("hash")
-    @classmethod
-    def _require_blake3(cls, v):
-        s = (v or "").strip().lower()
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return s
-
-    @field_validator("tags", mode="before")
-    @classmethod
-    def _tags_norm(cls, v):
-        if v is None:
-            return []
-        if isinstance(v, list):
-            out = [str(t).strip().lower() for t in v if str(t).strip()]
-            seen = set()
-            dedup = []
-            for t in out:
-                if t not in seen:
-                    seen.add(t)
-                    dedup.append(t)
-            return dedup
-        if isinstance(v, str):
-            return [t.strip().lower() for t in v.split(",") if t.strip()]
-        return []
-
-
-class TagsListQuery(BaseModel):
-    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
-
-    prefix: str | None = Field(None, min_length=1, max_length=256)
-    limit: int = Field(100, ge=1, le=1000)
-    offset: int = Field(0, ge=0, le=10_000_000)
-    order: Literal["count_desc", "name_asc"] = "count_desc"
-    include_zero: bool = True
-
-    @field_validator("prefix")
-    @classmethod
-    def normalize_prefix(cls, v: str | None) -> str | None:
-        if v is None:
-            return v
-        v = v.strip()
-        return v.lower() or None
-
-
-class TagsAdd(BaseModel):
-    model_config = ConfigDict(extra="ignore")
-    tags: list[str] = Field(..., min_length=1)
-
-    @field_validator("tags")
-    @classmethod
-    def normalize_tags(cls, v: list[str]) -> list[str]:
-        out = []
-        for t in v:
-            if not isinstance(t, str):
-                raise TypeError("tags must be strings")
-            tnorm = t.strip().lower()
-            if tnorm:
-                out.append(tnorm)
-        seen = set()
-        deduplicated = []
-        for x in out:
-            if x not in seen:
-                seen.add(x)
-                deduplicated.append(x)
-        return deduplicated
-
-
-class TagsRemove(TagsAdd):
-    pass
-
-
-class UploadAssetSpec(BaseModel):
-    """Upload Asset operation.
-    - tags: ordered; first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category from folder_paths.folder_names_and_paths
-    - name: display name
-    - user_metadata: arbitrary JSON object (optional)
-    - hash: optional canonical 'blake3:<hex>' provided by the client for validation / fast-path
-
-    Files created via this endpoint are stored on disk using the **content hash** as the filename stem
-    and the original extension is preserved when available.
-    """
-    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
-
-    tags: list[str] = Field(..., min_length=1)
-    name: str | None = Field(default=None, max_length=512, description="Display Name")
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    hash: str | None = Field(default=None)
-
-    @field_validator("hash", mode="before")
-    @classmethod
-    def _parse_hash(cls, v):
-        if v is None:
-            return None
-        s = str(v).strip().lower()
-        if not s:
-            return None
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return f"{algo}:{digest}"
-
-    @field_validator("tags", mode="before")
-    @classmethod
-    def _parse_tags(cls, v):
-        """
-        Accepts a list of strings (possibly multiple form fields),
-        where each string can be:
-          - JSON array (e.g., '["models","loras","foo"]')
-          - comma-separated ('models, loras, foo')
-          - single token ('models')
-        Returns a normalized, deduplicated, ordered list.
-        """
-        items: list[str] = []
-        if v is None:
-            return []
-        if isinstance(v, str):
-            v = [v]
-
-        if isinstance(v, list):
-            for item in v:
-                if item is None:
-                    continue
-                s = str(item).strip()
-                if not s:
-                    continue
-                if s.startswith("["):
-                    try:
-                        arr = json.loads(s)
-                        if isinstance(arr, list):
-                            items.extend(str(x) for x in arr)
-                            continue
-                    except Exception:
-                        pass  # fallback to CSV parse below
-                items.extend([p for p in s.split(",") if p.strip()])
-        else:
-            return []
-
-        # normalize + dedupe
-        norm = []
-        seen = set()
-        for t in items:
-            tnorm = str(t).strip().lower()
-            if tnorm and tnorm not in seen:
-                seen.add(tnorm)
-                norm.append(tnorm)
-        return norm
-
-    @field_validator("user_metadata", mode="before")
-    @classmethod
-    def _parse_metadata_json(cls, v):
-        if v is None or isinstance(v, dict):
-            return v or {}
-        if isinstance(v, str):
-            s = v.strip()
-            if not s:
-                return {}
-            try:
-                parsed = json.loads(s)
-            except Exception as e:
-                raise ValueError(f"user_metadata must be JSON: {e}") from e
-            if not isinstance(parsed, dict):
-                raise ValueError("user_metadata must be a JSON object")
-            return parsed
-        return {}
-
-    @model_validator(mode="after")
-    def _validate_order(self):
-        if not self.tags:
-            raise ValueError("tags must be provided and non-empty")
-        root = self.tags[0]
-        if root not in {"models", "input", "output"}:
-            raise ValueError("first tag must be one of: models, input, output")
-        if root == "models":
-            if len(self.tags) < 2:
-                raise ValueError("models uploads require a category tag as the second tag")
-        return self

app/assets/api/schemas_out.py

@@ -1,93 +0,0 @@
-from datetime import datetime
-from typing import Any
-
-from pydantic import BaseModel, ConfigDict, Field, field_serializer
-
-
-class AssetSummary(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    size: int | None = None
-    mime_type: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    preview_url: str | None = None
-    created_at: datetime | None = None
-    updated_at: datetime | None = None
-    last_access_time: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("created_at", "updated_at", "last_access_time")
-    def _ser_dt(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
-class AssetsList(BaseModel):
-    assets: list[AssetSummary]
-    total: int
-    has_more: bool
-
-
-class AssetUpdated(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    updated_at: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("updated_at")
-    def _ser_updated(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
-class AssetDetail(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    size: int | None = None
-    mime_type: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    preview_id: str | None = None
-    created_at: datetime | None = None
-    last_access_time: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("created_at", "last_access_time")
-    def _ser_dt(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
-class AssetCreated(AssetDetail):
-    created_new: bool
-
-
-class TagUsage(BaseModel):
-    name: str
-    count: int
-    type: str
-
-
-class TagsList(BaseModel):
-    tags: list[TagUsage] = Field(default_factory=list)
-    total: int
-    has_more: bool
-
-
-class TagsAdd(BaseModel):
-    model_config = ConfigDict(str_strip_whitespace=True)
-    added: list[str] = Field(default_factory=list)
-    already_present: list[str] = Field(default_factory=list)
-    total_tags: list[str] = Field(default_factory=list)
-
-
-class TagsRemove(BaseModel):
-    model_config = ConfigDict(str_strip_whitespace=True)
-    removed: list[str] = Field(default_factory=list)
-    not_present: list[str] = Field(default_factory=list)
-    total_tags: list[str] = Field(default_factory=list)

app/assets/database/bulk_ops.py

@@ -1,204 +0,0 @@
-import os
-import uuid
-import sqlalchemy
-from typing import Iterable
-from sqlalchemy.orm import Session
-from sqlalchemy.dialects import sqlite
-
-from app.assets.helpers import utcnow
-from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
-
-MAX_BIND_PARAMS = 800
-
-def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
-    if not rows:
-        return []
-    rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
-    for i in range(0, len(rows), rows_per_stmt):
-        yield rows[i:i + rows_per_stmt]
-
-def _iter_chunks(seq, n: int):
-    for i in range(0, len(seq), n):
-        yield seq[i:i + n]
-
-def _rows_per_stmt(cols: int) -> int:
-    return max(1, MAX_BIND_PARAMS // max(1, cols))
-
-
-def seed_from_paths_batch(
-    session: Session,
-    *,
-    specs: list[dict],
-    owner_id: str = "",
-) -> dict:
-    """Each spec is a dict with keys:
-      - abs_path: str
-      - size_bytes: int
-      - mtime_ns: int
-      - info_name: str
-      - tags: list[str]
-      - fname: Optional[str]
-    """
-    if not specs:
-        return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
-
-    now = utcnow()
-    asset_rows: list[dict] = []
-    state_rows: list[dict] = []
-    path_to_asset: dict[str, str] = {}
-    asset_to_info: dict[str, dict] = {}  # asset_id -> prepared info row
-    path_list: list[str] = []
-
-    for sp in specs:
-        ap = os.path.abspath(sp["abs_path"])
-        aid = str(uuid.uuid4())
-        iid = str(uuid.uuid4())
-        path_list.append(ap)
-        path_to_asset[ap] = aid
-
-        asset_rows.append(
-            {
-                "id": aid,
-                "hash": None,
-                "size_bytes": sp["size_bytes"],
-                "mime_type": None,
-                "created_at": now,
-            }
-        )
-        state_rows.append(
-            {
-                "asset_id": aid,
-                "file_path": ap,
-                "mtime_ns": sp["mtime_ns"],
-            }
-        )
-        asset_to_info[aid] = {
-            "id": iid,
-            "owner_id": owner_id,
-            "name": sp["info_name"],
-            "asset_id": aid,
-            "preview_id": None,
-            "user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
-            "created_at": now,
-            "updated_at": now,
-            "last_access_time": now,
-            "_tags": sp["tags"],
-            "_filename": sp["fname"],
-        }
-
-    # insert all seed Assets (hash=NULL)
-    ins_asset = sqlite.insert(Asset)
-    for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
-        session.execute(ins_asset, chunk)
-
-    # try to claim AssetCacheState (file_path)
-    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
-    ins_state = (
-        sqlite.insert(AssetCacheState)
-        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-    )
-    for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
-        session.execute(ins_state, chunk)
-
-    # Query to find which of our paths won (were actually inserted)
-    winners_by_path: set[str] = set()
-    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetCacheState.file_path)
-            .where(AssetCacheState.file_path.in_(chunk))
-            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
-        )
-        winners_by_path.update(result.scalars().all())
-
-    all_paths_set = set(path_list)
-    losers_by_path = all_paths_set - winners_by_path
-    lost_assets = [path_to_asset[p] for p in losers_by_path]
-    if lost_assets:  # losers get their Asset removed
-        for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
-            session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
-
-    if not winners_by_path:
-        return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
-
-    # insert AssetInfo only for winners
-    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
-    winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
-    ins_info = (
-        sqlite.insert(AssetInfo)
-        .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
-    )
-    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
-        session.execute(ins_info, chunk)
-
-    # Query to find which info rows were actually inserted (by matching our generated IDs)
-    all_info_ids = [row["id"] for row in winner_info_rows]
-    inserted_info_ids: set[str] = set()
-    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
-        )
-        inserted_info_ids.update(result.scalars().all())
-
-    # build and insert tag + meta rows for the AssetInfo
-    tag_rows: list[dict] = []
-    meta_rows: list[dict] = []
-    if inserted_info_ids:
-        for row in winner_info_rows:
-            iid = row["id"]
-            if iid not in inserted_info_ids:
-                continue
-            for t in row["_tags"]:
-                tag_rows.append({
-                    "asset_info_id": iid,
-                    "tag_name": t,
-                    "origin": "automatic",
-                    "added_at": now,
-                })
-            if row["_filename"]:
-                meta_rows.append(
-                    {
-                        "asset_info_id": iid,
-                        "key": "filename",
-                        "ordinal": 0,
-                        "val_str": row["_filename"],
-                        "val_num": None,
-                        "val_bool": None,
-                        "val_json": None,
-                    }
-                )
-
-    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
-    return {
-        "inserted_infos": len(inserted_info_ids),
-        "won_states": len(winners_by_path),
-        "lost_states": len(losers_by_path),
-    }
-
-
-def bulk_insert_tags_and_meta(
-    session: Session,
-    *,
-    tag_rows: list[dict],
-    meta_rows: list[dict],
-    max_bind_params: int,
-) -> None:
-    """Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
-    - tag_rows keys: asset_info_id, tag_name, origin, added_at
-    - meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
-    """
-    if tag_rows:
-        ins_links = (
-            sqlite.insert(AssetInfoTag)
-            .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
-        )
-        for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
-            session.execute(ins_links, chunk)
-    if meta_rows:
-        ins_meta = (
-            sqlite.insert(AssetInfoMeta)
-            .on_conflict_do_nothing(
-                index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
-            )
-        )
-        for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
-            session.execute(ins_meta, chunk)

app/assets/database/models.py

@@ -1,233 +0,0 @@
-from __future__ import annotations
-
-import uuid
-from datetime import datetime
-
-from typing import Any
-from sqlalchemy import (
-    JSON,
-    BigInteger,
-    Boolean,
-    CheckConstraint,
-    DateTime,
-    ForeignKey,
-    Index,
-    Integer,
-    Numeric,
-    String,
-    Text,
-    UniqueConstraint,
-)
-from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
-
-from app.assets.helpers import utcnow
-from app.database.models import to_dict, Base
-
-
-class Asset(Base):
-    __tablename__ = "assets"
-
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
-    hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
-    size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
-    mime_type: Mapped[str | None] = mapped_column(String(255))
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=utcnow
-    )
-
-    infos: Mapped[list[AssetInfo]] = relationship(
-        "AssetInfo",
-        back_populates="asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
-        foreign_keys=lambda: [AssetInfo.asset_id],
-        cascade="all,delete-orphan",
-        passive_deletes=True,
-    )
-
-    preview_of: Mapped[list[AssetInfo]] = relationship(
-        "AssetInfo",
-        back_populates="preview_asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
-        foreign_keys=lambda: [AssetInfo.preview_id],
-        viewonly=True,
-    )
-
-    cache_states: Mapped[list[AssetCacheState]] = relationship(
-        back_populates="asset",
-        cascade="all, delete-orphan",
-        passive_deletes=True,
-    )
-
-    __table_args__ = (
-        Index("uq_assets_hash", "hash", unique=True),
-        Index("ix_assets_mime_type", "mime_type"),
-        CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
-    )
-
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        return to_dict(self, include_none=include_none)
-
-    def __repr__(self) -> str:
-        return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
-
-
-class AssetCacheState(Base):
-    __tablename__ = "asset_cache_state"
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
-    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
-    file_path: Mapped[str] = mapped_column(Text, nullable=False)
-    mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
-    needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
-
-    asset: Mapped[Asset] = relationship(back_populates="cache_states")
-
-    __table_args__ = (
-        Index("ix_asset_cache_state_file_path", "file_path"),
-        Index("ix_asset_cache_state_asset_id", "asset_id"),
-        CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
-        UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
-    )
-
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        return to_dict(self, include_none=include_none)
-
-    def __repr__(self) -> str:
-        return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
-
-
-class AssetInfo(Base):
-    __tablename__ = "assets_info"
-
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
-    owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
-    name: Mapped[str] = mapped_column(String(512), nullable=False)
-    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
-    preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
-    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
-    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
-    last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
-
-    asset: Mapped[Asset] = relationship(
-        "Asset",
-        back_populates="infos",
-        foreign_keys=[asset_id],
-        lazy="selectin",
-    )
-    preview_asset: Mapped[Asset | None] = relationship(
-        "Asset",
-        back_populates="preview_of",
-        foreign_keys=[preview_id],
-    )
-
-    metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
-        back_populates="asset_info",
-        cascade="all,delete-orphan",
-        passive_deletes=True,
-    )
-
-    tag_links: Mapped[list[AssetInfoTag]] = relationship(
-        back_populates="asset_info",
-        cascade="all,delete-orphan",
-        passive_deletes=True,
-        overlaps="tags,asset_infos",
-    )
-
-    tags: Mapped[list[Tag]] = relationship(
-        secondary="asset_info_tags",
-        back_populates="asset_infos",
-        lazy="selectin",
-        viewonly=True,
-        overlaps="tag_links,asset_info_links,asset_infos,tag",
-    )
-
-    __table_args__ = (
-        UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
-        Index("ix_assets_info_owner_name", "owner_id", "name"),
-        Index("ix_assets_info_owner_id", "owner_id"),
-        Index("ix_assets_info_asset_id", "asset_id"),
-        Index("ix_assets_info_name", "name"),
-        Index("ix_assets_info_created_at", "created_at"),
-        Index("ix_assets_info_last_access_time", "last_access_time"),
-    )
-
-    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
-        data = to_dict(self, include_none=include_none)
-        data["tags"] = [t.name for t in self.tags]
-        return data
-
-    def __repr__(self) -> str:
-        return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
-
-
-class AssetInfoMeta(Base):
-    __tablename__ = "asset_info_meta"
-
-    asset_info_id: Mapped[str] = mapped_column(
-        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
-    )
-    key: Mapped[str] = mapped_column(String(256), primary_key=True)
-    ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
-
-    val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)
-    val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)
-    val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
-    val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
-
-    asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
-
-    __table_args__ = (
-        Index("ix_asset_info_meta_key", "key"),
-        Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
-        Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
-        Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
-    )
-
-
-class AssetInfoTag(Base):
-    __tablename__ = "asset_info_tags"
-
-    asset_info_id: Mapped[str] = mapped_column(
-        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
-    )
-    tag_name: Mapped[str] = mapped_column(
-        String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
-    )
-    origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
-    added_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=utcnow
-    )
-
-    asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
-    tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
-
-    __table_args__ = (
-        Index("ix_asset_info_tags_tag_name", "tag_name"),
-        Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
-    )
-
-
-class Tag(Base):
-    __tablename__ = "tags"
-
-    name: Mapped[str] = mapped_column(String(512), primary_key=True)
-    tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
-
-    asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
-        back_populates="tag",
-        overlaps="asset_infos,tags",
-    )
-    asset_infos: Mapped[list[AssetInfo]] = relationship(
-        secondary="asset_info_tags",
-        back_populates="tags",
-        viewonly=True,
-        overlaps="asset_info_links,tag_links,tags,asset_info",
-    )
-
-    __table_args__ = (
-        Index("ix_tags_tag_type", "tag_type"),
-    )
-
-    def __repr__(self) -> str:
-        return f"<Tag {self.name}>"

app/assets/database/queries.py

@@ -1,976 +0,0 @@
-import os
-import logging
-import sqlalchemy as sa
-from collections import defaultdict
-from datetime import datetime
-from typing import Iterable, Any
-from sqlalchemy import select, delete, exists, func
-from sqlalchemy.dialects import sqlite
-from sqlalchemy.exc import IntegrityError
-from sqlalchemy.orm import Session, contains_eager, noload
-from app.assets.database.models import Asset, AssetInfo, AssetCacheState, AssetInfoMeta, AssetInfoTag, Tag
-from app.assets.helpers import (
-    compute_relative_filename, escape_like_prefix, normalize_tags, project_kv, utcnow
-)
-from typing import Sequence
-
-
-def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
-    """Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
-    owner_id = (owner_id or "").strip()
-    if owner_id == "":
-        return AssetInfo.owner_id == ""
-    return AssetInfo.owner_id.in_(["", owner_id])
-
-
-def pick_best_live_path(states: Sequence[AssetCacheState]) -> str:
-    """
-    Return the best on-disk path among cache states:
-      1) Prefer a path that exists with needs_verify == False (already verified).
-      2) Otherwise, pick the first path that exists.
-      3) Otherwise return empty string.
-    """
-    alive = [s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path)]
-    if not alive:
-        return ""
-    for s in alive:
-        if not getattr(s, "needs_verify", False):
-            return s.file_path
-    return alive[0].file_path
-
-
-def apply_tag_filters(
-    stmt: sa.sql.Select,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-) -> sa.sql.Select:
-    """include_tags: every tag must be present; exclude_tags: none may be present."""
-    include_tags = normalize_tags(include_tags)
-    exclude_tags = normalize_tags(exclude_tags)
-
-    if include_tags:
-        for tag_name in include_tags:
-            stmt = stmt.where(
-                exists().where(
-                    (AssetInfoTag.asset_info_id == AssetInfo.id)
-                    & (AssetInfoTag.tag_name == tag_name)
-                )
-            )
-
-    if exclude_tags:
-        stmt = stmt.where(
-            ~exists().where(
-                (AssetInfoTag.asset_info_id == AssetInfo.id)
-                & (AssetInfoTag.tag_name.in_(exclude_tags))
-            )
-        )
-    return stmt
-
-
-def apply_metadata_filter(
-    stmt: sa.sql.Select,
-    metadata_filter: dict | None = None,
-) -> sa.sql.Select:
-    """Apply filters using asset_info_meta projection table."""
-    if not metadata_filter:
-        return stmt
-
-    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
-        return sa.exists().where(
-            AssetInfoMeta.asset_info_id == AssetInfo.id,
-            AssetInfoMeta.key == key,
-            *preds,
-        )
-
-    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
-        if value is None:
-            no_row_for_key = sa.not_(
-                sa.exists().where(
-                    AssetInfoMeta.asset_info_id == AssetInfo.id,
-                    AssetInfoMeta.key == key,
-                )
-            )
-            null_row = _exists_for_pred(
-                key,
-                AssetInfoMeta.val_json.is_(None),
-                AssetInfoMeta.val_str.is_(None),
-                AssetInfoMeta.val_num.is_(None),
-                AssetInfoMeta.val_bool.is_(None),
-            )
-            return sa.or_(no_row_for_key, null_row)
-
-        if isinstance(value, bool):
-            return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
-        if isinstance(value, (int, float)):
-            from decimal import Decimal
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            return _exists_for_pred(key, AssetInfoMeta.val_num == num)
-        if isinstance(value, str):
-            return _exists_for_pred(key, AssetInfoMeta.val_str == value)
-        return _exists_for_pred(key, AssetInfoMeta.val_json == value)
-
-    for k, v in metadata_filter.items():
-        if isinstance(v, list):
-            ors = [_exists_clause_for_value(k, elem) for elem in v]
-            if ors:
-                stmt = stmt.where(sa.or_(*ors))
-        else:
-            stmt = stmt.where(_exists_clause_for_value(k, v))
-    return stmt
-
-
-def asset_exists_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
-    row = (
-        session.execute(
-            select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
-        )
-    ).first()
-    return row is not None
-
-
-def asset_info_exists_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> bool:
-    q = (
-        select(sa.literal(True))
-        .select_from(AssetInfo)
-        .where(AssetInfo.asset_id == asset_id)
-        .limit(1)
-    )
-    return (session.execute(q)).first() is not None
-
-
-def get_asset_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> Asset | None:
-    return (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-
-
-def get_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-) -> AssetInfo | None:
-    return session.get(AssetInfo, asset_info_id)
-
-
-def list_asset_infos_page(
-    session: Session,
-    owner_id: str = "",
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 20,
-    offset: int = 0,
-    sort: str = "created_at",
-    order: str = "desc",
-) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
-    base = (
-        select(AssetInfo)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
-        .where(visible_owner_clause(owner_id))
-    )
-
-    if name_contains:
-        escaped, esc = escape_like_prefix(name_contains)
-        base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
-
-    base = apply_tag_filters(base, include_tags, exclude_tags)
-    base = apply_metadata_filter(base, metadata_filter)
-
-    sort = (sort or "created_at").lower()
-    order = (order or "desc").lower()
-    sort_map = {
-        "name": AssetInfo.name,
-        "created_at": AssetInfo.created_at,
-        "updated_at": AssetInfo.updated_at,
-        "last_access_time": AssetInfo.last_access_time,
-        "size": Asset.size_bytes,
-    }
-    sort_col = sort_map.get(sort, AssetInfo.created_at)
-    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
-
-    base = base.order_by(sort_exp).limit(limit).offset(offset)
-
-    count_stmt = (
-        select(sa.func.count())
-        .select_from(AssetInfo)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .where(visible_owner_clause(owner_id))
-    )
-    if name_contains:
-        escaped, esc = escape_like_prefix(name_contains)
-        count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
-    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
-    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
-
-    total = int((session.execute(count_stmt)).scalar_one() or 0)
-
-    infos = (session.execute(base)).unique().scalars().all()
-
-    id_list: list[str] = [i.id for i in infos]
-    tag_map: dict[str, list[str]] = defaultdict(list)
-    if id_list:
-        rows = session.execute(
-            select(AssetInfoTag.asset_info_id, Tag.name)
-            .join(Tag, Tag.name == AssetInfoTag.tag_name)
-            .where(AssetInfoTag.asset_info_id.in_(id_list))
-            .order_by(AssetInfoTag.added_at)
-        )
-        for aid, tag_name in rows.all():
-            tag_map[aid].append(tag_name)
-
-    return infos, tag_map, total
-
-
-def fetch_asset_info_asset_and_tags(
-    session: Session,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[AssetInfo, Asset, list[str]] | None:
-    stmt = (
-        select(AssetInfo, Asset, Tag.name)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
-        .join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
-        .where(
-            AssetInfo.id == asset_info_id,
-            visible_owner_clause(owner_id),
-        )
-        .options(noload(AssetInfo.tags))
-        .order_by(Tag.name.asc())
-    )
-
-    rows = (session.execute(stmt)).all()
-    if not rows:
-        return None
-
-    first_info, first_asset, _ = rows[0]
-    tags: list[str] = []
-    seen: set[str] = set()
-    for _info, _asset, tag_name in rows:
-        if tag_name and tag_name not in seen:
-            seen.add(tag_name)
-            tags.append(tag_name)
-    return first_info, first_asset, tags
-
-
-def fetch_asset_info_and_asset(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[AssetInfo, Asset] | None:
-    stmt = (
-        select(AssetInfo, Asset)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .where(
-            AssetInfo.id == asset_info_id,
-            visible_owner_clause(owner_id),
-        )
-        .limit(1)
-        .options(noload(AssetInfo.tags))
-    )
-    row = session.execute(stmt)
-    pair = row.first()
-    if not pair:
-        return None
-    return pair[0], pair[1]
-
-def list_cache_states_by_asset_id(
-    session: Session, *, asset_id: str
-) -> Sequence[AssetCacheState]:
-    return (
-        session.execute(
-            select(AssetCacheState)
-            .where(AssetCacheState.asset_id == asset_id)
-            .order_by(AssetCacheState.id.asc())
-        )
-    ).scalars().all()
-
-
-def touch_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    ts: datetime | None = None,
-    only_if_newer: bool = True,
-) -> None:
-    ts = ts or utcnow()
-    stmt = sa.update(AssetInfo).where(AssetInfo.id == asset_info_id)
-    if only_if_newer:
-        stmt = stmt.where(
-            sa.or_(AssetInfo.last_access_time.is_(None), AssetInfo.last_access_time < ts)
-        )
-    session.execute(stmt.values(last_access_time=ts))
-
-
-def create_asset_info_for_existing_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    name: str,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] | None = None,
-    tag_origin: str = "manual",
-    owner_id: str = "",
-) -> AssetInfo:
-    """Create or return an existing AssetInfo for an Asset identified by asset_hash."""
-    now = utcnow()
-    asset = get_asset_by_hash(session, asset_hash=asset_hash)
-    if not asset:
-        raise ValueError(f"Unknown asset hash {asset_hash}")
-
-    info = AssetInfo(
-        owner_id=owner_id,
-        name=name,
-        asset_id=asset.id,
-        preview_id=None,
-        created_at=now,
-        updated_at=now,
-        last_access_time=now,
-    )
-    try:
-        with session.begin_nested():
-            session.add(info)
-            session.flush()
-    except IntegrityError:
-        existing = (
-            session.execute(
-                select(AssetInfo)
-                .options(noload(AssetInfo.tags))
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == name,
-                    AssetInfo.owner_id == owner_id,
-                )
-                .limit(1)
-            )
-        ).unique().scalars().first()
-        if not existing:
-            raise RuntimeError("AssetInfo upsert failed to find existing row after conflict.")
-        return existing
-
-    # metadata["filename"] hack
-    new_meta = dict(user_metadata or {})
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-    if computed_filename:
-        new_meta["filename"] = computed_filename
-    if new_meta:
-        replace_asset_info_metadata_projection(
-            session,
-            asset_info_id=info.id,
-            user_metadata=new_meta,
-        )
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=info.id,
-            tags=tags,
-            origin=tag_origin,
-        )
-    return info
-
-
-def set_asset_info_tags(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-) -> dict:
-    desired = normalize_tags(tags)
-
-    current = set(
-        tag_name for (tag_name,) in (
-            session.execute(select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id))
-        ).all()
-    )
-
-    to_add = [t for t in desired if t not in current]
-    to_remove = [t for t in current if t not in desired]
-
-    if to_add:
-        ensure_tags_exist(session, to_add, tag_type="user")
-        session.add_all([
-            AssetInfoTag(asset_info_id=asset_info_id, tag_name=t, origin=origin, added_at=utcnow())
-            for t in to_add
-        ])
-        session.flush()
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(AssetInfoTag.asset_info_id == asset_info_id, AssetInfoTag.tag_name.in_(to_remove))
-        )
-        session.flush()
-
-    return {"added": to_add, "removed": to_remove, "total": desired}
-
-
-def replace_asset_info_metadata_projection(
-    session: Session,
-    *,
-    asset_info_id: str,
-    user_metadata: dict | None = None,
-) -> None:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    info.user_metadata = user_metadata or {}
-    info.updated_at = utcnow()
-    session.flush()
-
-    session.execute(delete(AssetInfoMeta).where(AssetInfoMeta.asset_info_id == asset_info_id))
-    session.flush()
-
-    if not user_metadata:
-        return
-
-    rows: list[AssetInfoMeta] = []
-    for k, v in user_metadata.items():
-        for r in project_kv(k, v):
-            rows.append(
-                AssetInfoMeta(
-                    asset_info_id=asset_info_id,
-                    key=r["key"],
-                    ordinal=int(r["ordinal"]),
-                    val_str=r.get("val_str"),
-                    val_num=r.get("val_num"),
-                    val_bool=r.get("val_bool"),
-                    val_json=r.get("val_json"),
-                )
-            )
-    if rows:
-        session.add_all(rows)
-        session.flush()
-
-
-def ingest_fs_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    abs_path: str,
-    size_bytes: int,
-    mtime_ns: int,
-    mime_type: str | None = None,
-    info_name: str | None = None,
-    owner_id: str = "",
-    preview_id: str | None = None,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] = (),
-    tag_origin: str = "manual",
-    require_existing_tags: bool = False,
-) -> dict:
-    """
-    Idempotently upsert:
-      - Asset by content hash (create if missing)
-      - AssetCacheState(file_path) pointing to asset_id
-      - Optionally AssetInfo + tag links and metadata projection
-    Returns flags and ids.
-    """
-    locator = os.path.abspath(abs_path)
-    now = utcnow()
-
-    if preview_id:
-        if not session.get(Asset, preview_id):
-            preview_id = None
-
-    out: dict[str, Any] = {
-        "asset_created": False,
-        "asset_updated": False,
-        "state_created": False,
-        "state_updated": False,
-        "asset_info_id": None,
-    }
-
-    # 1) Asset by hash
-    asset = (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-    if not asset:
-        vals = {
-            "hash": asset_hash,
-            "size_bytes": int(size_bytes),
-            "mime_type": mime_type,
-            "created_at": now,
-        }
-        res = session.execute(
-            sqlite.insert(Asset)
-            .values(**vals)
-            .on_conflict_do_nothing(index_elements=[Asset.hash])
-        )
-        if int(res.rowcount or 0) > 0:
-            out["asset_created"] = True
-        asset = (
-            session.execute(
-                select(Asset).where(Asset.hash == asset_hash).limit(1)
-            )
-        ).scalars().first()
-        if not asset:
-            raise RuntimeError("Asset row not found after upsert.")
-    else:
-        changed = False
-        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
-            asset.size_bytes = int(size_bytes)
-            changed = True
-        if mime_type and asset.mime_type != mime_type:
-            asset.mime_type = mime_type
-            changed = True
-        if changed:
-            out["asset_updated"] = True
-
-    # 2) AssetCacheState upsert by file_path (unique)
-    vals = {
-        "asset_id": asset.id,
-        "file_path": locator,
-        "mtime_ns": int(mtime_ns),
-    }
-    ins = (
-        sqlite.insert(AssetCacheState)
-        .values(**vals)
-        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-    )
-
-    res = session.execute(ins)
-    if int(res.rowcount or 0) > 0:
-        out["state_created"] = True
-    else:
-        upd = (
-            sa.update(AssetCacheState)
-            .where(AssetCacheState.file_path == locator)
-            .where(
-                sa.or_(
-                    AssetCacheState.asset_id != asset.id,
-                    AssetCacheState.mtime_ns.is_(None),
-                    AssetCacheState.mtime_ns != int(mtime_ns),
-                )
-            )
-            .values(asset_id=asset.id, mtime_ns=int(mtime_ns))
-        )
-        res2 = session.execute(upd)
-        if int(res2.rowcount or 0) > 0:
-            out["state_updated"] = True
-
-    # 3) Optional AssetInfo + tags + metadata
-    if info_name:
-        try:
-            with session.begin_nested():
-                info = AssetInfo(
-                    owner_id=owner_id,
-                    name=info_name,
-                    asset_id=asset.id,
-                    preview_id=preview_id,
-                    created_at=now,
-                    updated_at=now,
-                    last_access_time=now,
-                )
-                session.add(info)
-                session.flush()
-                out["asset_info_id"] = info.id
-        except IntegrityError:
-            pass
-
-        existing_info = (
-            session.execute(
-                select(AssetInfo)
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == info_name,
-                    (AssetInfo.owner_id == owner_id),
-                )
-                .limit(1)
-            )
-        ).unique().scalar_one_or_none()
-        if not existing_info:
-            raise RuntimeError("Failed to update or insert AssetInfo.")
-
-        if preview_id and existing_info.preview_id != preview_id:
-            existing_info.preview_id = preview_id
-
-        existing_info.updated_at = now
-        if existing_info.last_access_time < now:
-            existing_info.last_access_time = now
-        session.flush()
-        out["asset_info_id"] = existing_info.id
-
-        norm = [t.strip().lower() for t in (tags or []) if (t or "").strip()]
-        if norm and out["asset_info_id"] is not None:
-            if not require_existing_tags:
-                ensure_tags_exist(session, norm, tag_type="user")
-
-            existing_tag_names = set(
-                name for (name,) in (session.execute(select(Tag.name).where(Tag.name.in_(norm)))).all()
-            )
-            missing = [t for t in norm if t not in existing_tag_names]
-            if missing and require_existing_tags:
-                raise ValueError(f"Unknown tags: {missing}")
-
-            existing_links = set(
-                tag_name
-                for (tag_name,) in (
-                    session.execute(
-                        select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == out["asset_info_id"])
-                    )
-                ).all()
-            )
-            to_add = [t for t in norm if t in existing_tag_names and t not in existing_links]
-            if to_add:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=out["asset_info_id"],
-                            tag_name=t,
-                            origin=tag_origin,
-                            added_at=now,
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-
-        # metadata["filename"] hack
-        if out["asset_info_id"] is not None:
-            primary_path = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-            computed_filename = compute_relative_filename(primary_path) if primary_path else None
-
-            current_meta = existing_info.user_metadata or {}
-            new_meta = dict(current_meta)
-            if user_metadata is not None:
-                for k, v in user_metadata.items():
-                    new_meta[k] = v
-            if computed_filename:
-                new_meta["filename"] = computed_filename
-
-            if new_meta != current_meta:
-                replace_asset_info_metadata_projection(
-                    session,
-                    asset_info_id=out["asset_info_id"],
-                    user_metadata=new_meta,
-                )
-
-    try:
-        remove_missing_tag_for_asset_id(session, asset_id=asset.id)
-    except Exception:
-        logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
-    return out
-
-
-def update_asset_info_full(
-    session: Session,
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: Sequence[str] | None = None,
-    user_metadata: dict | None = None,
-    tag_origin: str = "manual",
-    asset_info_row: Any = None,
-) -> AssetInfo:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-    else:
-        info = asset_info_row
-
-    touched = False
-    if name is not None and name != info.name:
-        info.name = name
-        touched = True
-
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=info.asset_id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-
-    if user_metadata is not None:
-        new_meta = dict(user_metadata)
-        if computed_filename:
-            new_meta["filename"] = computed_filename
-        replace_asset_info_metadata_projection(
-            session, asset_info_id=asset_info_id, user_metadata=new_meta
-        )
-        touched = True
-    else:
-        if computed_filename:
-            current_meta = info.user_metadata or {}
-            if current_meta.get("filename") != computed_filename:
-                new_meta = dict(current_meta)
-                new_meta["filename"] = computed_filename
-                replace_asset_info_metadata_projection(
-                    session, asset_info_id=asset_info_id, user_metadata=new_meta
-                )
-                touched = True
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=tag_origin,
-        )
-        touched = True
-
-    if touched and user_metadata is None:
-        info.updated_at = utcnow()
-        session.flush()
-
-    return info
-
-
-def delete_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str,
-) -> bool:
-    stmt = sa.delete(AssetInfo).where(
-        AssetInfo.id == asset_info_id,
-        visible_owner_clause(owner_id),
-    )
-    return int((session.execute(stmt)).rowcount or 0) > 0
-
-
-def list_tags_with_usage(
-    session: Session,
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    include_zero: bool = True,
-    order: str = "count_desc",
-    owner_id: str = "",
-) -> tuple[list[tuple[str, str, int]], int]:
-    counts_sq = (
-        select(
-            AssetInfoTag.tag_name.label("tag_name"),
-            func.count(AssetInfoTag.asset_info_id).label("cnt"),
-        )
-        .select_from(AssetInfoTag)
-        .join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
-        .where(visible_owner_clause(owner_id))
-        .group_by(AssetInfoTag.tag_name)
-        .subquery()
-    )
-
-    q = (
-        select(
-            Tag.name,
-            Tag.tag_type,
-            func.coalesce(counts_sq.c.cnt, 0).label("count"),
-        )
-        .select_from(Tag)
-        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
-    )
-
-    if prefix:
-        escaped, esc = escape_like_prefix(prefix.strip().lower())
-        q = q.where(Tag.name.like(escaped + "%", escape=esc))
-
-    if not include_zero:
-        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
-
-    if order == "name_asc":
-        q = q.order_by(Tag.name.asc())
-    else:
-        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
-
-    total_q = select(func.count()).select_from(Tag)
-    if prefix:
-        escaped, esc = escape_like_prefix(prefix.strip().lower())
-        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
-    if not include_zero:
-        total_q = total_q.where(
-            Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
-        )
-
-    rows = (session.execute(q.limit(limit).offset(offset))).all()
-    total = (session.execute(total_q)).scalar_one()
-
-    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
-    return rows_norm, int(total or 0)
-
-
-def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-        sqlite.insert(Tag)
-        .values(rows)
-        .on_conflict_do_nothing(index_elements=[Tag.name])
-    )
-    session.execute(ins)
-
-
-def get_asset_tags(session: Session, *, asset_info_id: str) -> list[str]:
-    return [
-        tag_name for (tag_name,) in (
-            session.execute(
-                select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    ]
-
-
-def add_tags_to_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-    create_if_missing: bool = True,
-    asset_info_row: Any = None,
-) -> dict:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"added": [], "already_present": [], "total_tags": total}
-
-    if create_if_missing:
-        ensure_tags_exist(session, norm, tag_type="user")
-
-    current = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    want = set(norm)
-    to_add = sorted(want - current)
-
-    if to_add:
-        with session.begin_nested() as nested:
-            try:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=asset_info_id,
-                            tag_name=t,
-                            origin=origin,
-                            added_at=utcnow(),
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-            except IntegrityError:
-                nested.rollback()
-
-    after = set(get_asset_tags(session, asset_info_id=asset_info_id))
-    return {
-        "added": sorted(((after - current) & want)),
-        "already_present": sorted(want & current),
-        "total_tags": sorted(after),
-    }
-
-
-def remove_tags_from_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-) -> dict:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"removed": [], "not_present": [], "total_tags": total}
-
-    existing = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    to_remove = sorted(set(t for t in norm if t in existing))
-    not_present = sorted(set(t for t in norm if t not in existing))
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(
-                AssetInfoTag.asset_info_id == asset_info_id,
-                AssetInfoTag.tag_name.in_(to_remove),
-            )
-        )
-        session.flush()
-
-    total = get_asset_tags(session, asset_info_id=asset_info_id)
-    return {"removed": to_remove, "not_present": not_present, "total_tags": total}
-
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sa.delete(AssetInfoTag).where(
-            AssetInfoTag.asset_info_id.in_(sa.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
-            AssetInfoTag.tag_name == "missing",
-        )
-    )
-
-
-def set_asset_info_preview(
-    session: Session,
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-) -> None:
-    """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    if preview_asset_id is None:
-        info.preview_id = None
-    else:
-        # validate preview asset exists
-        if not session.get(Asset, preview_asset_id):
-            raise ValueError(f"Preview Asset {preview_asset_id} not found")
-        info.preview_id = preview_asset_id
-
-    info.updated_at = utcnow()
-    session.flush()

app/assets/database/tags.py

@@ -1,62 +0,0 @@
-from typing import Iterable
-
-import sqlalchemy
-from sqlalchemy.orm import Session
-from sqlalchemy.dialects import sqlite
-
-from app.assets.helpers import normalize_tags, utcnow
-from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
-
-
-def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-            sqlite.insert(Tag)
-            .values(rows)
-            .on_conflict_do_nothing(index_elements=[Tag.name])
-        )
-    return session.execute(ins)
-
-def add_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-    origin: str = "automatic",
-) -> None:
-    select_rows = (
-        sqlalchemy.select(
-            AssetInfo.id.label("asset_info_id"),
-            sqlalchemy.literal("missing").label("tag_name"),
-            sqlalchemy.literal(origin).label("origin"),
-            sqlalchemy.literal(utcnow()).label("added_at"),
-        )
-        .where(AssetInfo.asset_id == asset_id)
-        .where(
-            sqlalchemy.not_(
-                sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
-            )
-        )
-    )
-    session.execute(
-        sqlite.insert(AssetInfoTag)
-        .from_select(
-            ["asset_info_id", "tag_name", "origin", "added_at"],
-            select_rows,
-        )
-        .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
-    )
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sqlalchemy.delete(AssetInfoTag).where(
-            AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
-            AssetInfoTag.tag_name == "missing",
-        )
-    )

app/assets/hashing.py

@@ -1,75 +0,0 @@
-from blake3 import blake3
-from typing import IO
-import os
-import asyncio
-
-
-DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
-
-# NOTE: this allows hashing different representations of a file-like object
-def blake3_hash(
-    fp: str | IO[bytes],
-    chunk_size: int = DEFAULT_CHUNK,
-) -> str:
-    """
-    Returns a BLAKE3 hex digest for ``fp``, which may be:
-      - a filename (str/bytes) or PathLike
-      - an open binary file object
-    If ``fp`` is a file object, it must be opened in **binary** mode and support
-    ``read``, ``seek``, and ``tell``. The function will seek to the start before
-    reading and will attempt to restore the original position afterward.
-    """
-    # duck typing to check if input is a file-like object
-    if hasattr(fp, "read"):
-        return _hash_file_obj(fp, chunk_size)
-
-    with open(os.fspath(fp), "rb") as f:
-        return _hash_file_obj(f, chunk_size)
-
-
-async def blake3_hash_async(
-    fp: str | IO[bytes],
-    chunk_size: int = DEFAULT_CHUNK,
-) -> str:
-    """Async wrapper for ``blake3_hash_sync``.
-    Uses a worker thread so the event loop remains responsive.
-    """
-    # If it is a path, open inside the worker thread to keep I/O off the loop.
-    if hasattr(fp, "read"):
-        return await asyncio.to_thread(blake3_hash, fp, chunk_size)
-
-    def _worker() -> str:
-        with open(os.fspath(fp), "rb") as f:
-            return _hash_file_obj(f, chunk_size)
-
-    return await asyncio.to_thread(_worker)
-
-
-def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
-    """
-    Hash an already-open binary file object by streaming in chunks.
-    - Seeks to the beginning before reading (if supported).
-    - Restores the original position afterward (if tell/seek are supported).
-    """
-    if chunk_size <= 0:
-        chunk_size = DEFAULT_CHUNK
-
-    # in case file object is already open and not at the beginning, track so can be restored after hashing
-    orig_pos = file_obj.tell()
-
-    try:
-        # seek to the beginning before reading
-        if orig_pos != 0:
-            file_obj.seek(0)
-
-        h = blake3()
-        while True:
-            chunk = file_obj.read(chunk_size)
-            if not chunk:
-                break
-            h.update(chunk)
-        return h.hexdigest()
-    finally:
-        # restore original position in file object, if needed
-        if orig_pos != 0:
-            file_obj.seek(orig_pos)

app/assets/helpers.py

@@ -1,312 +0,0 @@
-import contextlib
-import os
-from decimal import Decimal
-from aiohttp import web
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Literal, Any
-
-import folder_paths
-
-
-RootType = Literal["models", "input", "output"]
-ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
-
-def get_query_dict(request: web.Request) -> dict[str, Any]:
-    """
-    Gets a dictionary of query parameters from the request.
-
-    'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
-    """
-    query_dict = {
-        key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
-        for key in request.query.keys()
-    }
-    return query_dict
-
-def list_tree(base_dir: str) -> list[str]:
-    out: list[str] = []
-    base_abs = os.path.abspath(base_dir)
-    if not os.path.isdir(base_abs):
-        return out
-    for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
-        for name in filenames:
-            out.append(os.path.abspath(os.path.join(dirpath, name)))
-    return out
-
-def prefixes_for_root(root: RootType) -> list[str]:
-    if root == "models":
-        bases: list[str] = []
-        for _bucket, paths in get_comfy_models_folders():
-            bases.extend(paths)
-        return [os.path.abspath(p) for p in bases]
-    if root == "input":
-        return [os.path.abspath(folder_paths.get_input_directory())]
-    if root == "output":
-        return [os.path.abspath(folder_paths.get_output_directory())]
-    return []
-
-def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
-    """Escapes %, _ and the escape char itself in a LIKE prefix.
-    Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
-    """
-    s = s.replace(escape, escape + escape)  # escape the escape char first
-    s = s.replace("%", escape + "%").replace("_", escape + "_")  # escape LIKE wildcards
-    return s, escape
-
-def fast_asset_file_check(
-    *,
-    mtime_db: int | None,
-    size_db: int | None,
-    stat_result: os.stat_result,
-) -> bool:
-    if mtime_db is None:
-        return False
-    actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
-    if int(mtime_db) != int(actual_mtime_ns):
-        return False
-    sz = int(size_db or 0)
-    if sz > 0:
-        return int(stat_result.st_size) == sz
-    return True
-
-def utcnow() -> datetime:
-    """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
-    return datetime.now(timezone.utc).replace(tzinfo=None)
-
-def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
-    """Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
-
-    We trust `folder_paths.folder_names_and_paths` and include a category if
-    *any* of its base paths lies under the Comfy `models_dir`.
-    """
-    targets: list[tuple[str, list[str]]] = []
-    models_root = os.path.abspath(folder_paths.models_dir)
-    for name, values in folder_paths.folder_names_and_paths.items():
-        paths, _exts = values[0], values[1]  # NOTE: this prevents nodepacks that hackily edit folder_... from breaking ComfyUI
-        if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
-            targets.append((name, paths))
-    return targets
-
-def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    root = tags[0]
-    if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
-        try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
-        except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
-        if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
-        base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
-    else:
-        base_dir = os.path.abspath(
-            folder_paths.get_input_directory() if root == "input" else folder_paths.get_output_directory()
-        )
-        raw_subdirs = tags[1:]
-    for i in raw_subdirs:
-        if i in (".", ".."):
-            raise ValueError("invalid path component in tags")
-
-    return base_dir, raw_subdirs if raw_subdirs else []
-
-def ensure_within_base(candidate: str, base: str) -> None:
-    cand_abs = os.path.abspath(candidate)
-    base_abs = os.path.abspath(base)
-    try:
-        if os.path.commonpath([cand_abs, base_abs]) != base_abs:
-            raise ValueError("destination escapes base directory")
-    except Exception:
-        raise ValueError("invalid destination path")
-
-def compute_relative_filename(file_path: str) -> str | None:
-    """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
-      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
-      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
-
-    For non-model paths, returns None.
-    NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
-    """
-    try:
-        root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
-    except ValueError:
-        return None
-
-    p = Path(rel_path)
-    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
-    if not parts:
-        return None
-
-    if root_category == "models":
-        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
-        inside = parts[1:] if len(parts) > 1 else [parts[0]]
-        return "/".join(inside)
-    return "/".join(parts)  # input/output: keep all parts
-
-def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
-    """Given an absolute or relative file path, determine which root category the path belongs to:
-      - 'input' if the file resides under `folder_paths.get_input_directory()`
-      - 'output' if the file resides under `folder_paths.get_output_directory()`
-      - 'models' if the file resides under any base path of categories returned by `get_comfy_models_folders()`
-
-    Returns:
-        (root_category, relative_path_inside_that_root)
-        For 'models', the relative path is prefixed with the category name:
-            e.g. ('models', 'vae/test/sub/ae.safetensors')
-
-    Raises:
-        ValueError: if the path does not belong to input, output, or configured model bases.
-    """
-    fp_abs = os.path.abspath(file_path)
-
-    def _is_within(child: str, parent: str) -> bool:
-        try:
-            return os.path.commonpath([child, parent]) == parent
-        except Exception:
-            return False
-
-    def _rel(child: str, parent: str) -> str:
-        return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
-
-    # 1) input
-    input_base = os.path.abspath(folder_paths.get_input_directory())
-    if _is_within(fp_abs, input_base):
-        return "input", _rel(fp_abs, input_base)
-
-    # 2) output
-    output_base = os.path.abspath(folder_paths.get_output_directory())
-    if _is_within(fp_abs, output_base):
-        return "output", _rel(fp_abs, output_base)
-
-    # 3) models (check deepest matching base to avoid ambiguity)
-    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
-    for bucket, bases in get_comfy_models_folders():
-        for b in bases:
-            base_abs = os.path.abspath(b)
-            if not _is_within(fp_abs, base_abs):
-                continue
-            cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
-            if best is None or cand[0] > best[0]:
-                best = cand
-
-    if best is not None:
-        _, bucket, rel_inside = best
-        combined = os.path.join(bucket, rel_inside)
-        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
-
-    raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
-
-def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
-    """Return a tuple (name, tags) derived from a filesystem path.
-
-    Semantics:
-      - Root category is determined by `get_relative_to_root_category_path_of_asset`.
-      - The returned `name` is the base filename with extension from the relative path.
-      - The returned `tags` are:
-            [root_category] + parent folders of the relative path (in order)
-        For 'models', this means:
-            file '/.../ModelsDir/vae/test_tag/ae.safetensors'
-            -> root_category='models', some_path='vae/test_tag/ae.safetensors'
-            -> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
-
-    Raises:
-        ValueError: if the path does not belong to input, output, or configured model bases.
-    """
-    root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
-    p = Path(some_path)
-    parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
-
-def normalize_tags(tags: list[str] | None) -> list[str]:
-    """
-    Normalize a list of tags by:
-      - Stripping whitespace and converting to lowercase.
-      - Removing duplicates.
-    """
-    return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
-
-def collect_models_files() -> list[str]:
-    out: list[str] = []
-    for folder_name, bases in get_comfy_models_folders():
-        rel_files = folder_paths.get_filename_list(folder_name) or []
-        for rel_path in rel_files:
-            abs_path = folder_paths.get_full_path(folder_name, rel_path)
-            if not abs_path:
-                continue
-            abs_path = os.path.abspath(abs_path)
-            allowed = False
-            for b in bases:
-                base_abs = os.path.abspath(b)
-                with contextlib.suppress(Exception):
-                    if os.path.commonpath([abs_path, base_abs]) == base_abs:
-                        allowed = True
-                        break
-            if allowed:
-                out.append(abs_path)
-    return out
-
-def is_scalar(v):
-    if v is None:
-        return True
-    if isinstance(v, bool):
-        return True
-    if isinstance(v, (int, float, Decimal, str)):
-        return True
-    return False
-
-def project_kv(key: str, value):
-    """
-    Turn a metadata key/value into typed projection rows.
-    Returns list[dict] with keys:
-      key, ordinal, and one of val_str / val_num / val_bool / val_json (others None)
-    """
-    rows: list[dict] = []
-
-    def _null_row(ordinal: int) -> dict:
-        return {
-            "key": key, "ordinal": ordinal,
-            "val_str": None, "val_num": None, "val_bool": None, "val_json": None
-        }
-
-    if value is None:
-        rows.append(_null_row(0))
-        return rows
-
-    if is_scalar(value):
-        if isinstance(value, bool):
-            rows.append({"key": key, "ordinal": 0, "val_bool": bool(value)})
-        elif isinstance(value, (int, float, Decimal)):
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            rows.append({"key": key, "ordinal": 0, "val_num": num})
-        elif isinstance(value, str):
-            rows.append({"key": key, "ordinal": 0, "val_str": value})
-        else:
-            rows.append({"key": key, "ordinal": 0, "val_json": value})
-        return rows
-
-    if isinstance(value, list):
-        if all(is_scalar(x) for x in value):
-            for i, x in enumerate(value):
-                if x is None:
-                    rows.append(_null_row(i))
-                elif isinstance(x, bool):
-                    rows.append({"key": key, "ordinal": i, "val_bool": bool(x)})
-                elif isinstance(x, (int, float, Decimal)):
-                    num = x if isinstance(x, Decimal) else Decimal(str(x))
-                    rows.append({"key": key, "ordinal": i, "val_num": num})
-                elif isinstance(x, str):
-                    rows.append({"key": key, "ordinal": i, "val_str": x})
-                else:
-                    rows.append({"key": key, "ordinal": i, "val_json": x})
-            return rows
-        for i, x in enumerate(value):
-            rows.append({"key": key, "ordinal": i, "val_json": x})
-        return rows
-
-    rows.append({"key": key, "ordinal": 0, "val_json": value})
-    return rows

app/assets/manager.py

@@ -1,516 +0,0 @@
-import os
-import mimetypes
-import contextlib
-from typing import Sequence
-
-from app.database.db import create_session
-from app.assets.api import schemas_out, schemas_in
-from app.assets.database.queries import (
-    asset_exists_by_hash,
-    asset_info_exists_for_asset_id,
-    get_asset_by_hash,
-    get_asset_info_by_id,
-    fetch_asset_info_asset_and_tags,
-    fetch_asset_info_and_asset,
-    create_asset_info_for_existing_asset,
-    touch_asset_info_by_id,
-    update_asset_info_full,
-    delete_asset_info_by_id,
-    list_cache_states_by_asset_id,
-    list_asset_infos_page,
-    list_tags_with_usage,
-    get_asset_tags,
-    add_tags_to_asset_info,
-    remove_tags_from_asset_info,
-    pick_best_live_path,
-    ingest_fs_asset,
-    set_asset_info_preview,
-)
-from app.assets.helpers import resolve_destination_from_tags, ensure_within_base
-from app.assets.database.models import Asset
-
-
-def _safe_sort_field(requested: str | None) -> str:
-    if not requested:
-        return "created_at"
-    v = requested.lower()
-    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
-        return v
-    return "created_at"
-
-
-def _get_size_mtime_ns(path: str) -> tuple[int, int]:
-    st = os.stat(path, follow_symlinks=True)
-    return st.st_size, getattr(st, "st_mtime_ns", int(st.st_mtime * 1_000_000_000))
-
-
-def _safe_filename(name: str | None, fallback: str) -> str:
-    n = os.path.basename((name or "").strip() or fallback)
-    if n:
-        return n
-    return fallback
-
-
-def asset_exists(*, asset_hash: str) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
-    with create_session() as session:
-        return asset_exists_by_hash(session, asset_hash=asset_hash)
-
-
-def list_assets(
-    *,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 20,
-    offset: int = 0,
-    sort: str = "created_at",
-    order: str = "desc",
-    owner_id: str = "",
-) -> schemas_out.AssetsList:
-    sort = _safe_sort_field(sort)
-    order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
-
-    with create_session() as session:
-        infos, tag_map, total = list_asset_infos_page(
-            session,
-            owner_id=owner_id,
-            include_tags=include_tags,
-            exclude_tags=exclude_tags,
-            name_contains=name_contains,
-            metadata_filter=metadata_filter,
-            limit=limit,
-            offset=offset,
-            sort=sort,
-            order=order,
-        )
-
-    summaries: list[schemas_out.AssetSummary] = []
-    for info in infos:
-        asset = info.asset
-        tags = tag_map.get(info.id, [])
-        summaries.append(
-            schemas_out.AssetSummary(
-                id=info.id,
-                name=info.name,
-                asset_hash=asset.hash if asset else None,
-                size=int(asset.size_bytes) if asset else None,
-                mime_type=asset.mime_type if asset else None,
-                tags=tags,
-                created_at=info.created_at,
-                updated_at=info.updated_at,
-                last_access_time=info.last_access_time,
-            )
-        )
-
-    return schemas_out.AssetsList(
-        assets=summaries,
-        total=total,
-        has_more=(offset + len(summaries)) < total,
-    )
-
-
-def get_asset(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
-    with create_session() as session:
-        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not res:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        info, asset, tag_names = res
-        preview_id = info.preview_id
-
-    return schemas_out.AssetDetail(
-        id=info.id,
-        name=info.name,
-        asset_hash=asset.hash if asset else None,
-        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
-        mime_type=asset.mime_type if asset else None,
-        tags=tag_names,
-        user_metadata=info.user_metadata or {},
-        preview_id=preview_id,
-        created_at=info.created_at,
-        last_access_time=info.last_access_time,
-    )
-
-
-def resolve_asset_content_for_download(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[str, str, str]:
-    with create_session() as session:
-        pair = fetch_asset_info_and_asset(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not pair:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-        info, asset = pair
-        states = list_cache_states_by_asset_id(session, asset_id=asset.id)
-        abs_path = pick_best_live_path(states)
-        if not abs_path:
-            raise FileNotFoundError
-
-        touch_asset_info_by_id(session, asset_info_id=asset_info_id)
-        session.commit()
-
-        ctype = asset.mime_type or mimetypes.guess_type(info.name or abs_path)[0] or "application/octet-stream"
-        download_name = info.name or os.path.basename(abs_path)
-        return abs_path, ctype, download_name
-
-
-def upload_asset_from_temp_path(
-    spec: schemas_in.UploadAssetSpec,
-    *,
-    temp_path: str,
-    client_filename: str | None = None,
-    owner_id: str = "",
-    expected_asset_hash: str | None = None,
-) -> schemas_out.AssetCreated:
-    """
-    Create new asset or update existing asset from a temporary file path.
-    """
-    try:
-        # NOTE: blake3 is not required right now, so this will fail if blake3 is not installed in local environment
-        import app.assets.hashing as hashing
-        digest = hashing.blake3_hash(temp_path)
-    except Exception as e:
-        raise RuntimeError(f"failed to hash uploaded file: {e}")
-    asset_hash = "blake3:" + digest
-
-    if expected_asset_hash and asset_hash != expected_asset_hash.strip().lower():
-        raise ValueError("HASH_MISMATCH")
-
-    with create_session() as session:
-        existing = get_asset_by_hash(session, asset_hash=asset_hash)
-        if existing is not None:
-            with contextlib.suppress(Exception):
-                if temp_path and os.path.exists(temp_path):
-                    os.remove(temp_path)
-
-            display_name = _safe_filename(spec.name or (client_filename or ""), fallback=digest)
-            info = create_asset_info_for_existing_asset(
-                session,
-                asset_hash=asset_hash,
-                name=display_name,
-                user_metadata=spec.user_metadata or {},
-                tags=spec.tags or [],
-                tag_origin="manual",
-                owner_id=owner_id,
-            )
-            tag_names = get_asset_tags(session, asset_info_id=info.id)
-            session.commit()
-
-            return schemas_out.AssetCreated(
-                id=info.id,
-                name=info.name,
-                asset_hash=existing.hash,
-                size=int(existing.size_bytes) if existing.size_bytes is not None else None,
-                mime_type=existing.mime_type,
-                tags=tag_names,
-                user_metadata=info.user_metadata or {},
-                preview_id=info.preview_id,
-                created_at=info.created_at,
-                last_access_time=info.last_access_time,
-                created_new=False,
-            )
-
-    base_dir, subdirs = resolve_destination_from_tags(spec.tags)
-    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
-    os.makedirs(dest_dir, exist_ok=True)
-
-    src_for_ext = (client_filename or spec.name or "").strip()
-    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
-    ext = _ext if 0 < len(_ext) <= 16 else ""
-    hashed_basename = f"{digest}{ext}"
-    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
-    ensure_within_base(dest_abs, base_dir)
-
-    content_type = (
-        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
-        or mimetypes.guess_type(hashed_basename, strict=False)[0]
-        or "application/octet-stream"
-    )
-
-    try:
-        os.replace(temp_path, dest_abs)
-    except Exception as e:
-        raise RuntimeError(f"failed to move uploaded file into place: {e}")
-
-    try:
-        size_bytes, mtime_ns = _get_size_mtime_ns(dest_abs)
-    except OSError as e:
-        raise RuntimeError(f"failed to stat destination file: {e}")
-
-    with create_session() as session:
-        result = ingest_fs_asset(
-            session,
-            asset_hash=asset_hash,
-            abs_path=dest_abs,
-            size_bytes=size_bytes,
-            mtime_ns=mtime_ns,
-            mime_type=content_type,
-            info_name=_safe_filename(spec.name or (client_filename or ""), fallback=digest),
-            owner_id=owner_id,
-            preview_id=None,
-            user_metadata=spec.user_metadata or {},
-            tags=spec.tags,
-            tag_origin="manual",
-            require_existing_tags=False,
-        )
-        info_id = result["asset_info_id"]
-        if not info_id:
-            raise RuntimeError("failed to create asset metadata")
-
-        pair = fetch_asset_info_and_asset(session, asset_info_id=info_id, owner_id=owner_id)
-        if not pair:
-            raise RuntimeError("inconsistent DB state after ingest")
-        info, asset = pair
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        created_result = schemas_out.AssetCreated(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash,
-            size=int(asset.size_bytes),
-            mime_type=asset.mime_type,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-            created_new=result["asset_created"],
-        )
-        session.commit()
-
-    return created_result
-
-
-def update_asset(
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetUpdated:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        info = update_asset_info_full(
-            session,
-            asset_info_id=asset_info_id,
-            name=name,
-            tags=tags,
-            user_metadata=user_metadata,
-            tag_origin="manual",
-            asset_info_row=info_row,
-        )
-
-        tag_names = get_asset_tags(session, asset_info_id=asset_info_id)
-        result = schemas_out.AssetUpdated(
-            id=info.id,
-            name=info.name,
-            asset_hash=info.asset.hash if info.asset else None,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            updated_at=info.updated_at,
-        )
-        session.commit()
-
-    return result
-
-
-def set_asset_preview(
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        set_asset_info_preview(
-            session,
-            asset_info_id=asset_info_id,
-            preview_asset_id=preview_asset_id,
-        )
-
-        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not res:
-            raise RuntimeError("State changed during preview update")
-        info, asset, tags = res
-        result = schemas_out.AssetDetail(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash if asset else None,
-            size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
-            mime_type=asset.mime_type if asset else None,
-            tags=tags,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-        )
-        session.commit()
-
-    return result
-
-
-def delete_asset_reference(*, asset_info_id: str, owner_id: str, delete_content_if_orphan: bool = True) -> bool:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        asset_id = info_row.asset_id if info_row else None
-        deleted = delete_asset_info_by_id(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not deleted:
-            session.commit()
-            return False
-
-        if not delete_content_if_orphan or not asset_id:
-            session.commit()
-            return True
-
-        still_exists = asset_info_exists_for_asset_id(session, asset_id=asset_id)
-        if still_exists:
-            session.commit()
-            return True
-
-        states = list_cache_states_by_asset_id(session, asset_id=asset_id)
-        file_paths = [s.file_path for s in (states or []) if getattr(s, "file_path", None)]
-
-        asset_row = session.get(Asset, asset_id)
-        if asset_row is not None:
-            session.delete(asset_row)
-
-        session.commit()
-        for p in file_paths:
-            with contextlib.suppress(Exception):
-                if p and os.path.isfile(p):
-                    os.remove(p)
-    return True
-
-
-def create_asset_from_hash(
-    *,
-    hash_str: str,
-    name: str,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetCreated | None:
-    canonical = hash_str.strip().lower()
-    with create_session() as session:
-        asset = get_asset_by_hash(session, asset_hash=canonical)
-        if not asset:
-            return None
-
-        info = create_asset_info_for_existing_asset(
-            session,
-            asset_hash=canonical,
-            name=_safe_filename(name, fallback=canonical.split(":", 1)[1]),
-            user_metadata=user_metadata or {},
-            tags=tags or [],
-            tag_origin="manual",
-            owner_id=owner_id,
-        )
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        result = schemas_out.AssetCreated(
-            id=info.id,
-            name=info.name,
-            asset_hash=asset.hash,
-            size=int(asset.size_bytes),
-            mime_type=asset.mime_type,
-            tags=tag_names,
-            user_metadata=info.user_metadata or {},
-            preview_id=info.preview_id,
-            created_at=info.created_at,
-            last_access_time=info.last_access_time,
-            created_new=False,
-        )
-        session.commit()
-
-    return result
-
-
-def add_tags_to_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    origin: str = "manual",
-    owner_id: str = "",
-) -> schemas_out.TagsAdd:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-        data = add_tags_to_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=origin,
-            create_if_missing=True,
-            asset_info_row=info_row,
-        )
-        session.commit()
-    return schemas_out.TagsAdd(**data)
-
-
-def remove_tags_from_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    owner_id: str = "",
-) -> schemas_out.TagsRemove:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        data = remove_tags_from_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-        )
-        session.commit()
-    return schemas_out.TagsRemove(**data)
-
-
-def list_tags(
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    order: str = "count_desc",
-    include_zero: bool = True,
-    owner_id: str = "",
-) -> schemas_out.TagsList:
-    limit = max(1, min(1000, limit))
-    offset = max(0, offset)
-
-    with create_session() as session:
-        rows, total = list_tags_with_usage(
-            session,
-            prefix=prefix,
-            limit=limit,
-            offset=offset,
-            include_zero=include_zero,
-            order=order,
-            owner_id=owner_id,
-        )
-
-    tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
-    return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)

app/assets/scanner.py

@@ -1,263 +0,0 @@
-import contextlib
-import time
-import logging
-import os
-import sqlalchemy
-
-import folder_paths
-from app.database.db import create_session, dependencies_available
-from app.assets.helpers import (
-    collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
-    list_tree,prefixes_for_root, escape_like_prefix,
-    RootType
-)
-from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
-from app.assets.database.bulk_ops import seed_from_paths_batch
-from app.assets.database.models import Asset, AssetCacheState, AssetInfo
-
-
-def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
-    """
-    Scan the given roots and seed the assets into the database.
-    """
-    if not dependencies_available():
-        if enable_logging:
-            logging.warning("Database dependencies not available, skipping assets scan")
-        return
-    t_start = time.perf_counter()
-    created = 0
-    skipped_existing = 0
-    orphans_pruned = 0
-    paths: list[str] = []
-    try:
-        existing_paths: set[str] = set()
-        for r in roots:
-            try:
-                survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
-                if survivors:
-                    existing_paths.update(survivors)
-            except Exception as e:
-                logging.exception("fast DB scan failed for %s: %s", r, e)
-
-        try:
-            orphans_pruned = _prune_orphaned_assets(roots)
-        except Exception as e:
-            logging.exception("orphan pruning failed: %s", e)
-
-        if "models" in roots:
-            paths.extend(collect_models_files())
-        if "input" in roots:
-            paths.extend(list_tree(folder_paths.get_input_directory()))
-        if "output" in roots:
-            paths.extend(list_tree(folder_paths.get_output_directory()))
-
-        specs: list[dict] = []
-        tag_pool: set[str] = set()
-        for p in paths:
-            abs_p = os.path.abspath(p)
-            if abs_p in existing_paths:
-                skipped_existing += 1
-                continue
-            try:
-                stat_p = os.stat(abs_p, follow_symlinks=False)
-            except OSError:
-                continue
-            # skip empty files
-            if not stat_p.st_size:
-                continue
-            name, tags = get_name_and_tags_from_asset_path(abs_p)
-            specs.append(
-                {
-                    "abs_path": abs_p,
-                    "size_bytes": stat_p.st_size,
-                    "mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
-                    "info_name": name,
-                    "tags": tags,
-                    "fname": compute_relative_filename(abs_p),
-                }
-            )
-            for t in tags:
-                tag_pool.add(t)
-        # if no file specs, nothing to do
-        if not specs:
-            return
-        with create_session() as sess:
-            if tag_pool:
-                ensure_tags_exist(sess, tag_pool, tag_type="user")
-
-            result = seed_from_paths_batch(sess, specs=specs, owner_id="")
-            created += result["inserted_infos"]
-            sess.commit()
-    finally:
-        if enable_logging:
-            logging.info(
-                "Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, orphans_pruned=%d, total_seen=%d)",
-                roots,
-                time.perf_counter() - t_start,
-                created,
-                skipped_existing,
-                orphans_pruned,
-                len(paths),
-            )
-
-
-def _prune_orphaned_assets(roots: tuple[RootType, ...]) -> int:
-    """Prune cache states outside configured prefixes, then delete orphaned seed assets."""
-    all_prefixes = [os.path.abspath(p) for r in roots for p in prefixes_for_root(r)]
-    if not all_prefixes:
-        return 0
-
-    def make_prefix_condition(prefix: str):
-        base = prefix if prefix.endswith(os.sep) else prefix + os.sep
-        escaped, esc = escape_like_prefix(base)
-        return AssetCacheState.file_path.like(escaped + "%", escape=esc)
-
-    matches_valid_prefix = sqlalchemy.or_(*[make_prefix_condition(p) for p in all_prefixes])
-
-    orphan_subq = (
-        sqlalchemy.select(Asset.id)
-        .outerjoin(AssetCacheState, AssetCacheState.asset_id == Asset.id)
-        .where(Asset.hash.is_(None), AssetCacheState.id.is_(None))
-    ).scalar_subquery()
-
-    with create_session() as sess:
-        sess.execute(sqlalchemy.delete(AssetCacheState).where(~matches_valid_prefix))
-        sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id.in_(orphan_subq)))
-        result = sess.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(orphan_subq)))
-        sess.commit()
-        return result.rowcount
-
-
-def _fast_db_consistency_pass(
-    root: RootType,
-    *,
-    collect_existing_paths: bool = False,
-    update_missing_tags: bool = False,
-) -> set[str] | None:
-    """Fast DB+FS pass for a root:
-      - Toggle needs_verify per state using fast check
-      - For hashed assets with at least one fast-ok state in this root: delete stale missing states
-      - For seed assets with all states missing: delete Asset and its AssetInfos
-      - Optionally add/remove 'missing' tags based on fast-ok in this root
-      - Optionally return surviving absolute paths
-    """
-    prefixes = prefixes_for_root(root)
-    if not prefixes:
-        return set() if collect_existing_paths else None
-
-    conds = []
-    for p in prefixes:
-        base = os.path.abspath(p)
-        if not base.endswith(os.sep):
-            base += os.sep
-        escaped, esc = escape_like_prefix(base)
-        conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
-
-    with create_session() as sess:
-        rows = (
-            sess.execute(
-                sqlalchemy.select(
-                    AssetCacheState.id,
-                    AssetCacheState.file_path,
-                    AssetCacheState.mtime_ns,
-                    AssetCacheState.needs_verify,
-                    AssetCacheState.asset_id,
-                    Asset.hash,
-                    Asset.size_bytes,
-                )
-                .join(Asset, Asset.id == AssetCacheState.asset_id)
-                .where(sqlalchemy.or_(*conds))
-                .order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
-            )
-        ).all()
-
-        by_asset: dict[str, dict] = {}
-        for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
-            acc = by_asset.get(aid)
-            if acc is None:
-                acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
-                by_asset[aid] = acc
-
-            fast_ok = False
-            try:
-                exists = True
-                fast_ok = fast_asset_file_check(
-                    mtime_db=mtime_db,
-                    size_db=acc["size_db"],
-                    stat_result=os.stat(fp, follow_symlinks=True),
-                )
-            except FileNotFoundError:
-                exists = False
-            except OSError:
-                exists = False
-
-            acc["states"].append({
-                "sid": sid,
-                "fp": fp,
-                "exists": exists,
-                "fast_ok": fast_ok,
-                "needs_verify": bool(needs_verify),
-            })
-
-        to_set_verify: list[int] = []
-        to_clear_verify: list[int] = []
-        stale_state_ids: list[int] = []
-        survivors: set[str] = set()
-
-        for aid, acc in by_asset.items():
-            a_hash = acc["hash"]
-            states = acc["states"]
-            any_fast_ok = any(s["fast_ok"] for s in states)
-            all_missing = all(not s["exists"] for s in states)
-
-            for s in states:
-                if not s["exists"]:
-                    continue
-                if s["fast_ok"] and s["needs_verify"]:
-                    to_clear_verify.append(s["sid"])
-                if not s["fast_ok"] and not s["needs_verify"]:
-                    to_set_verify.append(s["sid"])
-
-            if a_hash is None:
-                if states and all_missing:  # remove seed Asset completely, if no valid AssetCache exists
-                    sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
-                    asset = sess.get(Asset, aid)
-                    if asset:
-                        sess.delete(asset)
-                else:
-                    for s in states:
-                        if s["exists"]:
-                            survivors.add(os.path.abspath(s["fp"]))
-                continue
-
-            if any_fast_ok:  # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
-                for s in states:
-                    if not s["exists"]:
-                        stale_state_ids.append(s["sid"])
-                if update_missing_tags:
-                    with contextlib.suppress(Exception):
-                        remove_missing_tag_for_asset_id(sess, asset_id=aid)
-            elif update_missing_tags:
-                with contextlib.suppress(Exception):
-                    add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
-
-            for s in states:
-                if s["exists"]:
-                    survivors.add(os.path.abspath(s["fp"]))
-
-        if stale_state_ids:
-            sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
-        if to_set_verify:
-            sess.execute(
-                sqlalchemy.update(AssetCacheState)
-                .where(AssetCacheState.id.in_(to_set_verify))
-                .values(needs_verify=True)
-            )
-        if to_clear_verify:
-            sess.execute(
-                sqlalchemy.update(AssetCacheState)
-                .where(AssetCacheState.id.in_(to_clear_verify))
-                .values(needs_verify=False)
-            )
-        sess.commit()
-        return survivors if collect_existing_paths else None

app/database/models.py

@@ -1,21 +1,14 @@
-from typing import Any
-from datetime import datetime
-from sqlalchemy.orm import DeclarativeBase
+from sqlalchemy.orm import declarative_base
 
-class Base(DeclarativeBase):
-    pass
+Base = declarative_base()
 
-def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
+
+def to_dict(obj):
     fields = obj.__table__.columns.keys()
-    out: dict[str, Any] = {}
-    for field in fields:
-        val = getattr(obj, field)
-        if val is None and not include_none:
-            continue
-        if isinstance(val, datetime):
-            out[field] = val.isoformat()
-        else:
-            out[field] = val
-    return out
+    return {
+        field: (val.to_dict() if hasattr(val, "to_dict") else val)
+        for field in fields
+        if (val := getattr(obj, field))
+    }
 
 # TODO: Define models here

app/frontend_management.py

@@ -10,8 +10,7 @@ import importlib
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import Dict, TypedDict, Optional
-from aiohttp import web
+from typing import TypedDict, Optional
 from importlib.metadata import version
 
 import requests
@@ -43,7 +42,6 @@ def get_installed_frontend_version():
     frontend_version_str = version("comfyui-frontend-package")
     return frontend_version_str
 
-
 def get_required_frontend_version():
     """Get the required frontend version from requirements.txt."""
     try:
@@ -65,7 +63,6 @@ def get_required_frontend_version():
         logging.error(f"Error reading requirements.txt: {e}")
         return None
 
-
 def check_frontend_version():
     """Check if the frontend version is up to date."""
 
@@ -206,37 +203,6 @@ class FrontendManager:
         """Get the required frontend package version."""
         return get_required_frontend_version()
 
-    @classmethod
-    def get_installed_templates_version(cls) -> str:
-        """Get the currently installed workflow templates package version."""
-        try:
-            templates_version_str = version("comfyui-workflow-templates")
-            return templates_version_str
-        except Exception:
-            return None
-
-    @classmethod
-    def get_required_templates_version(cls) -> str:
-        """Get the required workflow templates version from requirements.txt."""
-        try:
-            with open(requirements_path, "r", encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if line.startswith("comfyui-workflow-templates=="):
-                        version_str = line.split("==")[-1]
-                        if not is_valid_version(version_str):
-                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
-                            return None
-                        return version_str
-                logging.error("comfyui-workflow-templates not found in requirements.txt")
-                return None
-        except FileNotFoundError:
-            logging.error("requirements.txt not found. Cannot determine required templates version.")
-            return None
-        except Exception as e:
-            logging.error(f"Error reading requirements.txt: {e}")
-            return None
-
     @classmethod
     def default_frontend_path(cls) -> str:
         try:
@@ -258,54 +224,7 @@ comfyui-frontend-package is not installed.
             sys.exit(-1)
 
     @classmethod
-    def template_asset_map(cls) -> Optional[Dict[str, str]]:
-        """Return a mapping of template asset names to their absolute paths."""
-        try:
-            from comfyui_workflow_templates import (
-                get_asset_path,
-                iter_templates,
-            )
-        except ImportError:
-            logging.error(
-                f"""
-********** ERROR ***********
-
-comfyui-workflow-templates is not installed.
-
-{frontend_install_warning_message()}
-
-********** ERROR ***********
-""".strip()
-            )
-            return None
-
-        try:
-            template_entries = list(iter_templates())
-        except Exception as exc:
-            logging.error(f"Failed to enumerate workflow templates: {exc}")
-            return None
-
-        asset_map: Dict[str, str] = {}
-        try:
-            for entry in template_entries:
-                for asset in entry.assets:
-                    asset_map[asset.filename] = get_asset_path(
-                        entry.template_id, asset.filename
-                    )
-        except Exception as exc:
-            logging.error(f"Failed to resolve template asset paths: {exc}")
-            return None
-
-        if not asset_map:
-            logging.error("No workflow template assets found. Did the packages install correctly?")
-            return None
-
-        return asset_map
-
-
-    @classmethod
-    def legacy_templates_path(cls) -> Optional[str]:
-        """Return the legacy templates directory shipped inside the meta package."""
+    def templates_path(cls) -> str:
         try:
             import comfyui_workflow_templates
 
@@ -324,7 +243,6 @@ comfyui-workflow-templates is not installed.
 ********** ERROR ***********
 """.strip()
             )
-            return None
 
     @classmethod
     def embedded_docs_path(cls) -> str:
@@ -441,17 +359,3 @@ comfyui-workflow-templates is not installed.
             logging.info("Falling back to the default frontend.")
             check_frontend_version()
             return cls.default_frontend_path()
-    @classmethod
-    def template_asset_handler(cls):
-        assets = cls.template_asset_map()
-        if not assets:
-            return None
-
-        async def serve_template(request: web.Request) -> web.StreamResponse:
-            rel_path = request.match_info.get("path", "")
-            target = assets.get(rel_path)
-            if target is None:
-                raise web.HTTPNotFound()
-            return web.FileResponse(target)
-
-        return serve_template

app/model_manager.py

@@ -44,7 +44,7 @@ class ModelFileManager:
         @routes.get("/experiment/models/{folder}")
         async def get_all_models(request):
             folder = request.match_info.get("folder", None)
-            if folder not in folder_paths.folder_names_and_paths:
+            if not folder in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = self.get_model_file_list(folder)
             return web.json_response(files)
@@ -55,7 +55,7 @@ class ModelFileManager:
             path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
-            if folder_name not in folder_paths.folder_names_and_paths:
+            if not folder_name in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
             folders = folder_paths.folder_names_and_paths[folder_name]
@@ -130,21 +130,10 @@ class ModelFileManager:
 
             for file_name in filenames:
                 try:
-                    full_path = os.path.join(dirpath, file_name)
-                    relative_path = os.path.relpath(full_path, directory)
-
-                    # Get file metadata
-                    file_info = {
-                        "name": relative_path,
-                        "pathIndex": pathIndex,
-                        "modified": os.path.getmtime(full_path),  # Add modification time
-                        "created": os.path.getctime(full_path),   # Add creation time
-                        "size": os.path.getsize(full_path)        # Add file size
-                    }
-                    result.append(file_info)
-
-                except Exception as e:
-                    logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
+                    relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                    result.append(relative_path)
+                except:
+                    logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
                     continue
 
             for d in subdirs:
@@ -155,7 +144,7 @@ class ModelFileManager:
                     logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
                     continue
 
-        return result, dirs, time.perf_counter()
+        return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
 
     def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
         dirname = os.path.dirname(filepath)

app/subgraph_manager.py

@@ -1,132 +0,0 @@
-from __future__ import annotations
-
-from typing import TypedDict
-import os
-import folder_paths
-import glob
-from aiohttp import web
-import hashlib
-
-
-class Source:
-    custom_node = "custom_node"
-    templates = "templates"
-
-class SubgraphEntry(TypedDict):
-    source: str
-    """
-    Source of subgraph - custom_nodes vs templates.
-    """
-    path: str
-    """
-    Relative path of the subgraph file.
-    For custom nodes, will be the relative directory like <custom_node_dir>/subgraphs/<name>.json
-    """
-    name: str
-    """
-    Name of subgraph file.
-    """
-    info: CustomNodeSubgraphEntryInfo
-    """
-    Additional info about subgraph; in the case of custom_nodes, will contain nodepack name
-    """
-    data: str
-
-class CustomNodeSubgraphEntryInfo(TypedDict):
-    node_pack: str
-    """Node pack name."""
-
-class SubgraphManager:
-    def __init__(self):
-        self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
-        self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None
-
-    def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:
-        """Create a subgraph entry from a file path. Expects normalized path (forward slashes)."""
-        entry_id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
-        entry: SubgraphEntry = {
-            "source": source,
-            "name": os.path.splitext(os.path.basename(file))[0],
-            "path": file,
-            "info": {"node_pack": node_pack},
-        }
-        return entry_id, entry
-
-    async def load_entry_data(self, entry: SubgraphEntry):
-        with open(entry['path'], 'r') as f:
-            entry['data'] = f.read()
-        return entry
-
-    async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None:
-        if entry is None:
-            return None
-        entry = entry.copy()
-        entry.pop('path', None)
-        if remove_data:
-            entry.pop('data', None)
-        return entry
-
-    async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]:
-        entries = entries.copy()
-        for key in list(entries.keys()):
-            entries[key] = await self.sanitize_entry(entries[key], remove_data)
-        return entries
-
-    async def get_custom_node_subgraphs(self, loadedModules, force_reload=False):
-        """Load subgraphs from custom nodes."""
-        if not force_reload and self.cached_custom_node_subgraphs is not None:
-            return self.cached_custom_node_subgraphs
-
-        subgraphs_dict: dict[SubgraphEntry] = {}
-        for folder in folder_paths.get_folder_paths("custom_nodes"):
-            pattern = os.path.join(folder, "*/subgraphs/*.json")
-            for file in glob.glob(pattern):
-                file = file.replace('\\', '/')
-                node_pack = "custom_nodes." + file.split('/')[-3]
-                entry_id, entry = self._create_entry(file, Source.custom_node, node_pack)
-                subgraphs_dict[entry_id] = entry
-
-        self.cached_custom_node_subgraphs = subgraphs_dict
-        return subgraphs_dict
-
-    async def get_blueprint_subgraphs(self, force_reload=False):
-        """Load subgraphs from the blueprints directory."""
-        if not force_reload and self.cached_blueprint_subgraphs is not None:
-            return self.cached_blueprint_subgraphs
-
-        subgraphs_dict: dict[SubgraphEntry] = {}
-        blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
-
-        if os.path.exists(blueprints_dir):
-            for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
-                file = file.replace('\\', '/')
-                entry_id, entry = self._create_entry(file, Source.templates, "comfyui")
-                subgraphs_dict[entry_id] = entry
-
-        self.cached_blueprint_subgraphs = subgraphs_dict
-        return subgraphs_dict
-
-    async def get_all_subgraphs(self, loadedModules, force_reload=False):
-        """Get all subgraphs from all sources (custom nodes and blueprints)."""
-        custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload)
-        blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload)
-        return {**custom_node_subgraphs, **blueprint_subgraphs}
-
-    async def get_subgraph(self, id: str, loadedModules):
-        """Get a specific subgraph by ID from any source."""
-        entry = (await self.get_all_subgraphs(loadedModules)).get(id)
-        if entry is not None and entry.get('data') is None:
-            await self.load_entry_data(entry)
-        return entry
-
-    def add_routes(self, routes, loadedModules):
-        @routes.get("/global_subgraphs")
-        async def get_global_subgraphs(request):
-            subgraphs_dict = await self.get_all_subgraphs(loadedModules)
-            return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True))
-
-        @routes.get("/global_subgraphs/{id}")
-        async def get_global_subgraph(request):
-            id = request.match_info.get("id", None)
-            subgraph = await self.get_subgraph(id, loadedModules)
-            return web.json_response(await self.sanitize_entry(subgraph))

app/user_manager.py

@@ -20,15 +20,13 @@ class FileInfo(TypedDict):
     path: str
     size: int
     modified: int
-    created: int
 
 
 def get_file_info(path: str, relative_to: str) -> FileInfo:
     return {
         "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
         "size": os.path.getsize(path),
-        "modified": os.path.getmtime(path),
-        "created": os.path.getctime(path)
+        "modified": os.path.getmtime(path)
     }
 
 
@@ -59,9 +57,6 @@ class UserManager():
         user = "default"
         if args.multi_user and "comfy-user" in request.headers:
             user = request.headers["comfy-user"]
-            # Block System Users (use same error message to prevent probing)
-            if user.startswith(folder_paths.SYSTEM_USER_PREFIX):
-                raise KeyError("Unknown user: " + user)
 
         if user not in self.users:
             raise KeyError("Unknown user: " + user)
@@ -69,16 +64,15 @@ class UserManager():
         return user
 
     def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
+        user_directory = folder_paths.get_user_directory()
+
         if type == "userdata":
-            root_dir = folder_paths.get_user_directory()
+            root_dir = user_directory
         else:
             raise KeyError("Unknown filepath type:" + type)
 
         user = self.get_request_user_id(request)
-        user_root = folder_paths.get_public_user_directory(user)
-        if user_root is None:
-            return None
-        path = user_root
+        path = user_root = os.path.abspath(os.path.join(root_dir, user))
 
         # prevent leaving /{type}
         if os.path.commonpath((root_dir, user_root)) != root_dir:
@@ -105,11 +99,7 @@ class UserManager():
         name = name.strip()
         if not name:
             raise ValueError("username not provided")
-        if name.startswith(folder_paths.SYSTEM_USER_PREFIX):
-            raise ValueError("System User prefix not allowed")
         user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
-        if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX):
-            raise ValueError("System User prefix not allowed")
         user_id = user_id + "_" + str(uuid.uuid4())
 
         self.users[user_id] = name
@@ -140,10 +130,7 @@ class UserManager():
             if username in self.users.values():
                 return web.json_response({"error": "Duplicate username."}, status=400)
 
-            try:
-                user_id = self.add_user(username)
-            except ValueError as e:
-                return web.json_response({"error": str(e)}, status=400)
+            user_id = self.add_user(username)
             return web.json_response(user_id)
 
         @routes.get("/userdata")
@@ -374,17 +361,10 @@ class UserManager():
             if not overwrite and os.path.exists(path):
                 return web.Response(status=409, text="File already exists")
 
-            try:
-                body = await request.read()
+            body = await request.read()
 
-                with open(path, "wb") as f:
-                    f.write(body)
-            except OSError as e:
-                logging.warning(f"Error saving file '{path}': {e}")
-                return web.Response(
-                    status=400,
-                    reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|"
-                )
+            with open(path, "wb") as f:
+                f.write(body)
 
             user_path = self.get_request_user_filepath(request, None)
             if full_info:
@@ -435,7 +415,7 @@ class UserManager():
                 return source
 
             dest = get_user_data_path(request, check_exists=False, param="dest")
-            if not isinstance(dest, str):
+            if not isinstance(source, str):
                 return dest
 
             overwrite = request.query.get("overwrite", 'true') != "false"

comfy/audio_encoders/audio_encoders.py

@@ -1,91 +0,0 @@
-from .wav2vec2 import Wav2Vec2Model
-from .whisper import WhisperLargeV3
-import comfy.model_management
-import comfy.ops
-import comfy.utils
-import logging
-import torchaudio
-
-
-class AudioEncoderModel():
-    def __init__(self, config):
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        model_type = config.pop("model_type")
-        model_config = dict(config)
-        model_config.update({
-            "dtype": self.dtype,
-            "device": offload_device,
-            "operations": comfy.ops.manual_cast
-        })
-
-        if model_type == "wav2vec2":
-            self.model = Wav2Vec2Model(**model_config)
-        elif model_type == "whisper3":
-            self.model = WhisperLargeV3(**model_config)
-        self.model.eval()
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-        self.model_sample_rate = 16000
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_audio(self, audio, sample_rate):
-        comfy.model_management.load_model_gpu(self.patcher)
-        audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)
-        out, all_layers = self.model(audio.to(self.load_device))
-        outputs = {}
-        outputs["encoded_audio"] = out
-        outputs["encoded_audio_all_layers"] = all_layers
-        outputs["audio_samples"] = audio.shape[2]
-        return outputs
-
-
-def load_audio_encoder_from_sd(sd, prefix=""):
-    sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
-    if "encoder.layer_norm.bias" in sd: #wav2vec2
-        embed_dim = sd["encoder.layer_norm.bias"].shape[0]
-        if embed_dim == 1024:# large
-            config = {
-                "model_type": "wav2vec2",
-                "embed_dim": 1024,
-                "num_heads": 16,
-                "num_layers": 24,
-                "conv_norm": True,
-                "conv_bias": True,
-                "do_normalize": True,
-                "do_stable_layer_norm": True
-                }
-        elif embed_dim == 768: # base
-            config = {
-                "model_type": "wav2vec2",
-                "embed_dim": 768,
-                "num_heads": 12,
-                "num_layers": 12,
-                "conv_norm": False,
-                "conv_bias": False,
-                "do_normalize": False, # chinese-wav2vec2-base has this False
-                "do_stable_layer_norm": False
-            }
-        else:
-            raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
-    elif "model.encoder.embed_positions.weight" in sd:
-        sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""})
-        config = {
-            "model_type": "whisper3",
-        }
-    else:
-        raise RuntimeError("ERROR: audio encoder not supported.")
-
-    audio_encoder = AudioEncoderModel(config)
-    m, u = audio_encoder.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing audio encoder: {}".format(m))
-    if len(u) > 0:
-        logging.warning("unexpected audio encoder: {}".format(u))
-
-    return audio_encoder

comfy/audio_encoders/wav2vec2.py

@@ -1,252 +0,0 @@
-import torch
-import torch.nn as nn
-from comfy.ldm.modules.attention import optimized_attention_masked
-
-
-class LayerNormConv(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-        self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))
-
-class LayerGroupNormConv(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-        self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(self.layer_norm(x))
-
-class ConvNoNorm(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(x)
-
-
-class ConvFeatureEncoder(nn.Module):
-    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        if conv_norm:
-            self.conv_layers = nn.ModuleList([
-                LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-            ])
-        else:
-            self.conv_layers = nn.ModuleList([
-                LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-            ])
-
-    def forward(self, x):
-        x = x.unsqueeze(1)
-
-        for conv in self.conv_layers:
-            x = conv(x)
-
-        return x.transpose(1, 2)
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype)
-        self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.layer_norm(x)
-        x = self.projection(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self, embed_dim=768, kernel_size=128, groups=16):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            embed_dim,
-            embed_dim,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-            groups=groups,
-        )
-        self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)
-        self.activation = nn.GELU()
-
-    def forward(self, x):
-        x = x.transpose(1, 2)
-        x = self.conv(x)[:, :, :-1]
-        x = self.activation(x)
-        x = x.transpose(1, 2)
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        embed_dim=768,
-        num_heads=12,
-        num_layers=12,
-        mlp_ratio=4.0,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim)
-        self.layers = nn.ModuleList([
-            TransformerEncoderLayer(
-                embed_dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                do_stable_layer_norm=do_stable_layer_norm,
-                device=device, dtype=dtype, operations=operations
-            )
-            for _ in range(num_layers)
-        ])
-
-        self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
-        self.do_stable_layer_norm = do_stable_layer_norm
-
-    def forward(self, x, mask=None):
-        x = x + self.pos_conv_embed(x)
-        all_x = ()
-        if not self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        for layer in self.layers:
-            all_x += (x,)
-            x = layer(x, mask)
-        if self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        all_x += (x,)
-        return x, all_x
-
-
-class Attention(nn.Module):
-    def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim = embed_dim // num_heads
-
-        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x, mask=None):
-        assert (mask is None)  # TODO?
-        q = self.q_proj(x)
-        k = self.k_proj(x)
-        v = self.v_proj(x)
-
-        out = optimized_attention_masked(q, k, v, self.num_heads)
-        return self.out_proj(out)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype)
-        self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.intermediate_dense(x)
-        x = torch.nn.functional.gelu(x)
-        x = self.output_dense(x)
-        return x
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        embed_dim=768,
-        num_heads=12,
-        mlp_ratio=4.0,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations)
-
-        self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
-        self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
-        self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
-        self.do_stable_layer_norm = do_stable_layer_norm
-
-    def forward(self, x, mask=None):
-        residual = x
-        if self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        x = self.attention(x, mask=mask)
-        x = residual + x
-        if not self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-            return self.final_layer_norm(x + self.feed_forward(x))
-        else:
-            return x + self.feed_forward(self.final_layer_norm(x))
-
-
-class Wav2Vec2Model(nn.Module):
-    """Complete Wav2Vec 2.0 model."""
-
-    def __init__(
-        self,
-        embed_dim=1024,
-        final_dim=256,
-        num_heads=16,
-        num_layers=24,
-        conv_norm=True,
-        conv_bias=True,
-        do_normalize=True,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        conv_dim = 512
-        self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)
-        self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)
-
-        self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
-        self.do_normalize = do_normalize
-
-        self.encoder = TransformerEncoder(
-            embed_dim=embed_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            do_stable_layer_norm=do_stable_layer_norm,
-            device=device, dtype=dtype, operations=operations
-        )
-
-    def forward(self, x, mask_time_indices=None, return_dict=False):
-        x = torch.mean(x, dim=1)
-
-        if self.do_normalize:
-            x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
-
-        features = self.feature_extractor(x)
-        features = self.feature_projection(features)
-        batch_size, seq_len, _ = features.shape
-
-        x, all_x = self.encoder(features)
-        return x, all_x

comfy/audio_encoders/whisper.py

@@ -1,186 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchaudio
-from typing import Optional
-from comfy.ldm.modules.attention import optimized_attention_masked
-import comfy.ops
-
-class WhisperFeatureExtractor(nn.Module):
-    def __init__(self, n_mels=128, device=None):
-        super().__init__()
-        self.sample_rate = 16000
-        self.n_fft = 400
-        self.hop_length = 160
-        self.n_mels = n_mels
-        self.chunk_length = 30
-        self.n_samples = 480000
-
-        self.mel_spectrogram = torchaudio.transforms.MelSpectrogram(
-            sample_rate=self.sample_rate,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            n_mels=self.n_mels,
-            f_min=0,
-            f_max=8000,
-            norm="slaney",
-            mel_scale="slaney",
-        ).to(device)
-
-    def __call__(self, audio):
-        audio = torch.mean(audio, dim=1)
-        batch_size = audio.shape[0]
-        processed_audio = []
-
-        for i in range(batch_size):
-            aud = audio[i]
-            if aud.shape[0] > self.n_samples:
-                aud = aud[:self.n_samples]
-            elif aud.shape[0] < self.n_samples:
-                aud = F.pad(aud, (0, self.n_samples - aud.shape[0]))
-            processed_audio.append(aud)
-
-        audio = torch.stack(processed_audio)
-
-        mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device)
-
-        log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10()
-        log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0)
-        log_mel_spec = (log_mel_spec + 4.0) / 4.0
-
-        return log_mel_spec
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        assert d_model % n_heads == 0
-
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.d_k = d_model // n_heads
-
-        self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-        self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device)
-        self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-        self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        batch_size, seq_len, _ = query.shape
-
-        q = self.q_proj(query)
-        k = self.k_proj(key)
-        v = self.v_proj(value)
-
-        attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask)
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None):
-        super().__init__()
-
-        self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations)
-        self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
-
-        self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device)
-        self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device)
-        self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        residual = x
-        x = self.self_attn_layer_norm(x)
-        x = self.self_attn(x, x, x, attention_mask)
-        x = residual + x
-
-        residual = x
-        x = self.final_layer_norm(x)
-        x = self.fc1(x)
-        x = F.gelu(x)
-        x = self.fc2(x)
-        x = residual + x
-
-        return x
-
-
-class AudioEncoder(nn.Module):
-    def __init__(
-        self,
-        n_mels: int = 128,
-        n_ctx: int = 1500,
-        n_state: int = 1280,
-        n_head: int = 20,
-        n_layer: int = 32,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-
-        self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device)
-        self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device)
-
-        self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device)
-
-        self.layers = nn.ModuleList([
-            EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations)
-            for _ in range(n_layer)
-        ])
-
-        self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = F.gelu(self.conv1(x))
-        x = F.gelu(self.conv2(x))
-
-        x = x.transpose(1, 2)
-
-        x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x)
-
-        all_x = ()
-        for layer in self.layers:
-            all_x += (x,)
-            x = layer(x)
-
-        x = self.layer_norm(x)
-        all_x += (x,)
-        return x, all_x
-
-
-class WhisperLargeV3(nn.Module):
-    def __init__(
-        self,
-        n_mels: int = 128,
-        n_audio_ctx: int = 1500,
-        n_audio_state: int = 1280,
-        n_audio_head: int = 20,
-        n_audio_layer: int = 32,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-
-        self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device)
-
-        self.encoder = AudioEncoder(
-            n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer,
-            dtype=dtype, device=device, operations=operations
-        )
-
-    def forward(self, audio):
-        mel = self.feature_extractor(audio)
-        x, all_x = self.encoder(mel)
-        return x, all_x

comfy/cldm/cldm.py

@@ -413,8 +413,7 @@ class ControlNet(nn.Module):
         out_middle = []
 
         if self.num_classes is not None:
-            if y is None:
-                raise ValueError("y is None, did you try using a controlnet for SDXL on SD1?")
+            assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)
 
         h = x

comfy/cli_args.py

@@ -97,13 +97,6 @@ class LatentPreviewMethod(enum.Enum):
     Latent2RGB = "latent2rgb"
     TAESD = "taesd"
 
-    @classmethod
-    def from_string(cls, value: str):
-        for member in cls:
-            if member.value == value:
-                return member
-        return None
-
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
@@ -112,7 +105,6 @@ cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -128,12 +120,6 @@ upcast.add_argument("--force-upcast-attention", action="store_true", help="Force
 upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
 
 
-parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
-manager_group = parser.add_mutually_exclusive_group()
-manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
-manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
-
-
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
@@ -144,10 +130,7 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e
 
 parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
 
-parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
-parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
-
-parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
+parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
 
 parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
 
@@ -158,12 +141,8 @@ class PerformanceFeature(enum.Enum):
     Fp16Accumulation = "fp16_accumulation"
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
-    AutoTune = "autotune"
-    DynamicVRAM = "dynamic_vram"
 
-parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
-
-parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
 
 parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
 parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
@@ -175,14 +154,13 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
 parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
 parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.")
-parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes. Also prevents the frontend from communicating with the internet.")
+parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
 parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
 parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
 
-
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
 
@@ -232,7 +210,6 @@ database_default_path = os.path.abspath(
     os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-parser.add_argument("--disable-assets-autoscan", action="store_true", help="Disable asset scanning on startup for database synchronization.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
@@ -258,6 +235,3 @@ elif args.fast == []:
 # '--fast' is provided with a list of performance features, use that list
 else:
     args.fast = set(args.fast)
-
-def enables_dynamic_vram():
-    return PerformanceFeature.DynamicVRAM in args.fast and not args.highvram and not args.gpu_only

comfy/clip_model.py

@@ -1,59 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
-import math
-
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
-
-def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, eps=1e-5):
-    def scale_dim(size, scale):
-        scaled = math.ceil(size * scale / patch_size) * patch_size
-        return max(patch_size, int(scaled))
-
-    # Binary search for optimal scale
-    lo, hi = eps / 10, 100.0
-    while hi - lo >= eps:
-        mid = (lo + hi) / 2
-        h, w = scale_dim(oh, mid), scale_dim(ow, mid)
-        if (h // patch_size) * (w // patch_size) <= max_num_patches:
-            lo = mid
-        else:
-            hi = mid
-
-    return scale_dim(oh, lo), scale_dim(ow, lo)
-
-def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True):
-    if size > 0:
-        return clip_preprocess(image, size=size, mean=mean, std=std, crop=crop)
-
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-
-    b, c, h, w = image.shape
-    h, w = siglip2_flex_calc_resolution(h, w, patch_size, num_patches)
-
-    image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear", antialias=True)
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3, 1, 1])) / std.view([3, 1, 1])
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -114,12 +61,8 @@ class CLIPEncoder(torch.nn.Module):
     def forward(self, x, mask=None, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
-        all_intermediate = None
         if intermediate_output is not None:
-            if intermediate_output == "all":
-                all_intermediate = []
-                intermediate_output = None
-            elif intermediate_output < 0:
+            if intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
         intermediate = None
@@ -127,12 +70,6 @@ class CLIPEncoder(torch.nn.Module):
             x = l(x, mask, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
-            if all_intermediate is not None:
-                all_intermediate.append(x.unsqueeze(1).clone())
-
-        if all_intermediate is not None:
-            intermediate = torch.cat(all_intermediate, dim=1)
-
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
@@ -160,7 +97,7 @@ class CLIPTextModel_(torch.nn.Module):
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]):
+    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
         if embeds is not None:
             x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device)
         else:
@@ -209,27 +146,6 @@ class CLIPTextModel(torch.nn.Module):
         out = self.text_projection(x[2])
         return (x[0], x[1], out, x[2])
 
-def siglip2_pos_embed(embed_weight, embeds, orig_shape):
-    embed_weight_len = round(embed_weight.shape[0] ** 0.5)
-    embed_weight = comfy.ops.cast_to_input(embed_weight, embeds).movedim(1, 0).reshape(1, -1, embed_weight_len, embed_weight_len)
-    embed_weight = torch.nn.functional.interpolate(embed_weight, size=orig_shape, mode="bilinear", align_corners=False, antialias=True)
-    embed_weight = embed_weight.reshape(-1, embed_weight.shape[-2] * embed_weight.shape[-1]).movedim(0, 1)
-    return embeds + embed_weight
-
-class Siglip2Embeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", num_patches=None, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.patch_embedding = operations.Linear(num_channels * patch_size * patch_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
-        self.patch_size = patch_size
-
-    def forward(self, pixel_values):
-        b, c, h, w = pixel_values.shape
-        img = pixel_values.movedim(1, -1).reshape(b, h // self.patch_size, self.patch_size, w // self.patch_size, self.patch_size, c)
-        img = img.permute(0, 1, 3, 2, 4, 5)
-        img = img.reshape(b, img.shape[1] * img.shape[2], -1)
-        img = self.patch_embedding(img)
-        return siglip2_pos_embed(self.position_embedding.weight, img, (h // self.patch_size, w // self.patch_size))
 
 class CLIPVisionEmbeddings(torch.nn.Module):
     def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
@@ -273,11 +189,8 @@ class CLIPVision(torch.nn.Module):
         intermediate_activation = config_dict["hidden_act"]
         model_type = config_dict["model_type"]
 
-        if model_type in ["siglip2_vision_model"]:
-            self.embeddings = Siglip2Embeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, num_patches=config_dict.get("num_patches", None), dtype=dtype, device=device, operations=operations)
-        else:
-            self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
-        if model_type in ["siglip_vision_model", "siglip2_vision_model"]:
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
+        if model_type == "siglip_vision_model":
             self.pre_layrnorm = lambda a: a
             self.output_layernorm = True
         else:

comfy/clip_vision.py

@@ -1,5 +1,6 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
+import torch
 import json
 import logging
 
@@ -16,12 +17,28 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
 
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
-    "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "dinov2": comfy.image_encoders.dino2.Dinov2Model,
 }
 
@@ -33,47 +50,30 @@ class ClipVisionModel():
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
-        self.model_type = config.get("model_type", "clip_vision_model")
-        self.config = config.copy()
-        model_class = IMAGE_ENCODERS.get(self.model_type)
-        if self.model_type == "siglip_vision_model":
-            self.return_all_hidden_states = True
-        else:
-            self.return_all_hidden_states = False
-
+        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
         self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
         self.model.eval()
 
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
 
     def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
+        return self.model.load_state_dict(sd, strict=False)
 
     def get_sd(self):
         return self.model.state_dict()
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        if self.model_type == "siglip2_vision_model":
-            pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()
-        else:
-            pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
         outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        outputs["image_sizes"] = [pixel_values.shape[1:]] * pixel_values.shape[0]
-        if self.return_all_hidden_states:
-            all_hs = out[1].to(comfy.model_management.intermediate_device())
-            outputs["penultimate_hidden_states"] = all_hs[:, -2]
-            outputs["all_hidden_states"] = all_hs
-        else:
-            outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
-
+        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
         outputs["mm_projected"] = out[3]
         return outputs
 
@@ -113,14 +113,10 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
         embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0]
         if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
-            patch_embedding_shape = sd["vision_model.embeddings.patch_embedding.weight"].shape
-            if len(patch_embedding_shape) == 2:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip2_base_naflex.json")
-            else:
-                if embed_shape == 729:
-                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
-                elif embed_shape == 1024:
-                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
+            if embed_shape == 729:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+            elif embed_shape == 1024:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
         elif embed_shape == 577:
             if "multi_modal_projector.linear_1.bias" in sd:
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json")
@@ -128,12 +124,8 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
-
-    # Dinov2
-    elif 'encoder.layer.39.layer_scale2.lambda1' in sd:
+    elif "embeddings.patch_embeddings.projection.weight" in sd:
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
-    elif 'encoder.layer.23.layer_scale2.lambda1' in sd:
-        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json")
     else:
         return None
 

comfy/clip_vision_siglip2_base_naflex.json

@@ -1,14 +0,0 @@
-{
-  "num_channels": 3,
-  "hidden_act": "gelu_pytorch_tanh",
-  "hidden_size": 1152,
-  "image_size": -1,
-  "intermediate_size": 4304,
-  "model_type": "siglip2_vision_model",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 27,
-  "patch_size": 16,
-  "num_patches": 256,
-  "image_mean": [0.5, 0.5, 0.5],
-  "image_std": [0.5, 0.5, 0.5]
-}

comfy/comfy_types/node_typing.py

@@ -236,8 +236,6 @@ class ComfyNodeABC(ABC):
     """Flags a node as experimental, informing users that it may change or not work as expected."""
     DEPRECATED: bool
     """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
-    DEV_ONLY: bool
-    """Flags a node as dev-only, hiding it from search/menus unless dev mode is enabled."""
     API_NODE: Optional[bool]
     """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
 

comfy/conds.py

@@ -1,7 +1,6 @@
 import torch
 import math
 import comfy.utils
-import logging
 
 
 class CONDRegular:
@@ -11,15 +10,12 @@ class CONDRegular:
     def _copy_with(self, cond):
         return self.__class__(cond)
 
-    def process_cond(self, batch_size, **kwargs):
-        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))
+    def process_cond(self, batch_size, device, **kwargs):
+        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
 
     def can_concat(self, other):
         if self.cond.shape != other.cond.shape:
             return False
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device, skipping concat.")
-            return False
         return True
 
     def concat(self, others):
@@ -33,14 +29,14 @@ class CONDRegular:
 
 
 class CONDNoiseShape(CONDRegular):
-    def process_cond(self, batch_size, area, **kwargs):
+    def process_cond(self, batch_size, device, area, **kwargs):
         data = self.cond
         if area is not None:
             dims = len(area) // 2
             for i in range(dims):
                 data = data.narrow(i + 2, area[i + dims], area[i])
 
-        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))
+        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
 
 
 class CONDCrossAttn(CONDRegular):
@@ -55,9 +51,6 @@ class CONDCrossAttn(CONDRegular):
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device: skipping concat.")
-            return False
         return True
 
     def concat(self, others):
@@ -80,7 +73,7 @@ class CONDConstant(CONDRegular):
     def __init__(self, cond):
         self.cond = cond
 
-    def process_cond(self, batch_size, **kwargs):
+    def process_cond(self, batch_size, device, **kwargs):
         return self._copy_with(self.cond)
 
     def can_concat(self, other):
@@ -99,10 +92,10 @@ class CONDList(CONDRegular):
     def __init__(self, cond):
         self.cond = cond
 
-    def process_cond(self, batch_size, **kwargs):
+    def process_cond(self, batch_size, device, **kwargs):
         out = []
         for c in self.cond:
-            out.append(comfy.utils.repeat_to_batch_size(c, batch_size))
+            out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
 
         return self._copy_with(out)
 

comfy/context_windows.py

@@ -1,635 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, Callable
-import torch
-import numpy as np
-import collections
-from dataclasses import dataclass
-from abc import ABC, abstractmethod
-import logging
-import comfy.model_management
-import comfy.patcher_extension
-if TYPE_CHECKING:
-    from comfy.model_base import BaseModel
-    from comfy.model_patcher import ModelPatcher
-    from comfy.controlnet import ControlBase
-
-
-class ContextWindowABC(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def get_tensor(self, full: torch.Tensor) -> torch.Tensor:
-        """
-        Get torch.Tensor applicable to current window.
-        """
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor:
-        """
-        Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy.
-        """
-        raise NotImplementedError("Not implemented.")
-
-class ContextHandlerABC(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list:
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
-        raise NotImplementedError("Not implemented.")
-
-
-
-class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):
-        self.index_list = index_list
-        self.context_length = len(index_list)
-        self.dim = dim
-        self.total_frames = total_frames
-        self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
-
-    def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
-        if dim is None:
-            dim = self.dim
-        if dim == 0 and full.shape[dim] == 1:
-            return full
-        idx = tuple([slice(None)] * dim + [self.index_list])
-        window = full[idx]
-        if retain_index_list:
-            idx = tuple([slice(None)] * dim + [retain_index_list])
-            window[idx] = full[idx]
-        return window.to(device)
-
-    def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
-        if dim is None:
-            dim = self.dim
-        idx = tuple([slice(None)] * dim + [self.index_list])
-        full[idx] += to_add
-        return full
-
-    def get_region_index(self, num_regions: int) -> int:
-        region_idx = int(self.center_ratio * num_regions)
-        return min(max(region_idx, 0), num_regions - 1)
-
-
-class IndexListCallbacks:
-    EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
-    COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results"
-    EXECUTE_START = "execute_start"
-    EXECUTE_CLEANUP = "execute_cleanup"
-    RESIZE_COND_ITEM = "resize_cond_item"
-
-    def init_callbacks(self):
-        return {}
-
-
-@dataclass
-class ContextSchedule:
-    name: str
-    func: Callable
-
-@dataclass
-class ContextFuseMethod:
-    name: str
-    func: Callable
-
-ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
-class IndexListContextHandler(ContextHandlerABC):
-    def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
-                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
-        self.context_schedule = context_schedule
-        self.fuse_method = fuse_method
-        self.context_length = context_length
-        self.context_overlap = context_overlap
-        self.context_stride = context_stride
-        self.closed_loop = closed_loop
-        self.dim = dim
-        self._step = 0
-        self.freenoise = freenoise
-        self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
-        self.split_conds_to_windows = split_conds_to_windows
-
-        self.callbacks = {}
-
-    def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
-        # for now, assume first dim is batch - should have stored on BaseModel in actual implementation
-        if x_in.size(self.dim) > self.context_length:
-            logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
-            if self.cond_retain_index_list:
-                logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
-            return True
-        return False
-
-    def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase:
-        if control.previous_controlnet is not None:
-            self.prepare_control_objects(control.previous_controlnet, device)
-        return control
-
-    def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list:
-        if cond_in is None:
-            return None
-        # reuse or resize cond items to match context requirements
-        resized_cond = []
-        # if multiple conds, split based on primary region
-        if self.split_conds_to_windows and len(cond_in) > 1:
-            region = window.get_region_index(len(cond_in))
-            logging.info(f"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}")
-            cond_in = [cond_in[region]]
-        # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
-        for actual_cond in cond_in:
-            resized_actual_cond = actual_cond.copy()
-            # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary
-            for key in actual_cond:
-                try:
-                    cond_item = actual_cond[key]
-                    if isinstance(cond_item, torch.Tensor):
-                        # check that tensor is the expected length - x.size(0)
-                        if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim):
-                            # if so, it's subsetting time - tell controls the expected indeces so they can handle them
-                            actual_cond_item = window.get_tensor(cond_item)
-                            resized_actual_cond[key] = actual_cond_item.to(device)
-                        else:
-                            resized_actual_cond[key] = cond_item.to(device)
-                    # look for control
-                    elif key == "control":
-                        resized_actual_cond[key] = self.prepare_control_objects(cond_item, device)
-                    elif isinstance(cond_item, dict):
-                        new_cond_item = cond_item.copy()
-                        # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
-                        for cond_key, cond_value in new_cond_item.items():
-                            # Allow callbacks to handle custom conditioning items
-                            handled = False
-                            for callback in comfy.patcher_extension.get_all_callbacks(
-                                IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks
-                            ):
-                                result = callback(cond_key, cond_value, window, x_in, device, new_cond_item)
-                                if result is not None:
-                                    new_cond_item[cond_key] = result
-                                    handled = True
-                                    break
-                            if handled:
-                                continue
-                            if isinstance(cond_value, torch.Tensor):
-                                if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
-                                   (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
-                                    new_cond_item[cond_key] = window.get_tensor(cond_value, device)
-                            # Handle audio_embed (temporal dim is 1)
-                            elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                audio_cond = cond_value.cond
-                                if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
-                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
-                            # Handle vace_context (temporal dim is 3)
-                            elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                vace_cond = cond_value.cond
-                                if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
-                                    sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
-                                    new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
-                            # if has cond that is a Tensor, check if needs to be subset
-                            elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                if  (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \
-                                    (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):
-                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))
-                            elif cond_key == "num_video_frames": # for SVD
-                                new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
-                                new_cond_item[cond_key].cond = window.context_length
-                        resized_actual_cond[key] = new_cond_item
-                    else:
-                        resized_actual_cond[key] = cond_item
-                finally:
-                    del cond_item  # just in case to prevent VRAM issues
-            resized_cond.append(resized_actual_cond)
-        return resized_cond
-
-    def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
-        matches = torch.nonzero(mask)
-        if torch.numel(matches) == 0:
-            raise Exception("No sample_sigmas matched current timestep; something went wrong.")
-        self._step = int(matches[0].item())
-
-    def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
-        full_length = x_in.size(self.dim) # TODO: choose dim based on model
-        context_windows = self.context_schedule.func(full_length, self, model_options)
-        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]
-        return context_windows
-
-    def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
-        self.set_step(timestep, model_options)
-        context_windows = self.get_context_windows(model, x_in, model_options)
-        enumerated_context_windows = list(enumerate(context_windows))
-
-        conds_final = [torch.zeros_like(x_in) for _ in conds]
-        if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
-        else:
-            counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
-        biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds]
-
-        for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks):
-            callback(self, model, x_in, conds, timestep, model_options)
-
-        for enum_window in enumerated_context_windows:
-            results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options)
-            for result in results:
-                self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep,
-                                            conds_final, counts_final, biases_final)
-        try:
-            # finalize conds
-            if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-                # relative is already normalized, so return as is
-                del counts_final
-                return conds_final
-            else:
-                # normalize conds via division by context usage counts
-                for i in range(len(conds_final)):
-                    conds_final[i] /= counts_final[i]
-                del counts_final
-                return conds_final
-        finally:
-            for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks):
-                callback(self, model, x_in, conds, timestep, model_options)
-
-    def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],
-                                model_options, device=None, first_device=None):
-        results: list[ContextResults] = []
-        for window_idx, window in enumerated_context_windows:
-            # allow processing to end between context window executions for faster Cancel
-            comfy.model_management.throw_exception_if_processing_interrupted()
-
-            for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
-                callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
-
-            # update exposed params
-            model_options["transformer_options"]["context_window"] = window
-            # get subsections of x, timestep, conds
-            sub_x = window.get_tensor(x_in, device)
-            sub_timestep = window.get_tensor(timestep, device, dim=0)
-            sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds]
-
-            sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options)
-            if device is not None:
-                for i in range(len(sub_conds_out)):
-                    sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
-            results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
-        return results
-
-
-    def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor,
-                                    conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]):
-        if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            for pos, idx in enumerate(window.index_list):
-                # bias is the influence of a specific index in relation to the whole context window
-                bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2)
-                bias = max(1e-2, bias)
-                # take weighted average relative to total bias of current idx
-                for i in range(len(sub_conds_out)):
-                    bias_total = biases_final[i][idx]
-                    prev_weight = (bias_total / (bias_total + bias))
-                    new_weight = (bias / (bias_total + bias))
-                    # account for dims of tensors
-                    idx_window = tuple([slice(None)] * self.dim + [idx])
-                    pos_window = tuple([slice(None)] * self.dim + [pos])
-                    # apply new values
-                    conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
-                    biases_final[i][idx] = bias_total + bias
-        else:
-            # add conds and counts based on weights of fuse method
-            weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep)
-            weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device)
-            for i in range(len(sub_conds_out)):
-                window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor)
-                window.add_window(counts_final[i], weights_tensor)
-
-        for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks):
-            callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final)
-
-
-def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs):
-    # limit noise_shape length to context_length for more accurate vram use estimation
-    model_options = kwargs.get("model_options", None)
-    if model_options is None:
-        raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.")
-    handler: IndexListContextHandler = model_options.get("context_handler", None)
-    if handler is not None:
-        noise_shape = list(noise_shape)
-        noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
-    return executor(model, noise_shape, *args, **kwargs)
-
-
-def create_prepare_sampling_wrapper(model: ModelPatcher):
-    model.add_wrapper_with_key(
-        comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING,
-        "ContextWindows_prepare_sampling",
-        _prepare_sampling_wrapper
-    )
-
-
-def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):
-    model_options = extra_args.get("model_options", None)
-    if model_options is None:
-        raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.")
-    handler: IndexListContextHandler = model_options.get("context_handler", None)
-    if handler is None:
-        raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
-    if not handler.freenoise:
-        return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
-    noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"])
-
-    return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
-
-
-def create_sampler_sample_wrapper(model: ModelPatcher):
-    model.add_wrapper_with_key(
-        comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
-        "ContextWindows_sampler_sample",
-        _sampler_sample_wrapper
-    )
-
-
-def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
-    total_dims = len(x_in.shape)
-    weights_tensor = torch.Tensor(weights).to(device=device)
-    for _ in range(dim):
-        weights_tensor = weights_tensor.unsqueeze(0)
-    for _ in range(total_dims - dim - 1):
-        weights_tensor = weights_tensor.unsqueeze(-1)
-    return weights_tensor
-
-def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]:
-    total_dims = len(x_in.shape)
-    shape = []
-    for _ in range(dim):
-        shape.append(1)
-    shape.append(x_in.shape[dim])
-    for _ in range(total_dims - dim - 1):
-        shape.append(1)
-    return shape
-
-class ContextSchedules:
-    UNIFORM_LOOPED = "looped_uniform"
-    UNIFORM_STANDARD = "standard_uniform"
-    STATIC_STANDARD = "standard_static"
-    BATCHED = "batched"
-
-
-# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py
-def create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames < handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-
-    context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)
-    # obtain uniform windows as normal, looping and all
-    for context_step in 1 << np.arange(context_stride):
-        pad = int(round(num_frames * ordered_halving(handler._step)))
-        for j in range(
-            int(ordered_halving(handler._step) * context_step) + pad,
-            num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap),
-            (handler.context_length * context_step - handler.context_overlap),
-        ):
-            windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])
-
-    return windows
-
-def create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning;
-    # instead, they get shifted to the corresponding end of the frames.
-    # in the case that a window (shifted or not) is identical to the previous one, it gets skipped.
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-
-    context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)
-    # first, obtain uniform windows as normal, looping and all
-    for context_step in 1 << np.arange(context_stride):
-        pad = int(round(num_frames * ordered_halving(handler._step)))
-        for j in range(
-            int(ordered_halving(handler._step) * context_step) + pad,
-            num_frames + pad + (-handler.context_overlap),
-            (handler.context_length * context_step - handler.context_overlap),
-        ):
-            windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])
-
-    # now that windows are created, shift any windows that loop, and delete duplicate windows
-    delete_idxs = []
-    win_i = 0
-    while win_i < len(windows):
-        # if window is rolls over itself, need to shift it
-        is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames)
-        if is_roll:
-            roll_val = windows[win_i][roll_idx]  # roll_val might not be 0 for windows of higher strides
-            shift_window_to_end(windows[win_i], num_frames=num_frames)
-            # check if next window (cyclical) is missing roll_val
-            if roll_val not in windows[(win_i+1) % len(windows)]:
-                # need to insert new window here - just insert window starting at roll_val
-                windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length)))
-        # delete window if it's not unique
-        for pre_i in range(0, win_i):
-            if windows[win_i] == windows[pre_i]:
-                delete_idxs.append(win_i)
-                break
-        win_i += 1
-
-    # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation
-    delete_idxs.reverse()
-    for i in delete_idxs:
-        windows.pop(i)
-
-    return windows
-
-
-def create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-    # always return the same set of windows
-    delta = handler.context_length - handler.context_overlap
-    for start_idx in range(0, num_frames, delta):
-        # if past the end of frames, move start_idx back to allow same context_length
-        ending = start_idx + handler.context_length
-        if ending >= num_frames:
-            final_delta = ending - num_frames
-            final_start_idx = start_idx - final_delta
-            windows.append(list(range(final_start_idx, final_start_idx + handler.context_length)))
-            break
-        windows.append(list(range(start_idx, start_idx + handler.context_length)))
-    return windows
-
-
-def create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-    # always return the same set of windows;
-    # no overlap, just cut up based on context_length;
-    # last window size will be different if num_frames % opts.context_length != 0
-    for start_idx in range(0, num_frames, handler.context_length):
-        windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames))))
-    return windows
-
-
-def create_windows_default(num_frames: int, handler: IndexListContextHandler):
-    return [list(range(num_frames))]
-
-
-CONTEXT_MAPPING = {
-    ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped,
-    ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard,
-    ContextSchedules.STATIC_STANDARD: create_windows_static_standard,
-    ContextSchedules.BATCHED: create_windows_batched,
-}
-
-
-def get_matching_context_schedule(context_schedule: str) -> ContextSchedule:
-    func = CONTEXT_MAPPING.get(context_schedule, None)
-    if func is None:
-        raise ValueError(f"Unknown context_schedule '{context_schedule}'.")
-    return ContextSchedule(context_schedule, func)
-
-
-def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None):
-    return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs)
-
-
-def create_weights_flat(length: int, **kwargs) -> list[float]:
-    # weight is the same for all
-    return [1.0] * length
-
-def create_weights_pyramid(length: int, **kwargs) -> list[float]:
-    # weight is based on the distance away from the edge of the context window;
-    # based on weighted average concept in FreeNoise paper
-    if length % 2 == 0:
-        max_weight = length // 2
-        weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))
-    else:
-        max_weight = (length + 1) // 2
-        weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
-    return weight_sequence
-
-def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs):
-    # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302
-    # only expected overlap is given different weights
-    weights_torch = torch.ones((length))
-    # blend left-side on all except first window
-    if min(idxs) > 0:
-        ramp_up = torch.linspace(1e-37, 1, handler.context_overlap)
-        weights_torch[:handler.context_overlap] = ramp_up
-    # blend right-side on all except last window
-    if max(idxs) < full_length-1:
-        ramp_down = torch.linspace(1, 1e-37, handler.context_overlap)
-        weights_torch[-handler.context_overlap:] = ramp_down
-    return weights_torch
-
-class ContextFuseMethods:
-    FLAT = "flat"
-    PYRAMID = "pyramid"
-    RELATIVE = "relative"
-    OVERLAP_LINEAR = "overlap-linear"
-
-    LIST = [PYRAMID, FLAT, OVERLAP_LINEAR]
-    LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR]
-
-
-FUSE_MAPPING = {
-    ContextFuseMethods.FLAT: create_weights_flat,
-    ContextFuseMethods.PYRAMID: create_weights_pyramid,
-    ContextFuseMethods.RELATIVE: create_weights_pyramid,
-    ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear,
-}
-
-def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:
-    func = FUSE_MAPPING.get(fuse_method, None)
-    if func is None:
-        raise ValueError(f"Unknown fuse_method '{fuse_method}'.")
-    return ContextFuseMethod(fuse_method, func)
-
-# Returns fraction that has denominator that is a power of 2
-def ordered_halving(val):
-    # get binary value, padded with 0s for 64 bits
-    bin_str = f"{val:064b}"
-    # flip binary value, padding included
-    bin_flip = bin_str[::-1]
-    # convert binary to int
-    as_int = int(bin_flip, 2)
-    # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616,
-    # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's)
-    return as_int / (1 << 64)
-
-
-def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]:
-    all_indexes = list(range(num_frames))
-    for w in windows:
-        for val in w:
-            try:
-                all_indexes.remove(val)
-            except ValueError:
-                pass
-    return all_indexes
-
-
-def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]:
-    prev_val = -1
-    for i, val in enumerate(window):
-        val = val % num_frames
-        if val < prev_val:
-            return True, i
-        prev_val = val
-    return False, -1
-
-
-def shift_window_to_start(window: list[int], num_frames: int):
-    start_val = window[0]
-    for i in range(len(window)):
-        # 1) subtract each element by start_val to move vals relative to the start of all frames
-        # 2) add num_frames and take modulus to get adjusted vals
-        window[i] = ((window[i] - start_val) + num_frames) % num_frames
-
-
-def shift_window_to_end(window: list[int], num_frames: int):
-    # 1) shift window to start
-    shift_window_to_start(window, num_frames)
-    end_val = window[-1]
-    end_delta = num_frames - end_val - 1
-    for i in range(len(window)):
-        # 2) add end_delta to each val to slide windows to end
-        window[i] = window[i] + end_delta
-
-
-# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465
-def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):
-    logging.info("Context windows: Applying FreeNoise")
-    generator = torch.Generator(device='cpu').manual_seed(seed)
-    latent_video_length = noise.shape[dim]
-    delta = context_length - context_overlap
-
-    for start_idx in range(0, latent_video_length - context_length, delta):
-        place_idx = start_idx + context_length
-
-        actual_delta = min(delta, latent_video_length - place_idx)
-        if actual_delta <= 0:
-            break
-
-        list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx
-
-        source_slice = [slice(None)] * noise.ndim
-        source_slice[dim] = list_idx
-        target_slice = [slice(None)] * noise.ndim
-        target_slice[dim] = slice(place_idx, place_idx + actual_delta)
-
-        noise[tuple(target_slice)] = noise[tuple(source_slice)]
-
-    return noise

comfy/controlnet.py

@@ -28,7 +28,6 @@ import comfy.model_detection
 import comfy.model_patcher
 import comfy.ops
 import comfy.latent_formats
-import comfy.model_base
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
@@ -36,7 +35,6 @@ import comfy.ldm.cascade.controlnet
 import comfy.cldm.mmdit
 import comfy.ldm.hydit.controlnet
 import comfy.ldm.flux.controlnet
-import comfy.ldm.qwen_image.controlnet
 import comfy.cldm.dit_embedder
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
@@ -45,6 +43,7 @@ if TYPE_CHECKING:
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
+    #print(current_batch_size, target_batch_size)
     if current_batch_size == 1:
         return tensor
 
@@ -203,7 +202,7 @@ class ControlNet(ControlBase):
         self.control_model = control_model
         self.load_device = load_device
         if control_model is not None:
-            self.control_model_wrapped = comfy.model_patcher.CoreModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+            self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
 
         self.compression_ratio = compression_ratio
         self.global_average_pooling = global_average_pooling
@@ -237,11 +236,11 @@ class ControlNet(ControlBase):
             self.cond_hint = None
             compression_ratio = self.compression_ratio
             if self.vae is not None:
-                compression_ratio *= self.vae.spacial_compression_encode()
+                compression_ratio *= self.vae.downscale_ratio
             else:
                 if self.latent_format is not None:
                     raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center")
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
             self.cond_hint = self.preprocess_image(self.cond_hint)
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -253,10 +252,7 @@ class ControlNet(ControlBase):
                 to_concat = []
                 for c in self.extra_concat_orig:
                     c = c.to(self.cond_hint.device)
-                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, "center")
-                    if c.ndim < self.cond_hint.ndim:
-                        c = c.unsqueeze(2)
-                        c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2)
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
                     to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
                 self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
 
@@ -269,12 +265,12 @@ class ControlNet(ControlBase):
         for c in self.extra_conds:
             temp = cond.get(c, None)
             if temp is not None:
-                extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device)
+                extra[c] = temp.to(dtype)
 
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
-        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra)
+        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
         return self.control_merge(control, control_prev, output_dtype=None)
 
     def copy(self):
@@ -310,13 +306,11 @@ class ControlLoraOps:
             self.bias = None
 
         def forward(self, input):
-            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
+            weight, bias = comfy.ops.cast_bias_weight(self, input)
             if self.up is not None:
-                x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
+                return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
             else:
-                x = torch.nn.functional.linear(input, weight, bias)
-            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
-            return x
+                return torch.nn.functional.linear(input, weight, bias)
 
     class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(
@@ -352,13 +346,12 @@ class ControlLoraOps:
 
 
         def forward(self, input):
-            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
+            weight, bias = comfy.ops.cast_bias_weight(self, input)
             if self.up is not None:
-                x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
+                return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
             else:
-                x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
-            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
-            return x
+                return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
+
 
 class ControlLora(ControlNet):
     def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
@@ -589,22 +582,6 @@ def load_controlnet_flux_instantx(sd, model_options={}):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-def load_controlnet_qwen_instantx(sd, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
-    control_latent_channels = sd.get("controlnet_x_embedder.weight").shape[1]
-
-    extra_condition_channels = 0
-    concat_mask = False
-    if control_latent_channels == 68: #inpaint controlnet
-        extra_condition_channels = control_latent_channels - 64
-        concat_mask = True
-    control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(extra_condition_channels=extra_condition_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
-    control_model = controlnet_load_state_dict(control_model, sd)
-    latent_format = comfy.latent_formats.Wan21()
-    extra_conds = []
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
-    return control
-
 def convert_mistoline(sd):
     return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
 
@@ -678,11 +655,8 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
                 return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format
             else:
                 return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
-        elif "transformer_blocks.0.img_mlp.net.0.proj.weight" in controlnet_data:
-            return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options)
         elif "controlnet_x_embedder.weight" in controlnet_data:
             return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
-
     elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
         return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
 

comfy/float.py

@@ -65,147 +65,3 @@ def stochastic_rounding(value, dtype, seed=0):
         return output
 
     return value.to(dtype=dtype)
-
-
-# TODO: improve this?
-def stochastic_float_to_fp4_e2m1(x, generator):
-    orig_shape = x.shape
-    sign = torch.signbit(x).to(torch.uint8)
-
-    exp = torch.floor(torch.log2(x.abs()) + 1.0).clamp(0, 3)
-    x += (torch.rand(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - 0.5) * (2 ** (exp - 2.0)) * 1.25
-
-    x = x.abs()
-    exp = torch.floor(torch.log2(x) + 1.1925).clamp(0, 3)
-
-    mantissa = torch.where(
-        exp > 0,
-        (x / (2.0 ** (exp - 1)) - 1.0) * 2.0,
-        (x * 2.0),
-        out=x
-    ).round().to(torch.uint8)
-    del x
-
-    exp = exp.to(torch.uint8)
-
-    fp4 = (sign << 3) | (exp << 1) | mantissa
-    del sign, exp, mantissa
-
-    fp4_flat = fp4.view(-1)
-    packed = (fp4_flat[0::2] << 4) | fp4_flat[1::2]
-    return packed.reshape(list(orig_shape)[:-1] + [-1])
-
-
-def to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor:
-    """
-    Rearrange a large matrix by breaking it into blocks and applying the rearrangement pattern.
-    See:
-        https://docs.nvidia.com/cuda/cublas/index.html#d-block-scaling-factors-layout
-
-    Args:
-        input_matrix: Input tensor of shape (H, W)
-    Returns:
-        Rearranged tensor of shape (32*ceil_div(H,128), 16*ceil_div(W,4))
-    """
-
-    def ceil_div(a, b):
-        return (a + b - 1) // b
-
-    rows, cols = input_matrix.shape
-    n_row_blocks = ceil_div(rows, 128)
-    n_col_blocks = ceil_div(cols, 4)
-
-    # Calculate the padded shape
-    padded_rows = n_row_blocks * 128
-    padded_cols = n_col_blocks * 4
-
-    padded = input_matrix
-    if (rows, cols) != (padded_rows, padded_cols):
-        padded = torch.zeros(
-            (padded_rows, padded_cols),
-            device=input_matrix.device,
-            dtype=input_matrix.dtype,
-        )
-        padded[:rows, :cols] = input_matrix
-
-    # Rearrange the blocks
-    blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)
-    rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16)
-    if flatten:
-        return rearranged.flatten()
-
-    return rearranged.reshape(padded_rows, padded_cols)
-
-
-def stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator):
-    F4_E2M1_MAX = 6.0
-    F8_E4M3_MAX = 448.0
-
-    orig_shape = x.shape
-
-    block_size = 16
-
-    x = x.reshape(orig_shape[0], -1, block_size)
-    scaled_block_scales_fp8 = torch.clamp(((torch.amax(torch.abs(x), dim=-1)) / F4_E2M1_MAX) / per_tensor_scale.to(x.dtype), max=F8_E4M3_MAX).to(torch.float8_e4m3fn)
-    x = x / (per_tensor_scale.to(x.dtype) * scaled_block_scales_fp8.to(x.dtype)).unsqueeze(-1)
-
-    x = x.view(orig_shape).nan_to_num()
-    data_lp = stochastic_float_to_fp4_e2m1(x, generator=generator)
-    return data_lp, scaled_block_scales_fp8
-
-
-def stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0):
-    def roundup(x: int, multiple: int) -> int:
-        """Round up x to the nearest multiple."""
-        return ((x + multiple - 1) // multiple) * multiple
-
-    generator = torch.Generator(device=x.device)
-    generator.manual_seed(seed)
-
-    # Handle padding
-    if pad_16x:
-        rows, cols = x.shape
-        padded_rows = roundup(rows, 16)
-        padded_cols = roundup(cols, 16)
-        if padded_rows != rows or padded_cols != cols:
-            x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))
-
-    x, blocked_scaled = stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator)
-    return x, to_blocked(blocked_scaled, flatten=False)
-
-
-def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=0, block_size=4096 * 4096):
-    def roundup(x: int, multiple: int) -> int:
-        """Round up x to the nearest multiple."""
-        return ((x + multiple - 1) // multiple) * multiple
-
-    orig_shape = x.shape
-
-    # Handle padding
-    if pad_16x:
-        rows, cols = x.shape
-        padded_rows = roundup(rows, 16)
-        padded_cols = roundup(cols, 16)
-        if padded_rows != rows or padded_cols != cols:
-            x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))
-            # Note: We update orig_shape because the output tensor logic below assumes x.shape matches
-            # what we want to produce. If we pad here, we want the padded output.
-            orig_shape = x.shape
-
-    orig_shape = list(orig_shape)
-
-    output_fp4 = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 2], dtype=torch.uint8, device=x.device)
-    output_block = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 16], dtype=torch.float8_e4m3fn, device=x.device)
-
-    generator = torch.Generator(device=x.device)
-    generator.manual_seed(seed)
-
-    num_slices = max(1, (x.numel() / block_size))
-    slice_size = max(1, (round(x.shape[0] / num_slices)))
-
-    for i in range(0, x.shape[0], slice_size):
-        fp4, block = stochastic_round_quantize_nvfp4_block(x[i: i + slice_size], per_tensor_scale, generator=generator)
-        output_fp4[i:i + slice_size].copy_(fp4)
-        output_block[i:i + slice_size].copy_(block)
-
-    return output_fp4, to_blocked(output_block, flatten=False)

comfy/hooks.py

@@ -527,8 +527,7 @@ class HookKeyframeGroup:
                         if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
-                    else:
-                        break
+                    else: break
         # update steps current context is used
         self._current_used_steps += 1
         # update current timestep this was performed on

comfy/image_encoders/dino2.py

@@ -31,20 +31,6 @@ class LayerScale(torch.nn.Module):
     def forward(self, x):
         return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)
 
-class Dinov2MLP(torch.nn.Module):
-    def __init__(self, hidden_size: int, dtype, device, operations):
-        super().__init__()
-
-        mlp_ratio = 4
-        hidden_features = int(hidden_size * mlp_ratio)
-        self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype)
-        self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype)
-
-    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
-        hidden_state = self.fc1(hidden_state)
-        hidden_state = torch.nn.functional.gelu(hidden_state)
-        hidden_state = self.fc2(hidden_state)
-        return hidden_state
 
 class SwiGLUFFN(torch.nn.Module):
     def __init__(self, dim, dtype, device, operations):
@@ -64,15 +50,12 @@ class SwiGLUFFN(torch.nn.Module):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
         super().__init__()
         self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
         self.layer_scale1 = LayerScale(dim, dtype, device, operations)
         self.layer_scale2 = LayerScale(dim, dtype, device, operations)
-        if use_swiglu_ffn:
-            self.mlp = SwiGLUFFN(dim, dtype, device, operations)
-        else:
-            self.mlp = Dinov2MLP(dim, dtype, device, operations)
+        self.mlp = SwiGLUFFN(dim, dtype, device, operations)
         self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
         self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
@@ -83,10 +66,9 @@ class Dino2Block(torch.nn.Module):
 
 
 class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
-                                          for _ in range(num_layers)])
+        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
 
     def forward(self, x, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
@@ -96,8 +78,8 @@ class Dino2Encoder(torch.nn.Module):
                 intermediate_output = len(self.layer) + intermediate_output
 
         intermediate = None
-        for i, layer in enumerate(self.layer):
-            x = layer(x, optimized_attention)
+        for i, l in enumerate(self.layer):
+            x = l(x, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
         return x, intermediate
@@ -146,10 +128,9 @@ class Dinov2Model(torch.nn.Module):
         dim = config_dict["hidden_size"]
         heads = config_dict["num_attention_heads"]
         layer_norm_eps = config_dict["layer_norm_eps"]
-        use_swiglu_ffn = config_dict["use_swiglu_ffn"]
 
         self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
-        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
+        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations)
         self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
     def forward(self, pixel_values, attention_mask=None, intermediate_output=None):

comfy/image_encoders/dino2_large.json

@@ -1,22 +0,0 @@
-{
-  "hidden_size": 1024,
-  "use_mask_token": true,
-  "patch_size": 14,
-  "image_size": 518,
-  "num_channels": 3,
-  "num_attention_heads": 16,
-  "initializer_range": 0.02,
-  "attention_probs_dropout_prob": 0.0,
-  "hidden_dropout_prob": 0.0,
-  "hidden_act": "gelu",
-  "mlp_ratio": 4,
-  "model_type": "dinov2",
-  "num_hidden_layers": 24,
-  "layer_norm_eps": 1e-6,
-  "qkv_bias": true,
-  "use_swiglu_ffn": false,
-  "layerscale_value": 1.0,
-  "drop_path_rate": 0.0,
-  "image_mean": [0.485, 0.456, 0.406],
-  "image_std": [0.229, 0.224, 0.225]
-}

comfy/k_diffusion/sampling.py

@@ -1,12 +1,11 @@
 import math
-import time
 from functools import partial
 
 from scipy import integrate
 import torch
 from torch import nn
 import torchsde
-from tqdm.auto import trange as trange_, tqdm
+from tqdm.auto import trange, tqdm
 
 from . import utils
 from . import deis
@@ -14,36 +13,6 @@ from . import sa_solver
 import comfy.model_patcher
 import comfy.model_sampling
 
-import comfy.memory_management
-
-
-def trange(*args, **kwargs):
-    if comfy.memory_management.aimdo_allocator is None:
-        return trange_(*args, **kwargs)
-
-    pbar = trange_(*args, **kwargs, smoothing=1.0)
-    pbar._i = 0
-    pbar.set_postfix_str("  Model Initializing ...  ")
-
-    _update = pbar.update
-
-    def warmup_update(n=1):
-        pbar._i += 1
-        if pbar._i == 1:
-            pbar.i1_time = time.time()
-            pbar.set_postfix_str(" Model Initialization complete!  ")
-        elif pbar._i == 2:
-            #bring forward the effective start time based the the diff between first and second iteration
-            #to attempt to remove load overhead from the final step rate estimate.
-            pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
-            pbar.set_postfix_str("")
-
-        _update(n)
-
-    pbar.update = warmup_update
-    return pbar
-
-
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])
 
@@ -105,9 +74,6 @@ def get_ancestral_step(sigma_from, sigma_to, eta=1.):
 
 def default_noise_sampler(x, seed=None):
     if seed is not None:
-        if x.device == torch.device("cpu"):
-            seed += 1
-
         generator = torch.Generator(device=x.device)
         generator.manual_seed(seed)
     else:
@@ -120,24 +86,24 @@ class BatchedBrownianTree:
     """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
 
     def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = kwargs.pop("cpu", True)
+        self.cpu_tree = True
+        if "cpu" in kwargs:
+            self.cpu_tree = kwargs.pop("cpu")
         t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.pop('w0', None)
-        if w0 is None:
-            w0 = torch.zeros_like(x)
-        self.batched = False
+        w0 = kwargs.get('w0', torch.zeros_like(x))
         if seed is None:
-            seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),)
-        elif isinstance(seed, (tuple, list)):
-            if len(seed) != x.shape[0]:
-                raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.")
-            self.batched = True
+            seed = torch.randint(0, 2 ** 63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
             w0 = w0[0]
-        else:
-            seed = (seed,)
+        except TypeError:
+            seed = [seed]
+            self.batched = False
         if self.cpu_tree:
-            t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu()
-        self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed)
+            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
+        else:
+            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
 
     @staticmethod
     def sort(a, b):
@@ -145,10 +111,11 @@ class BatchedBrownianTree:
 
     def __call__(self, t0, t1):
         t0, t1, sign = self.sort(t0, t1)
-        device, dtype = t0.device, t0.dtype
         if self.cpu_tree:
-            t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float()
-        w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign)
+            w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
+        else:
+            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+
         return w if self.batched else w[0]
 
 
@@ -204,16 +171,6 @@ def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
     return sigmas
 
 
-def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:
-    """Compute the result of h*phi_1(h) in exponential integrator methods."""
-    return torch.expm1(h)
-
-
-def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:
-    """Compute the result of h*phi_2(h) in exponential integrator methods."""
-    return (torch.expm1(h) - h) / h
-
-
 @torch.no_grad()
 def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
@@ -896,11 +853,6 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     return x
 
 
-@torch.no_grad()
-def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
-    return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
-
-
 @torch.no_grad()
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""
@@ -973,16 +925,6 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
 
 
-@torch.no_grad()
-def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
-    if len(sigmas) <= 1:
-        return x
-    extra_args = {} if extra_args is None else extra_args
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
-    return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
-
-
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     if len(sigmas) <= 1:
@@ -1591,17 +1533,15 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
 
 
 @torch.no_grad()
-def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"):
+def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
     """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
-    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
+    arXiv: https://arxiv.org/abs/2305.14267
     """
-    if solver_type not in {"phi_1", "phi_2"}:
-        raise ValueError("solver_type must be 'phi_1' or 'phi_2'")
-
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+
     inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
@@ -1609,70 +1549,55 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
 
-    fac = 1 / (2 * r)
-
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
         if sigmas[i + 1] == 0:
             x = denoised
-            continue
+        else:
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
+            h_eta = h * (eta + 1)
+            lambda_s_1 = lambda_s + r * h
+            fac = 1 / (2 * r)
+            sigma_s_1 = sigma_fn(lambda_s_1)
 
-        lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-        h = lambda_t - lambda_s
-        h_eta = h * (eta + 1)
-        lambda_s_1 = torch.lerp(lambda_s, lambda_t, r)
-        sigma_s_1 = sigma_fn(lambda_s_1)
+            # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
 
-        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
-        alpha_t = sigmas[i + 1] * lambda_t.exp()
+            coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
+            if inject_noise:
+                # 0 < r < 1
+                noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
+                noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt()
+                noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1])
 
-        # Step 1
-        x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised
-        if inject_noise:
-            sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
-            x_2 = x_2 + sde_noise * sigma_s_1 * s_noise
-        denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+            # Step 1
+            x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
+            if inject_noise:
+                x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
-        # Step 2
-        if solver_type == "phi_1":
-            denoised_d = torch.lerp(denoised, denoised_2, fac)
-            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
-        elif solver_type == "phi_2":
-            b2 = ei_h_phi_2(-h_eta) / r
-            b1 = ei_h_phi_1(-h_eta) - b2
-            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)
-
-        if inject_noise:
-            segment_factor = (r - 1) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1])
-            x = x + sde_noise * sigmas[i + 1] * s_noise
+            # Step 2
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d
+            if inject_noise:
+                x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
     return x
 
-@torch.no_grad()
-def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=None, disable=None, solver_type="phi_2"):
-    """Deterministic exponential Heun second order method in data prediction (x0) and logSNR time."""
-    return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None, r=1.0, solver_type=solver_type)
-
-
-@torch.no_grad()
-def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type="phi_2"):
-    """Stochastic exponential Heun second order method in data prediction (x0) and logSNR time."""
-    return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=1.0, solver_type=solver_type)
-
 
 @torch.no_grad()
 def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
     """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.
-    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
+    arXiv: https://arxiv.org/abs/2305.14267
     """
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+
     inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
@@ -1684,49 +1609,45 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
         if sigmas[i + 1] == 0:
             x = denoised
-            continue
+        else:
+            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+            h = lambda_t - lambda_s
+            h_eta = h * (eta + 1)
+            lambda_s_1 = lambda_s + r_1 * h
+            lambda_s_2 = lambda_s + r_2 * h
+            sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
 
-        lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-        h = lambda_t - lambda_s
-        h_eta = h * (eta + 1)
-        lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1)
-        lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2)
-        sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
+            # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+            alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
+            alpha_t = sigmas[i + 1] * lambda_t.exp()
 
-        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
-        alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
-        alpha_t = sigmas[i + 1] * lambda_t.exp()
+            coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
+            if inject_noise:
+                # 0 < r_1 < r_2 < 1
+                noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
+                noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt()
+                noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt()
+                noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
 
-        # Step 1
-        x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised
-        if inject_noise:
-            sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
-            x_2 = x_2 + sde_noise * sigma_s_1 * s_noise
-        denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+            # Step 1
+            x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
+            if inject_noise:
+                x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
-        # Step 2
-        a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta)
-        a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2
-        x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2)
-        if inject_noise:
-            segment_factor = (r_1 - r_2) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2)
-            x_3 = x_3 + sde_noise * sigma_s_2 * s_noise
-        denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
+            # Step 2
+            x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
+            if inject_noise:
+                x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
+            denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
 
-        # Step 3
-        b3 = ei_h_phi_2(-h_eta) / r_2
-        b1 = ei_h_phi_1(-h_eta) - b3
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3)
-        if inject_noise:
-            segment_factor = (r_2 - 1) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1])
-            x = x + sde_noise * sigmas[i + 1] * s_noise
+            # Step 3
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
+            if inject_noise:
+                x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
     return x
 
 
@@ -1810,7 +1731,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
         # Predictor
         if sigmas[i + 1] == 0:
             # Denoising step
-            x_pred = denoised
+            x = denoised
         else:
             tau_t = tau_func(sigmas[i + 1])
             curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
@@ -1831,7 +1752,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
             if tau_t > 0 and s_noise > 0:
                 noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
                 x_pred = x_pred + noise
-    return x_pred
+    return x
 
 
 @torch.no_grad()

comfy/latent_formats.py

@@ -6,9 +6,7 @@ class LatentFormat:
     latent_dimensions = 2
     latent_rgb_factors = None
     latent_rgb_factors_bias = None
-    latent_rgb_factors_reshape = None
     taesd_decoder_name = None
-    spacial_downscale_ratio = 8
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -81,7 +79,6 @@ class SD_X4(LatentFormat):
 
 class SC_Prior(LatentFormat):
     latent_channels = 16
-    spacial_downscale_ratio = 42
     def __init__(self):
         self.scale_factor = 1.0
         self.latent_rgb_factors = [
@@ -104,7 +101,6 @@ class SC_Prior(LatentFormat):
         ]
 
 class SC_B(LatentFormat):
-    spacial_downscale_ratio = 4
     def __init__(self):
         self.scale_factor = 1.0 / 0.43
         self.latent_rgb_factors = [
@@ -182,55 +178,6 @@ class Flux(SD3):
     def process_out(self, latent):
         return (latent / self.scale_factor) + self.shift_factor
 
-class Flux2(LatentFormat):
-    latent_channels = 128
-    spacial_downscale_ratio = 16
-
-    def __init__(self):
-        self.latent_rgb_factors =[
-            [0.0058, 0.0113, 0.0073],
-            [0.0495, 0.0443, 0.0836],
-            [-0.0099, 0.0096, 0.0644],
-            [0.2144, 0.3009, 0.3652],
-            [0.0166, -0.0039, -0.0054],
-            [0.0157, 0.0103, -0.0160],
-            [-0.0398, 0.0902, -0.0235],
-            [-0.0052, 0.0095, 0.0109],
-            [-0.3527, -0.2712, -0.1666],
-            [-0.0301, -0.0356, -0.0180],
-            [-0.0107, 0.0078, 0.0013],
-            [0.0746, 0.0090, -0.0941],
-            [0.0156, 0.0169, 0.0070],
-            [-0.0034, -0.0040, -0.0114],
-            [0.0032, 0.0181, 0.0080],
-            [-0.0939, -0.0008, 0.0186],
-            [0.0018, 0.0043, 0.0104],
-            [0.0284, 0.0056, -0.0127],
-            [-0.0024, -0.0022, -0.0030],
-            [0.1207, -0.0026, 0.0065],
-            [0.0128, 0.0101, 0.0142],
-            [0.0137, -0.0072, -0.0007],
-            [0.0095, 0.0092, -0.0059],
-            [0.0000, -0.0077, -0.0049],
-            [-0.0465, -0.0204, -0.0312],
-            [0.0095, 0.0012, -0.0066],
-            [0.0290, -0.0034, 0.0025],
-            [0.0220, 0.0169, -0.0048],
-            [-0.0332, -0.0457, -0.0468],
-            [-0.0085, 0.0389, 0.0609],
-            [-0.0076, 0.0003, -0.0043],
-            [-0.0111, -0.0460, -0.0614],
-        ]
-
-        self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
-        self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
-
-    def process_in(self, latent):
-        return latent
-
-    def process_out(self, latent):
-        return latent
-
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
@@ -276,7 +223,6 @@ class Mochi(LatentFormat):
 class LTXV(LatentFormat):
     latent_channels = 128
     latent_dimensions = 3
-    spacial_downscale_ratio = 32
 
     def __init__(self):
         self.latent_rgb_factors = [
@@ -412,11 +358,6 @@ class LTXV(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
 
-class LTXAV(LTXV):
-    def __init__(self):
-        self.latent_rgb_factors = None
-        self.latent_rgb_factors_bias = None
-
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
@@ -441,7 +382,6 @@ class HunyuanVideo(LatentFormat):
     ]
 
     latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]
-    taesd_decoder_name = "taehv"
 
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
@@ -505,7 +445,7 @@ class Wan21(LatentFormat):
         ]).view(1, self.latent_channels, 1, 1, 1)
 
 
-        self.taesd_decoder_name = "lighttaew2_1"
+        self.taesd_decoder_name = None #TODO
 
     def process_in(self, latent):
         latents_mean = self.latents_mean.to(latent.device, latent.dtype)
@@ -520,7 +460,6 @@ class Wan21(LatentFormat):
 class Wan22(Wan21):
     latent_channels = 48
     latent_dimensions = 3
-    spacial_downscale_ratio = 16
 
     latent_rgb_factors = [
             [ 0.0119,  0.0103,  0.0046],
@@ -577,7 +516,6 @@ class Wan22(Wan21):
 
     def __init__(self):
         self.scale_factor = 1.0
-        self.taesd_decoder_name = "lighttaew2_2"
         self.latents_mean = torch.tensor([
                 -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
                 -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
@@ -595,157 +533,11 @@ class Wan22(Wan21):
                 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
             ]).view(1, self.latent_channels, 1, 1, 1)
 
-class HunyuanImage21(LatentFormat):
-    latent_channels = 64
-    latent_dimensions = 2
-    spacial_downscale_ratio = 32
-    scale_factor = 0.75289
-
-    latent_rgb_factors = [
-        [-0.0154, -0.0397, -0.0521],
-        [ 0.0005,  0.0093,  0.0006],
-        [-0.0805, -0.0773, -0.0586],
-        [-0.0494, -0.0487, -0.0498],
-        [-0.0212, -0.0076, -0.0261],
-        [-0.0179, -0.0417, -0.0505],
-        [ 0.0158,  0.0310,  0.0239],
-        [ 0.0409,  0.0516,  0.0201],
-        [ 0.0350,  0.0553,  0.0036],
-        [-0.0447, -0.0327, -0.0479],
-        [-0.0038, -0.0221, -0.0365],
-        [-0.0423, -0.0718, -0.0654],
-        [ 0.0039,  0.0368,  0.0104],
-        [ 0.0655,  0.0217,  0.0122],
-        [ 0.0490,  0.1638,  0.2053],
-        [ 0.0932,  0.0829,  0.0650],
-        [-0.0186, -0.0209, -0.0135],
-        [-0.0080, -0.0076, -0.0148],
-        [-0.0284, -0.0201,  0.0011],
-        [-0.0642, -0.0294, -0.0777],
-        [-0.0035,  0.0076, -0.0140],
-        [ 0.0519,  0.0731,  0.0887],
-        [-0.0102,  0.0095,  0.0704],
-        [ 0.0068,  0.0218, -0.0023],
-        [-0.0726, -0.0486, -0.0519],
-        [ 0.0260,  0.0295,  0.0263],
-        [ 0.0250,  0.0333,  0.0341],
-        [ 0.0168, -0.0120, -0.0174],
-        [ 0.0226,  0.1037,  0.0114],
-        [ 0.2577,  0.1906,  0.1604],
-        [-0.0646, -0.0137, -0.0018],
-        [-0.0112,  0.0309,  0.0358],
-        [-0.0347,  0.0146, -0.0481],
-        [ 0.0234,  0.0179,  0.0201],
-        [ 0.0157,  0.0313,  0.0225],
-        [ 0.0423,  0.0675,  0.0524],
-        [-0.0031,  0.0027, -0.0255],
-        [ 0.0447,  0.0555,  0.0330],
-        [-0.0152,  0.0103,  0.0299],
-        [-0.0755, -0.0489, -0.0635],
-        [ 0.0853,  0.0788,  0.1017],
-        [-0.0272, -0.0294, -0.0471],
-        [ 0.0440,  0.0400, -0.0137],
-        [ 0.0335,  0.0317, -0.0036],
-        [-0.0344, -0.0621, -0.0984],
-        [-0.0127, -0.0630, -0.0620],
-        [-0.0648,  0.0360,  0.0924],
-        [-0.0781, -0.0801, -0.0409],
-        [ 0.0363,  0.0613,  0.0499],
-        [ 0.0238,  0.0034,  0.0041],
-        [-0.0135,  0.0258,  0.0310],
-        [ 0.0614,  0.1086,  0.0589],
-        [ 0.0428,  0.0350,  0.0205],
-        [ 0.0153,  0.0173, -0.0018],
-        [-0.0288, -0.0455, -0.0091],
-        [ 0.0344,  0.0109, -0.0157],
-        [-0.0205, -0.0247, -0.0187],
-        [ 0.0487,  0.0126,  0.0064],
-        [-0.0220, -0.0013,  0.0074],
-        [-0.0203, -0.0094, -0.0048],
-        [-0.0719,  0.0429, -0.0442],
-        [ 0.1042,  0.0497,  0.0356],
-        [-0.0659, -0.0578, -0.0280],
-        [-0.0060, -0.0322, -0.0234]]
-
-    latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206]
-
-class HunyuanImage21Refiner(LatentFormat):
-    latent_channels = 64
-    latent_dimensions = 3
-    scale_factor = 1.03682
-
-    def process_in(self, latent):
-        out = latent * self.scale_factor
-        out = torch.cat((out[:, :, :1], out), dim=2)
-        out = out.permute(0, 2, 1, 3, 4)
-        b, f_times_2, c, h, w = out.shape
-        out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
-        out = out.permute(0, 2, 1, 3, 4).contiguous()
-        return out
-
-    def process_out(self, latent):
-        z = latent / self.scale_factor
-        z = z.permute(0, 2, 1, 3, 4)
-        b, f, c, h, w = z.shape
-        z = z.reshape(b, f, 2, c // 2, h, w)
-        z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
-        z = z.permute(0, 2, 1, 3, 4)
-        z = z[:, :, 1:]
-        return z
-
-class HunyuanVideo15(LatentFormat):
-    latent_rgb_factors = [
-        [ 0.0568, -0.0521, -0.0131],
-        [ 0.0014,  0.0735,  0.0326],
-        [ 0.0186,  0.0531, -0.0138],
-        [-0.0031,  0.0051,  0.0288],
-        [ 0.0110,  0.0556,  0.0432],
-        [-0.0041, -0.0023, -0.0485],
-        [ 0.0530,  0.0413,  0.0253],
-        [ 0.0283,  0.0251,  0.0339],
-        [ 0.0277, -0.0372, -0.0093],
-        [ 0.0393,  0.0944,  0.1131],
-        [ 0.0020,  0.0251,  0.0037],
-        [-0.0017,  0.0012,  0.0234],
-        [ 0.0468,  0.0436,  0.0203],
-        [ 0.0354,  0.0439, -0.0233],
-        [ 0.0090,  0.0123,  0.0346],
-        [ 0.0382,  0.0029,  0.0217],
-        [ 0.0261, -0.0300,  0.0030],
-        [-0.0088, -0.0220, -0.0283],
-        [-0.0272, -0.0121, -0.0363],
-        [-0.0664, -0.0622,  0.0144],
-        [ 0.0414,  0.0479,  0.0529],
-        [ 0.0355,  0.0612, -0.0247],
-        [ 0.0147,  0.0264,  0.0174],
-        [ 0.0438,  0.0038,  0.0542],
-        [ 0.0431, -0.0573, -0.0033],
-        [-0.0162, -0.0211, -0.0406],
-        [-0.0487, -0.0295, -0.0393],
-        [ 0.0005, -0.0109,  0.0253],
-        [ 0.0296,  0.0591,  0.0353],
-        [ 0.0119,  0.0181, -0.0306],
-        [-0.0085, -0.0362,  0.0229],
-        [ 0.0005, -0.0106,  0.0242]
-    ]
-
-    latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
-    latent_channels = 32
-    latent_dimensions = 3
-    spacial_downscale_ratio = 16
-    scale_factor = 1.03682
-    taesd_decoder_name = "lighttaehy1_5"
-
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
     scale_factor = 0.9990943042622529
 
-class Hunyuan3Dv2_1(LatentFormat):
-    scale_factor = 1.0039506158752403
-    latent_channels = 64
-    latent_dimensions = 1
-
 class Hunyuan3Dv2mini(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
@@ -754,25 +546,3 @@ class Hunyuan3Dv2mini(LatentFormat):
 class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
-
-class ACEAudio15(LatentFormat):
-    latent_channels = 64
-    latent_dimensions = 1
-
-class ChromaRadiance(LatentFormat):
-    latent_channels = 3
-    spacial_downscale_ratio = 1
-
-    def __init__(self):
-        self.latent_rgb_factors = [
-            # R    G    B
-            [ 1.0, 0.0, 0.0 ],
-            [ 0.0, 1.0, 0.0 ],
-            [ 0.0, 0.0, 1.0 ]
-        ]
-
-    def process_in(self, latent):
-        return latent
-
-    def process_out(self, latent):
-        return latent

comfy/ldm/ace/attention.py

@@ -133,7 +133,6 @@ class Attention(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        transformer_options={},
         **cross_attention_kwargs,
     ) -> torch.Tensor:
         return self.processor(
@@ -141,7 +140,6 @@ class Attention(nn.Module):
             hidden_states,
             encoder_hidden_states=encoder_hidden_states,
             attention_mask=attention_mask,
-            transformer_options=transformer_options,
             **cross_attention_kwargs,
         )
 
@@ -368,7 +366,6 @@ class CustomerAttnProcessor2_0:
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.Tensor:
@@ -436,7 +433,7 @@ class CustomerAttnProcessor2_0:
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         hidden_states = optimized_attention(
-            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True,
         ).to(query.dtype)
 
         # linear proj
@@ -700,7 +697,6 @@ class LinearTransformerBlock(nn.Module):
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         temb: torch.FloatTensor = None,
-        transformer_options={},
     ):
 
         N = hidden_states.shape[0]
@@ -724,7 +720,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
         else:
             attn_output, _ = self.attn(
@@ -734,7 +729,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=None,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=None,
-                transformer_options=transformer_options,
             )
 
         if self.use_adaln_single:
@@ -749,7 +743,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
             hidden_states = attn_output + hidden_states
 

comfy/ldm/ace/model.py

@@ -19,7 +19,6 @@ import torch
 from torch import nn
 
 import comfy.model_management
-import comfy.patcher_extension
 
 from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
 from .attention import LinearTransformerBlock, t2i_modulate
@@ -314,7 +313,6 @@ class ACEStepTransformer2DModel(nn.Module):
         output_length: int = 0,
         block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
         controlnet_scale: Union[float, torch.Tensor] = 1.0,
-        transformer_options={},
     ):
         embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype))
         temb = self.t_block(embedded_timestep)
@@ -340,34 +338,12 @@ class ACEStepTransformer2DModel(nn.Module):
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=encoder_rotary_freqs_cis,
                 temb=temb,
-                transformer_options=transformer_options,
             )
 
         output = self.final_layer(hidden_states, embedded_timestep, output_length)
         return output
 
-    def forward(self,
-        x,
-        timestep,
-        attention_mask=None,
-        context: Optional[torch.Tensor] = None,
-        text_attention_mask: Optional[torch.LongTensor] = None,
-        speaker_embeds: Optional[torch.FloatTensor] = None,
-        lyric_token_idx: Optional[torch.LongTensor] = None,
-        lyric_mask: Optional[torch.LongTensor] = None,
-        block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
-        controlnet_scale: Union[float, torch.Tensor] = 1.0,
-        lyrics_strength=1.0,
-        **kwargs
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states,
-                  controlnet_scale, lyrics_strength, **kwargs)
-
-    def _forward(
+    def forward(
         self,
         x,
         timestep,
@@ -395,7 +371,6 @@ class ACEStepTransformer2DModel(nn.Module):
 
         output_length = hidden_states.shape[-1]
 
-        transformer_options = kwargs.get("transformer_options", {})
         output = self.decode(
             hidden_states=hidden_states,
             attention_mask=attention_mask,
@@ -405,7 +380,6 @@ class ACEStepTransformer2DModel(nn.Module):
             output_length=output_length,
             block_controlnet_hidden_states=block_controlnet_hidden_states,
             controlnet_scale=controlnet_scale,
-            transformer_options=transformer_options,
         )
 
         return output

comfy/ldm/ace/vae/music_dcae_pipeline.py

@@ -23,6 +23,8 @@ class MusicDCAE(torch.nn.Module):
         else:
             self.source_sample_rate = source_sample_rate
 
+        # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
+
         self.transform = transforms.Compose([
             transforms.Normalize(0.5, 0.5),
         ])
@@ -35,6 +37,10 @@ class MusicDCAE(torch.nn.Module):
         self.scale_factor = 0.1786
         self.shift_factor = -1.9091
 
+    def load_audio(self, audio_path):
+        audio, sr = torchaudio.load(audio_path)
+        return audio, sr
+
     def forward_mel(self, audios):
         mels = []
         for i in range(len(audios)):
@@ -67,8 +73,10 @@ class MusicDCAE(torch.nn.Module):
             latent = self.dcae.encoder(mel.unsqueeze(0))
             latents.append(latent)
         latents = torch.cat(latents, dim=0)
+        # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
         latents = (latents - self.shift_factor) * self.scale_factor
         return latents
+        # return latents, latent_lengths
 
     @torch.no_grad()
     def decode(self, latents, audio_lengths=None, sr=None):
@@ -83,7 +91,9 @@ class MusicDCAE(torch.nn.Module):
             wav = self.vocoder.decode(mels[0]).squeeze(1)
 
             if sr is not None:
+                # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
                 wav = torchaudio.functional.resample(wav, 44100, sr)
+                # wav = resampler(wav)
             else:
                 sr = 44100
             pred_wavs.append(wav)
@@ -91,6 +101,7 @@ class MusicDCAE(torch.nn.Module):
         if audio_lengths is not None:
             pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
         return torch.stack(pred_wavs)
+        # return sr, pred_wavs
 
     def forward(self, audios, audio_lengths=None, sr=None):
         latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)

comfy/ldm/anima/model.py

@@ -1,202 +0,0 @@
-from comfy.ldm.cosmos.predict2 import MiniTrainDIT
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-
-def rotate_half(x):
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    x_embed = (x * cos) + (rotate_half(x) * sin)
-    return x_embed
-
-
-class RotaryEmbedding(nn.Module):
-    def __init__(self, head_dim):
-        super().__init__()
-        self.rope_theta = 10000
-        inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.int64).to(dtype=torch.float) / head_dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class Attention(nn.Module):
-    def __init__(self, query_dim, context_dim, n_heads, head_dim, device=None, dtype=None, operations=None):
-        super().__init__()
-
-        inner_dim = head_dim * n_heads
-        self.n_heads = n_heads
-        self.head_dim = head_dim
-        self.query_dim = query_dim
-        self.context_dim = context_dim
-
-        self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
-        self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
-
-        self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
-        self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
-
-        self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
-
-        self.o_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)
-
-    def forward(self, x, mask=None, context=None, position_embeddings=None, position_embeddings_context=None):
-        context = x if context is None else context
-        input_shape = x.shape[:-1]
-        q_shape = (*input_shape, self.n_heads, self.head_dim)
-        context_shape = context.shape[:-1]
-        kv_shape = (*context_shape, self.n_heads, self.head_dim)
-
-        query_states = self.q_norm(self.q_proj(x).view(q_shape)).transpose(1, 2)
-        key_states = self.k_norm(self.k_proj(context).view(kv_shape)).transpose(1, 2)
-        value_states = self.v_proj(context).view(kv_shape).transpose(1, 2)
-
-        if position_embeddings is not None:
-            assert position_embeddings_context is not None
-            cos, sin = position_embeddings
-            query_states = apply_rotary_pos_emb(query_states, cos, sin)
-            cos, sin = position_embeddings_context
-            key_states = apply_rotary_pos_emb(key_states, cos, sin)
-
-        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=mask)
-
-        attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output
-
-    def init_weights(self):
-        torch.nn.init.zeros_(self.o_proj.weight)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0, use_self_attn=False, layer_norm=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.use_self_attn = use_self_attn
-
-        if self.use_self_attn:
-            self.norm_self_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
-            self.self_attn = Attention(
-                query_dim=model_dim,
-                context_dim=model_dim,
-                n_heads=num_heads,
-                head_dim=model_dim//num_heads,
-                device=device,
-                dtype=dtype,
-                operations=operations,
-            )
-
-        self.norm_cross_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
-        self.cross_attn = Attention(
-            query_dim=model_dim,
-            context_dim=source_dim,
-            n_heads=num_heads,
-            head_dim=model_dim//num_heads,
-            device=device,
-            dtype=dtype,
-            operations=operations,
-        )
-
-        self.norm_mlp = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
-        self.mlp = nn.Sequential(
-            operations.Linear(model_dim, int(model_dim * mlp_ratio), device=device, dtype=dtype),
-            nn.GELU(),
-            operations.Linear(int(model_dim * mlp_ratio), model_dim, device=device, dtype=dtype)
-        )
-
-    def forward(self, x, context, target_attention_mask=None, source_attention_mask=None, position_embeddings=None, position_embeddings_context=None):
-        if self.use_self_attn:
-            normed = self.norm_self_attn(x)
-            attn_out = self.self_attn(normed, mask=target_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings)
-            x = x + attn_out
-
-        normed = self.norm_cross_attn(x)
-        attn_out = self.cross_attn(normed, mask=source_attention_mask, context=context, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)
-        x = x + attn_out
-
-        x = x + self.mlp(self.norm_mlp(x))
-        return x
-
-    def init_weights(self):
-        torch.nn.init.zeros_(self.mlp[2].weight)
-        self.cross_attn.init_weights()
-
-
-class LLMAdapter(nn.Module):
-    def __init__(
-            self,
-            source_dim=1024,
-            target_dim=1024,
-            model_dim=1024,
-            num_layers=6,
-            num_heads=16,
-            use_self_attn=True,
-            layer_norm=False,
-            device=None,
-            dtype=None,
-            operations=None,
-        ):
-        super().__init__()
-
-        self.embed = operations.Embedding(32128, target_dim, device=device, dtype=dtype)
-        if model_dim != target_dim:
-            self.in_proj = operations.Linear(target_dim, model_dim, device=device, dtype=dtype)
-        else:
-            self.in_proj = nn.Identity()
-        self.rotary_emb = RotaryEmbedding(model_dim//num_heads)
-        self.blocks = nn.ModuleList([
-            TransformerBlock(source_dim, model_dim, num_heads=num_heads, use_self_attn=use_self_attn, layer_norm=layer_norm, device=device, dtype=dtype, operations=operations) for _ in range(num_layers)
-        ])
-        self.out_proj = operations.Linear(model_dim, target_dim, device=device, dtype=dtype)
-        self.norm = operations.RMSNorm(target_dim, eps=1e-6, device=device, dtype=dtype)
-
-    def forward(self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None):
-        if target_attention_mask is not None:
-            target_attention_mask = target_attention_mask.to(torch.bool)
-            if target_attention_mask.ndim == 2:
-                target_attention_mask = target_attention_mask.unsqueeze(1).unsqueeze(1)
-
-        if source_attention_mask is not None:
-            source_attention_mask = source_attention_mask.to(torch.bool)
-            if source_attention_mask.ndim == 2:
-                source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1)
-
-        x = self.in_proj(self.embed(target_input_ids))
-        context = source_hidden_states
-        position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0)
-        position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0)
-        position_embeddings = self.rotary_emb(x, position_ids)
-        position_embeddings_context = self.rotary_emb(x, position_ids_context)
-        for block in self.blocks:
-            x = block(x, context, target_attention_mask=target_attention_mask, source_attention_mask=source_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)
-        return self.norm(self.out_proj(x))
-
-
-class Anima(MiniTrainDIT):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.llm_adapter = LLMAdapter(device=kwargs.get("device"), dtype=kwargs.get("dtype"), operations=kwargs.get("operations"))
-
-    def preprocess_text_embeds(self, text_embeds, text_ids):
-        if text_ids is not None:
-            return self.llm_adapter(text_embeds, text_ids)
-        else:
-            return text_embeds

comfy/ldm/audio/dit.py

@@ -298,8 +298,7 @@ class Attention(nn.Module):
         mask = None,
         context_mask = None,
         rotary_pos_emb = None,
-        causal = None,
-        transformer_options={},
+        causal = None
     ):
         h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
 
@@ -364,7 +363,7 @@ class Attention(nn.Module):
             heads_per_kv_head = h // kv_h
             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
 
-        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
         out = self.to_out(out)
 
         if mask is not None:
@@ -489,8 +488,7 @@ class TransformerBlock(nn.Module):
         global_cond=None,
         mask = None,
         context_mask = None,
-        rotary_pos_emb = None,
-        transformer_options={}
+        rotary_pos_emb = None
     ):
         if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
 
@@ -500,12 +498,12 @@ class TransformerBlock(nn.Module):
             residual = x
             x = self.pre_norm(x)
             x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
             x = x * torch.sigmoid(1 - gate_self)
             x = x + residual
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -519,10 +517,10 @@ class TransformerBlock(nn.Module):
             x = x + residual
 
         else:
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -608,8 +606,7 @@ class ContinuousTransformer(nn.Module):
         return_info = False,
         **kwargs
     ):
-        transformer_options = kwargs.get("transformer_options", {})
-        patches_replace = transformer_options.get("patches_replace", {})
+        patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
         batch, seq, device = *x.shape[:2], x.device
         context = kwargs["context"]
 
@@ -635,7 +632,7 @@ class ContinuousTransformer(nn.Module):
         # Attention layers
 
         if self.rotary_pos_emb is not None:
-            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
         else:
             rotary_pos_emb = None
 
@@ -648,13 +645,13 @@ class ContinuousTransformer(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"])
+                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
             # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
 
             if return_info:

comfy/ldm/aura/mmdit.py

@@ -9,7 +9,6 @@ import torch.nn.functional as F
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ops
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 def modulate(x, shift, scale):
@@ -85,7 +84,7 @@ class SingleAttention(nn.Module):
         )
 
     #@torch.compile()
-    def forward(self, c, transformer_options={}):
+    def forward(self, c):
 
         bsz, seqlen1, _ = c.shape
 
@@ -95,7 +94,7 @@ class SingleAttention(nn.Module):
         v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
         q, k = self.q_norm1(q), self.k_norm1(k)
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
         c = self.w1o(output)
         return c
 
@@ -144,7 +143,7 @@ class DoubleAttention(nn.Module):
 
 
     #@torch.compile()
-    def forward(self, c, x, transformer_options={}):
+    def forward(self, c, x):
 
         bsz, seqlen1, _ = c.shape
         bsz, seqlen2, _ = x.shape
@@ -168,7 +167,7 @@ class DoubleAttention(nn.Module):
             torch.cat([cv, xv], dim=1),
         )
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
 
         c, x = output.split([seqlen1, seqlen2], dim=1)
         c = self.w1o(c)
@@ -207,7 +206,7 @@ class MMDiTBlock(nn.Module):
         self.is_last = is_last
 
     #@torch.compile()
-    def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
+    def forward(self, c, x, global_cond, **kwargs):
 
         cres, xres = c, x
 
@@ -225,7 +224,7 @@ class MMDiTBlock(nn.Module):
         x = modulate(self.normX1(x), xshift_msa, xscale_msa)
 
         # attention
-        c, x = self.attn(c, x, transformer_options=transformer_options)
+        c, x = self.attn(c, x)
 
 
         c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
@@ -255,13 +254,13 @@ class DiTBlock(nn.Module):
         self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
 
     #@torch.compile()
-    def forward(self, cx, global_cond, transformer_options={}, **kwargs):
+    def forward(self, cx, global_cond, **kwargs):
         cxres = cx
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
             global_cond
         ).chunk(6, dim=1)
         cx = modulate(self.norm1(cx), shift_msa, scale_msa)
-        cx = self.attn(cx, transformer_options=transformer_options)
+        cx = self.attn(cx)
         cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
         mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
         cx = gate_mlp.unsqueeze(1) * mlpout
@@ -437,13 +436,6 @@ class MMDiT(nn.Module):
         return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
 
     def forward(self, x, timestep, context, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, transformer_options={}, **kwargs):
         patches_replace = transformer_options.get("patches_replace", {})
         # patchify x, add PE
         b, c, h, w = x.shape
@@ -473,14 +465,13 @@ class MMDiT(nn.Module):
                         out = {}
                         out["txt"], out["img"] = layer(args["txt"],
                                                        args["img"],
-                                                       args["vec"],
-                                                       transformer_options=args["transformer_options"])
+                                                       args["vec"])
                         return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
                     c = out["txt"]
                     x = out["img"]
                 else:
-                    c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)
+                    c, x = layer(c, x, global_cond, **kwargs)
 
         if len(self.single_layers) > 0:
             c_len = c.size(1)
@@ -489,13 +480,13 @@ class MMDiT(nn.Module):
                 if ("single_block", i) in blocks_replace:
                     def block_wrap(args):
                         out = {}
-                        out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"])
+                        out["img"] = layer(args["img"], args["vec"])
                         return out
 
-                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
                     cx = out["img"]
                 else:
-                    cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs)
+                    cx = layer(cx, global_cond, **kwargs)
 
             x = cx[:, c_len:]
 

comfy/ldm/cascade/common.py

@@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module):
 
         self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
 
-    def forward(self, q, k, v, transformer_options={}):
+    def forward(self, q, k, v):
         q = self.to_q(q)
         k = self.to_k(k)
         v = self.to_v(v)
 
-        out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads)
 
         return self.out_proj(out)
 
@@ -47,13 +47,13 @@ class Attention2D(nn.Module):
         self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
         # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
 
-    def forward(self, x, kv, self_attn=False, transformer_options={}):
+    def forward(self, x, kv, self_attn=False):
         orig_shape = x.shape
         x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
         if self_attn:
             kv = torch.cat([x, kv], dim=1)
         # x = self.attn(x, kv, kv, need_weights=False)[0]
-        x = self.attn(x, kv, kv, transformer_options=transformer_options)
+        x = self.attn(x, kv, kv)
         x = x.permute(0, 2, 1).view(*orig_shape)
         return x
 
@@ -114,9 +114,9 @@ class AttnBlock(nn.Module):
             operations.Linear(c_cond, c, dtype=dtype, device=device)
         )
 
-    def forward(self, x, kv, transformer_options={}):
+    def forward(self, x, kv):
         kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
         return x
 
 

comfy/ldm/cascade/stage_b.py

@@ -173,7 +173,7 @@ class StageB(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -187,7 +187,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -199,7 +199,7 @@ class StageB(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -216,7 +216,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -228,7 +228,7 @@ class StageB(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
         if pixels is None:
             pixels = x.new_zeros(x.size(0), 3, 8, 8)
 
@@ -245,8 +245,8 @@ class StageB(nn.Module):
             nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
         x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
                                           align_corners=True)
-        level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip)
+        x = self._up_decode(level_outputs, r_embed, clip)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/cascade/stage_c.py

@@ -182,7 +182,7 @@ class StageC(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip, cnet=None):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -201,7 +201,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -213,7 +213,7 @@ class StageC(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -235,7 +235,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -247,7 +247,7 @@ class StageC(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
         # Process the conditioning embeddings
         r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
         for c in self.t_conds:
@@ -262,8 +262,8 @@ class StageC(nn.Module):
 
         # Model Blocks
         x = self.embedding(x)
-        level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/chroma/layers.py

@@ -1,15 +1,15 @@
 import torch
 from torch import Tensor, nn
 
+from comfy.ldm.flux.math import attention
 from comfy.ldm.flux.layers import (
     MLPEmbedder,
     RMSNorm,
+    QKNorm,
+    SelfAttention,
     ModulationOut,
 )
 
-# TODO: remove this in a few months
-SingleStreamBlock = None
-DoubleStreamBlock = None
 
 
 class ChromaModulationOut(ModulationOut):
@@ -48,6 +48,124 @@ class Approximator(nn.Module):
         return x
 
 
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.flipped_img_txt = flipped_img_txt
+
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
+        (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
+
+        # prepare image for attention
+        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        attn = attention(torch.cat((txt_q, img_q), dim=2),
+                         torch.cat((txt_k, img_k), dim=2),
+                         torch.cat((txt_v, img_v), dim=2),
+                         pe=pe, mask=attn_mask)
+
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
+        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
+
+        # calculate the txt bloks
+        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
+        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
+
+        if txt.dtype == torch.float16:
+            txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
+
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float = None,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
+        mod = vec
+        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        x.addcmul_(mod.gate, output)
+        if x.dtype == torch.float16:
+            x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+        return x
+
+
 class LastLayer(nn.Module):
     def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
         super().__init__()

comfy/ldm/chroma/model.py

@@ -5,18 +5,17 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 from einops import rearrange, repeat
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 from comfy.ldm.flux.layers import (
     EmbedND,
     timestep_embedding,
-    DoubleStreamBlock,
-    SingleStreamBlock,
 )
 
 from .layers import (
+    DoubleStreamBlock,
     LastLayer,
+    SingleStreamBlock,
     Approximator,
     ChromaModulationOut,
 )
@@ -40,8 +39,7 @@ class ChromaParams:
     out_dim: int
     hidden_dim: int
     n_layers: int
-    txt_ids_dims: list
-    vec_in_dim: int
+
 
 
 
@@ -91,7 +89,6 @@ class Chroma(nn.Module):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
-                    modulation=False,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -100,7 +97,7 @@ class Chroma(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations)
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
                 for _ in range(params.depth_single_blocks)
             ]
         )
@@ -153,6 +150,8 @@ class Chroma(nn.Module):
         attn_mask: Tensor = None,
     ) -> Tensor:
         patches_replace = transformer_options.get("patches_replace", {})
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
 
         # running on sequences img
         img = self.img_in(img)
@@ -180,10 +179,7 @@ class Chroma(nn.Module):
         pe = self.pe_embedder(ids)
 
         blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.double_blocks)
-        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
-            transformer_options["block_index"] = i
             if i not in self.skip_mmdit:
                 double_mod = (
                     self.get_modulations(mod_vectors, "double_img", idx=i),
@@ -196,16 +192,14 @@ class Chroma(nn.Module):
                                                        txt=args["txt"],
                                                        vec=args["vec"],
                                                        pe=args["pe"],
-                                                       attn_mask=args.get("attn_mask"),
-                                                       transformer_options=args.get("transformer_options"))
+                                                       attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("double_block", i)]({"img": img,
                                                                "txt": txt,
                                                                "vec": double_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     txt = out["txt"]
                     img = out["img"]
@@ -214,8 +208,7 @@ class Chroma(nn.Module):
                                      txt=txt,
                                      vec=double_mod,
                                      pe=pe,
-                                     attn_mask=attn_mask,
-                                     transformer_options=transformer_options)
+                                     attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_i = control.get("input")
@@ -226,10 +219,7 @@ class Chroma(nn.Module):
 
         img = torch.cat((txt, img), 1)
 
-        transformer_options["total_blocks"] = len(self.single_blocks)
-        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
-            transformer_options["block_index"] = i
             if i not in self.skip_dit:
                 single_mod = self.get_modulations(mod_vectors, "single", idx=i)
                 if ("single_block", i) in blocks_replace:
@@ -238,19 +228,17 @@ class Chroma(nn.Module):
                         out["img"] = block(args["img"],
                                            vec=args["vec"],
                                            pe=args["pe"],
-                                           attn_mask=args.get("attn_mask"),
-                                           transformer_options=args.get("transformer_options"))
+                                           attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("single_block", i)]({"img": img,
                                                                "vec": single_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     img = out["img"]
                 else:
-                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_o = control.get("output")
@@ -260,27 +248,16 @@ class Chroma(nn.Module):
                             img[:, txt.shape[1] :, ...] += add
 
         img = img[:, txt.shape[1] :, ...]
-        if hasattr(self, "final_layer"):
-            final_mod = self.get_modulations(mod_vectors, "final")
-            img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        final_mod = self.get_modulations(mod_vectors, "final")
+        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
 
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
 
-        if img.ndim != 3 or context.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
         h_len = ((h + (self.patch_size // 2)) // self.patch_size)
         w_len = ((w + (self.patch_size // 2)) // self.patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)

comfy/ldm/chroma_radiance/layers.py

@@ -1,206 +0,0 @@
-# Adapted from https://github.com/lodestone-rock/flow
-from functools import lru_cache
-
-import torch
-from torch import nn
-
-from comfy.ldm.flux.layers import RMSNorm
-
-
-class NerfEmbedder(nn.Module):
-    """
-    An embedder module that combines input features with a 2D positional
-    encoding that mimics the Discrete Cosine Transform (DCT).
-
-    This module takes an input tensor of shape (B, P^2, C), where P is the
-    patch size, and enriches it with positional information before projecting
-    it to a new hidden size.
-    """
-    def __init__(
-        self,
-        in_channels: int,
-        hidden_size_input: int,
-        max_freqs: int,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        """
-        Initializes the NerfEmbedder.
-
-        Args:
-            in_channels (int): The number of channels in the input tensor.
-            hidden_size_input (int): The desired dimension of the output embedding.
-            max_freqs (int): The number of frequency components to use for both
-                             the x and y dimensions of the positional encoding.
-                             The total number of positional features will be max_freqs^2.
-        """
-        super().__init__()
-        self.dtype = dtype
-        self.max_freqs = max_freqs
-        self.hidden_size_input = hidden_size_input
-
-        # A linear layer to project the concatenated input features and
-        # positional encodings to the final output dimension.
-        self.embedder = nn.Sequential(
-            operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device)
-        )
-
-    @lru_cache(maxsize=4)
-    def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-        """
-        Generates and caches 2D DCT-like positional embeddings for a given patch size.
-
-        The LRU cache is a performance optimization that avoids recomputing the
-        same positional grid on every forward pass.
-
-        Args:
-            patch_size (int): The side length of the square input patch.
-            device: The torch device to create the tensors on.
-            dtype: The torch dtype for the tensors.
-
-        Returns:
-            A tensor of shape (1, patch_size^2, max_freqs^2) containing the
-            positional embeddings.
-        """
-        # Create normalized 1D coordinate grids from 0 to 1.
-        pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
-        pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
-
-        # Create a 2D meshgrid of coordinates.
-        pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij")
-
-        # Reshape positions to be broadcastable with frequencies.
-        # Shape becomes (patch_size^2, 1, 1).
-        pos_x = pos_x.reshape(-1, 1, 1)
-        pos_y = pos_y.reshape(-1, 1, 1)
-
-        # Create a 1D tensor of frequency values from 0 to max_freqs-1.
-        freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device)
-
-        # Reshape frequencies to be broadcastable for creating 2D basis functions.
-        # freqs_x shape: (1, max_freqs, 1)
-        # freqs_y shape: (1, 1, max_freqs)
-        freqs_x = freqs[None, :, None]
-        freqs_y = freqs[None, None, :]
-
-        # A custom weighting coefficient, not part of standard DCT.
-        # This seems to down-weight the contribution of higher-frequency interactions.
-        coeffs = (1 + freqs_x * freqs_y) ** -1
-
-        # Calculate the 1D cosine basis functions for x and y coordinates.
-        # This is the core of the DCT formulation.
-        dct_x = torch.cos(pos_x * freqs_x * torch.pi)
-        dct_y = torch.cos(pos_y * freqs_y * torch.pi)
-
-        # Combine the 1D basis functions to create 2D basis functions by element-wise
-        # multiplication, and apply the custom coefficients. Broadcasting handles the
-        # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y).
-        # The result is flattened into a feature vector for each position.
-        dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2)
-
-        return dct
-
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        """
-        Forward pass for the embedder.
-
-        Args:
-            inputs (Tensor): The input tensor of shape (B, P^2, C).
-
-        Returns:
-            Tensor: The output tensor of shape (B, P^2, hidden_size_input).
-        """
-        # Get the batch size, number of pixels, and number of channels.
-        B, P2, C = inputs.shape
-
-        # Infer the patch side length from the number of pixels (P^2).
-        patch_size = int(P2 ** 0.5)
-
-        input_dtype = inputs.dtype
-        inputs = inputs.to(dtype=self.dtype)
-
-        # Fetch the pre-computed or cached positional embeddings.
-        dct = self.fetch_pos(patch_size, inputs.device, self.dtype)
-
-        # Repeat the positional embeddings for each item in the batch.
-        dct = dct.repeat(B, 1, 1)
-
-        # Concatenate the original input features with the positional embeddings
-        # along the feature dimension.
-        inputs = torch.cat((inputs, dct), dim=-1)
-
-        # Project the combined tensor to the target hidden size.
-        return self.embedder(inputs).to(dtype=input_dtype)
-
-
-class NerfGLUBlock(nn.Module):
-    """
-    A NerfBlock using a Gated Linear Unit (GLU) like MLP.
-    """
-    def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None):
-        super().__init__()
-        # The total number of parameters for the MLP is increased to accommodate
-        # the gate, value, and output projection matrices.
-        # We now need to generate parameters for 3 matrices.
-        total_params = 3 * hidden_size_x**2 * mlp_ratio
-        self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device)
-        self.norm = RMSNorm(hidden_size_x, dtype=dtype, device=device, operations=operations)
-        self.mlp_ratio = mlp_ratio
-
-
-    def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
-        batch_size, num_x, hidden_size_x = x.shape
-        mlp_params = self.param_generator(s)
-
-        # Split the generated parameters into three parts for the gate, value, and output projection.
-        fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1)
-
-        # Reshape the parameters into matrices for batch matrix multiplication.
-        fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
-        fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
-        fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x)
-
-        # Normalize the generated weight matrices as in the original implementation.
-        fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2)
-        fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2)
-        fc2 = torch.nn.functional.normalize(fc2, dim=-2)
-
-        res_x = x
-        x = self.norm(x)
-
-        # Apply the final output projection.
-        x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2)
-
-        return x + res_x
-
-
-class NerfFinalLayer(nn.Module):
-    def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
-        self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
-        # So we temporarily move the channel dimension to the end for the norm operation.
-        return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1)
-
-
-class NerfFinalLayerConv(nn.Module):
-    def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
-        self.conv = operations.Conv2d(
-            in_channels=hidden_size,
-            out_channels=out_channels,
-            kernel_size=3,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
-        # So we temporarily move the channel dimension to the end for the norm operation.
-        return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1))

comfy/ldm/chroma_radiance/model.py

@@ -1,335 +0,0 @@
-# Credits:
-# Original Flux code can be found on: https://github.com/black-forest-labs/flux
-# Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow
-
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from torch import Tensor, nn
-from einops import repeat
-import comfy.ldm.common_dit
-
-from comfy.ldm.flux.layers import EmbedND, DoubleStreamBlock, SingleStreamBlock
-
-from comfy.ldm.chroma.model import Chroma, ChromaParams
-from comfy.ldm.chroma.layers import (
-    Approximator,
-)
-from .layers import (
-    NerfEmbedder,
-    NerfGLUBlock,
-    NerfFinalLayer,
-    NerfFinalLayerConv,
-)
-
-
-@dataclass
-class ChromaRadianceParams(ChromaParams):
-    patch_size: int
-    nerf_hidden_size: int
-    nerf_mlp_ratio: int
-    nerf_depth: int
-    nerf_max_freqs: int
-    # Setting nerf_tile_size to 0 disables tiling.
-    nerf_tile_size: int
-    # Currently one of linear (legacy) or conv.
-    nerf_final_head_type: str
-    # None means use the same dtype as the model.
-    nerf_embedder_dtype: Optional[torch.dtype]
-    use_x0: bool
-
-class ChromaRadiance(Chroma):
-    """
-    Transformer model for flow matching on sequences.
-    """
-
-    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
-        if operations is None:
-            raise RuntimeError("Attempt to create ChromaRadiance object without setting operations")
-        nn.Module.__init__(self)
-        self.dtype = dtype
-        params = ChromaRadianceParams(**kwargs)
-        self.params = params
-        self.patch_size = params.patch_size
-        self.in_channels = params.in_channels
-        self.out_channels = params.out_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(
-                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
-            )
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.in_dim = params.in_dim
-        self.out_dim = params.out_dim
-        self.hidden_dim = params.hidden_dim
-        self.n_layers = params.n_layers
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in_patch = operations.Conv2d(
-            params.in_channels,
-            params.hidden_size,
-            kernel_size=params.patch_size,
-            stride=params.patch_size,
-            bias=True,
-            dtype=dtype,
-            device=device,
-        )
-        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
-        # set as nn identity for now, will overwrite it later.
-        self.distilled_guidance_layer = Approximator(
-                    in_dim=self.in_dim,
-                    hidden_dim=self.hidden_dim,
-                    out_dim=self.out_dim,
-                    n_layers=self.n_layers,
-                    dtype=dtype, device=device, operations=operations
-                )
-
-        self.double_blocks = nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                    modulation=False,
-                    dtype=dtype, device=device, operations=operations
-                )
-                for _ in range(params.depth)
-            ]
-        )
-
-        self.single_blocks = nn.ModuleList(
-            [
-                SingleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    modulation=False,
-                    dtype=dtype, device=device, operations=operations,
-                )
-                for _ in range(params.depth_single_blocks)
-            ]
-        )
-
-        # pixel channel concat with DCT
-        self.nerf_image_embedder = NerfEmbedder(
-            in_channels=params.in_channels,
-            hidden_size_input=params.nerf_hidden_size,
-            max_freqs=params.nerf_max_freqs,
-            dtype=params.nerf_embedder_dtype or dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.nerf_blocks = nn.ModuleList([
-            NerfGLUBlock(
-                hidden_size_s=params.hidden_size,
-                hidden_size_x=params.nerf_hidden_size,
-                mlp_ratio=params.nerf_mlp_ratio,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            ) for _ in range(params.nerf_depth)
-        ])
-
-        if params.nerf_final_head_type == "linear":
-            self.nerf_final_layer = NerfFinalLayer(
-                params.nerf_hidden_size,
-                out_channels=params.in_channels,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-        elif params.nerf_final_head_type == "conv":
-            self.nerf_final_layer_conv = NerfFinalLayerConv(
-                params.nerf_hidden_size,
-                out_channels=params.in_channels,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-        else:
-            errstr = f"Unsupported nerf_final_head_type {params.nerf_final_head_type}"
-            raise ValueError(errstr)
-
-        self.skip_mmdit = []
-        self.skip_dit = []
-        self.lite = False
-
-        if params.use_x0:
-            self.register_buffer("__x0__", torch.tensor([]))
-
-    @property
-    def _nerf_final_layer(self) -> nn.Module:
-        if self.params.nerf_final_head_type == "linear":
-            return self.nerf_final_layer
-        if self.params.nerf_final_head_type == "conv":
-            return self.nerf_final_layer_conv
-        # Impossible to get here as we raise an error on unexpected types on initialization.
-        raise NotImplementedError
-
-    def img_in(self, img: Tensor) -> Tensor:
-        img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P]
-        # flatten into a sequence for the transformer.
-        return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden]
-
-    def forward_nerf(
-        self,
-        img_orig: Tensor,
-        img_out: Tensor,
-        params: ChromaRadianceParams,
-    ) -> Tensor:
-        B, C, H, W = img_orig.shape
-        num_patches = img_out.shape[1]
-        patch_size = params.patch_size
-
-        # Store the raw pixel values of each patch for the NeRF head later.
-        # unfold creates patches: [B, C * P * P, NumPatches]
-        nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)
-        nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]
-
-        # Reshape for per-patch processing
-        nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
-        nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
-
-        if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:
-            # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than
-            # the tile size.
-            img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params)
-        else:
-            # Get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct = self.nerf_image_embedder(nerf_pixels)
-
-            # Pass through the dynamic MLP blocks (the NeRF)
-            for block in self.nerf_blocks:
-                img_dct = block(img_dct, nerf_hidden)
-
-        # Reassemble the patches into the final image.
-        img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P]
-        # Reshape to combine with batch dimension for fold
-        img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P]
-        img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches]
-        img_dct = nn.functional.fold(
-            img_dct,
-            output_size=(H, W),
-            kernel_size=patch_size,
-            stride=patch_size,
-        )
-        return self._nerf_final_layer(img_dct)
-
-    def forward_tiled_nerf(
-        self,
-        nerf_hidden: Tensor,
-        nerf_pixels: Tensor,
-        batch: int,
-        channels: int,
-        num_patches: int,
-        patch_size: int,
-        params: ChromaRadianceParams,
-    ) -> Tensor:
-        """
-        Processes the NeRF head in tiles to save memory.
-        nerf_hidden has shape [B, L, D]
-        nerf_pixels has shape [B, L, C * P * P]
-        """
-        tile_size = params.nerf_tile_size
-        output_tiles = []
-        # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1.
-        for i in range(0, num_patches, tile_size):
-            end = min(i + tile_size, num_patches)
-
-            # Slice the current tile from the input tensors
-            nerf_hidden_tile = nerf_hidden[i * batch:end * batch]
-            nerf_pixels_tile = nerf_pixels[i * batch:end * batch]
-
-            # get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)
-
-            # pass through the dynamic MLP blocks (the NeRF)
-            for block in self.nerf_blocks:
-                img_dct_tile = block(img_dct_tile, nerf_hidden_tile)
-
-            output_tiles.append(img_dct_tile)
-
-        # Concatenate the processed tiles along the patch dimension
-        return torch.cat(output_tiles, dim=0)
-
-    def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams:
-        params = self.params
-        if not overrides:
-            return params
-        params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__}
-        nullable_keys = frozenset(("nerf_embedder_dtype",))
-        bad_keys = tuple(k for k in overrides if k not in params_dict)
-        if bad_keys:
-            e = f"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
-            raise ValueError(e)
-        bad_keys = tuple(
-            k
-            for k, v in overrides.items()
-            if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)
-        )
-        if bad_keys:
-            e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
-            raise ValueError(e)
-        # At this point it's all valid keys and values so we can merge with the existing params.
-        params_dict |= overrides
-        return params.__class__(**params_dict)
-
-    def _apply_x0_residual(self, predicted, noisy, timesteps):
-
-        # non zero during training to prevent 0 div
-        eps = 0.0
-        return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)
-
-    def _forward(
-        self,
-        x: Tensor,
-        timestep: Tensor,
-        context: Tensor,
-        guidance: Optional[Tensor],
-        control: Optional[dict]=None,
-        transformer_options: dict={},
-        **kwargs: dict,
-    ) -> Tensor:
-        bs, c, h, w = x.shape
-        img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
-
-        if img.ndim != 4:
-            raise ValueError("Input img tensor must be in [B, C, H, W] format.")
-        if context.ndim != 3:
-            raise ValueError("Input txt tensors must have 3 dimensions.")
-
-        params = self.radiance_get_override_params(transformer_options.get("chroma_radiance_options", {}))
-
-        h_len = (img.shape[-2] // self.patch_size)
-        w_len = (img.shape[-1] // self.patch_size)
-
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-
-        img_out = self.forward_orig(
-            img,
-            img_ids,
-            context,
-            txt_ids,
-            timestep,
-            guidance,
-            control,
-            transformer_options,
-            attn_mask=kwargs.get("attention_mask", None),
-        )
-
-        out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]
-
-        # If x0 variant → v-pred, just return this instead
-        if hasattr(self, "__x0__"):
-            out = self._apply_x0_residual(out, img, timestep)
-        return out
-

comfy/ldm/cosmos/blocks.py

@@ -176,7 +176,6 @@ class Attention(nn.Module):
         context=None,
         mask=None,
         rope_emb=None,
-        transformer_options={},
         **kwargs,
     ):
         """
@@ -185,7 +184,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
-        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
         del q, k, v
         out = rearrange(out, " b n s c -> s b (n c)")
         return self.to_out(out)
@@ -547,7 +546,6 @@ class VideoAttn(nn.Module):
         context: Optional[torch.Tensor] = None,
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for video attention.
@@ -573,7 +571,6 @@ class VideoAttn(nn.Module):
             context_M_B_D,
             crossattn_mask,
             rope_emb=rope_emb_L_1_1_D,
-            transformer_options=transformer_options,
         )
         x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
         return x_T_H_W_B_D
@@ -668,7 +665,6 @@ class DITBuildingBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for dynamically configured blocks with adaptive normalization.
@@ -706,7 +702,6 @@ class DITBuildingBlock(nn.Module):
                 adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
                 context=None,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         elif self.block_type in ["cross_attn", "ca"]:
             x = x + gate_1_1_1_B_D * self.block(
@@ -714,7 +709,6 @@ class DITBuildingBlock(nn.Module):
                 context=crossattn_emb,
                 crossattn_mask=crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         else:
             raise ValueError(f"Unknown block type: {self.block_type}")
@@ -790,7 +784,6 @@ class GeneralDITTransformerBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         for block in self.blocks:
             x = block(
@@ -800,6 +793,5 @@ class GeneralDITTransformerBlock(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
         return x

comfy/ldm/cosmos/cosmos_tokenizer/utils.py

@@ -58,8 +58,7 @@ def is_odd(n: int) -> bool:
 
 
 def nonlinearity(x):
-    # x * sigmoid(x)
-    return torch.nn.functional.silu(x)
+    return x * torch.sigmoid(x)
 
 
 def Normalize(in_channels, num_groups=32):

comfy/ldm/cosmos/model.py

@@ -27,8 +27,6 @@ from torchvision import transforms
 from enum import Enum
 import logging
 
-import comfy.patcher_extension
-
 from .blocks import (
     FinalLayer,
     GeneralDITTransformerBlock,
@@ -437,42 +435,6 @@ class GeneralDIT(nn.Module):
         latent_condition_sigma: Optional[torch.Tensor] = None,
         condition_video_augment_sigma: Optional[torch.Tensor] = None,
         **kwargs,
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x,
-                timesteps,
-                context,
-                attention_mask,
-                fps,
-                image_size,
-                padding_mask,
-                scalar_feature,
-                data_type,
-                latent_condition,
-                latent_condition_sigma,
-                condition_video_augment_sigma,
-                **kwargs)
-
-    def _forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        # crossattn_emb: torch.Tensor,
-        # crossattn_mask: Optional[torch.Tensor] = None,
-        fps: Optional[torch.Tensor] = None,
-        image_size: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        scalar_feature: Optional[torch.Tensor] = None,
-        data_type: Optional[DataType] = DataType.VIDEO,
-        latent_condition: Optional[torch.Tensor] = None,
-        latent_condition_sigma: Optional[torch.Tensor] = None,
-        condition_video_augment_sigma: Optional[torch.Tensor] = None,
-        **kwargs,
     ):
         """
         Args:
@@ -520,7 +482,6 @@ class GeneralDIT(nn.Module):
                 x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
             ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
 
-        transformer_options = kwargs.get("transformer_options", {})
         for _, block in self.blocks.items():
             assert (
                 self.blocks["block0"].x_format == block.x_format
@@ -535,7 +496,6 @@ class GeneralDIT(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
 
         x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")

comfy/ldm/cosmos/predict2.py

@@ -11,9 +11,7 @@ import math
 from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis
 from torchvision import transforms
 
-import comfy.patcher_extension
 from comfy.ldm.modules.attention import optimized_attention
-import comfy.ldm.common_dit
 
 def apply_rotary_pos_emb(
     t: torch.Tensor,
@@ -45,7 +43,7 @@ class GPT2FeedForward(nn.Module):
         return x
 
 
-def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
+def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor:
     """Computes multi-head attention using PyTorch's native implementation.
 
     This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
@@ -72,7 +70,7 @@ def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H
     q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
     k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
     v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)
+    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True)
 
 
 class Attention(nn.Module):
@@ -181,8 +179,8 @@ class Attention(nn.Module):
 
         return q, k, v
 
-    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
-        result = self.attn_op(q, k, v, transformer_options=transformer_options)  # [B, S, H, D]
+    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        result = self.attn_op(q, k, v)  # [B, S, H, D]
         return self.output_dropout(self.output_proj(result))
 
     def forward(
@@ -190,7 +188,6 @@ class Attention(nn.Module):
         x: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         rope_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Args:
@@ -198,7 +195,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
-        return self.compute_attention(q, k, v, transformer_options=transformer_options)
+        return self.compute_attention(q, k, v)
 
 
 class Timesteps(nn.Module):
@@ -335,7 +332,7 @@ class FinalLayer(nn.Module):
         device=None, dtype=None, operations=None
     ):
         super().__init__()
-        self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.linear = operations.Linear(
             hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
         )
@@ -461,10 +458,7 @@ class Block(nn.Module):
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
-        residual_dtype = x_B_T_H_W_D.dtype
-        compute_dtype = emb_B_T_D.dtype
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
 
@@ -514,34 +508,31 @@ class Block(nn.Module):
         result_B_T_H_W_D = rearrange(
             self.self_attn(
                 # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             ),
             "b (t h w) d -> b t h w d",
             t=T,
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
             layer_norm_cross_attn: Callable,
             _scale_cross_attn_B_T_1_1_D: torch.Tensor,
             _shift_cross_attn_B_T_1_1_D: torch.Tensor,
-            transformer_options: Optional[dict] = {},
         ) -> torch.Tensor:
             _normalized_x_B_T_H_W_D = _fn(
                 _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
             )
             _result_B_T_H_W_D = rearrange(
                 self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
-                    transformer_options=transformer_options,
                 ),
                 "b (t h w) d -> b t h w d",
                 t=T,
@@ -555,9 +546,8 @@ class Block(nn.Module):
             self.layer_norm_cross_attn,
             scale_cross_attn_B_T_1_1_D,
             shift_cross_attn_B_T_1_1_D,
-            transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
+        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -565,8 +555,8 @@ class Block(nn.Module):
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
         )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
         return x_B_T_H_W_D
 
 
@@ -815,21 +805,7 @@ class MiniTrainDIT(nn.Module):
         )
         return x_B_C_Tt_Hp_Wp
 
-    def forward(self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: torch.Tensor,
-        fps: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x, timesteps, context, fps, padding_mask, **kwargs)
-
-    def _forward(
+    def forward(
         self,
         x: torch.Tensor,
         timesteps: torch.Tensor,
@@ -838,8 +814,6 @@ class MiniTrainDIT(nn.Module):
         padding_mask: Optional[torch.Tensor] = None,
         **kwargs,
     ):
-        orig_shape = list(x.shape)
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_temporal, self.patch_spatial, self.patch_spatial))
         x_B_C_T_H_W = x
         timesteps_B_T = timesteps
         crossattn_emb = context
@@ -876,16 +850,7 @@ class MiniTrainDIT(nn.Module):
             "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
             "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
-            "transformer_options": kwargs.get("transformer_options", {}),
         }
-
-        # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
-        # in fp32, but run attention and MLP modules in fp16.
-        # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable
-        # quality degradation and visual artifacts.
-        if x_B_T_H_W_D.dtype == torch.float16:
-            x_B_T_H_W_D = x_B_T_H_W_D.float()
-
         for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
@@ -894,6 +859,6 @@ class MiniTrainDIT(nn.Module):
                 **block_kwargs,
             )
 
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
-        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)
         return x_B_C_Tt_Hp_Wp

comfy/ldm/flux/layers.py

@@ -48,44 +48,15 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10
     return embedding
 
 class MLPEmbedder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None, device=None, operations=None):
+    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
         super().__init__()
-        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
+        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
         self.silu = nn.SiLU()
-        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
 
     def forward(self, x: Tensor) -> Tensor:
         return self.out_layer(self.silu(self.in_layer(x)))
 
-class YakMLP(nn.Module):
-    def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
-        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
-        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device)
-        self.act_fn = nn.SiLU()
-
-    def forward(self, x: Tensor) -> Tensor:
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None):
-    if yak_mlp:
-        return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
-    if mlp_silu_act:
-        return nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
-            SiLUActivation(),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
-        )
-    else:
-        return nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-            nn.GELU(approximate="tanh"),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, dtype=None, device=None, operations=None):
@@ -109,14 +80,14 @@ class QKNorm(torch.nn.Module):
 
 
 class SelfAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
         super().__init__()
         self.num_heads = num_heads
         head_dim = dim // num_heads
 
         self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
         self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
-        self.proj = operations.Linear(dim, dim, bias=proj_bias, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
 
 
 @dataclass
@@ -127,11 +98,11 @@ class ModulationOut:
 
 
 class Modulation(nn.Module):
-    def __init__(self, dim: int, double: bool, bias=True, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
         super().__init__()
         self.is_double = double
         self.multiplier = 6 if double else 3
-        self.lin = operations.Linear(dim, self.multiplier * dim, bias=bias, dtype=dtype, device=device)
+        self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
 
     def forward(self, vec: Tensor) -> tuple:
         if vec.ndim == 2:
@@ -158,107 +129,77 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
         return tensor
 
 
-class SiLUActivation(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.gate_fn = nn.SiLU()
-
-    def forward(self, x: Tensor) -> Tensor:
-        x1, x2 = x.chunk(2, dim=-1)
-        return self.gate_fn(x1) * x2
-
-
 class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
         self.num_heads = num_heads
         self.hidden_size = hidden_size
-        self.modulation = modulation
-
-        if self.modulation:
-            self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
-
+        self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
         self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
 
         self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
 
-        self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
-
-        if self.modulation:
-            self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
-
+        self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
         self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
 
         self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-
-        self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
-
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
         self.flipped_img_txt = flipped_img_txt
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
-        if self.modulation:
-            img_mod1, img_mod2 = self.img_mod(vec)
-            txt_mod1, txt_mod2 = self.txt_mod(vec)
-        else:
-            (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None):
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
 
         # prepare image for attention
         img_modulated = self.img_norm1(img)
         img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img)
         img_qkv = self.img_attn.qkv(img_modulated)
-        del img_modulated
         img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        del img_qkv
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
         txt_modulated = self.txt_norm1(txt)
         txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt)
         txt_qkv = self.txt_attn.qkv(txt_modulated)
-        del txt_modulated
         txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        del txt_qkv
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
 
         if self.flipped_img_txt:
-            q = torch.cat((img_q, txt_q), dim=2)
-            del img_q, txt_q
-            k = torch.cat((img_k, txt_k), dim=2)
-            del img_k, txt_k
-            v = torch.cat((img_v, txt_v), dim=2)
-            del img_v, txt_v
             # run actual attention
-            attn = attention(q, k, v,
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
-            del q, k, v
+            attn = attention(torch.cat((img_q, txt_q), dim=2),
+                             torch.cat((img_k, txt_k), dim=2),
+                             torch.cat((img_v, txt_v), dim=2),
+                             pe=pe, mask=attn_mask)
 
             img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
         else:
-            q = torch.cat((txt_q, img_q), dim=2)
-            del txt_q, img_q
-            k = torch.cat((txt_k, img_k), dim=2)
-            del txt_k, img_k
-            v = torch.cat((txt_v, img_v), dim=2)
-            del txt_v, img_v
             # run actual attention
-            attn = attention(q, k, v,
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
-            del q, k, v
+            attn = attention(torch.cat((txt_q, img_q), dim=2),
+                             torch.cat((txt_k, img_k), dim=2),
+                             torch.cat((txt_v, img_v), dim=2),
+                             pe=pe, mask=attn_mask)
 
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
         # calculate the img bloks
-        img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
-        del img_attn
-        img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
+        img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
+        img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
 
         # calculate the txt bloks
         txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)
-        del txt_attn
         txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt)
 
         if txt.dtype == torch.float16:
@@ -279,10 +220,6 @@ class SingleStreamBlock(nn.Module):
         num_heads: int,
         mlp_ratio: float = 4.0,
         qk_scale: float = None,
-        modulation=True,
-        mlp_silu_act=False,
-        bias=True,
-        yak_mlp=False,
         dtype=None,
         device=None,
         operations=None
@@ -294,55 +231,30 @@ class SingleStreamBlock(nn.Module):
         self.scale = qk_scale or head_dim**-0.5
 
         self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-
-        self.mlp_hidden_dim_first = self.mlp_hidden_dim
-        self.yak_mlp = yak_mlp
-        if mlp_silu_act:
-            self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2)
-            self.mlp_act = SiLUActivation()
-        else:
-            self.mlp_act = nn.GELU(approximate="tanh")
-
-        if self.yak_mlp:
-            self.mlp_hidden_dim_first *= 2
-            self.mlp_act = nn.SiLU()
-
         # qkv and mlp_in
-        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device)
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
         # proj and mlp_out
-        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, bias=bias, dtype=dtype, device=device)
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
 
         self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
 
         self.hidden_size = hidden_size
         self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
-        if modulation:
-            self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
-        else:
-            self.modulation = None
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:
-        if self.modulation:
-            mod, _ = self.modulation(vec)
-        else:
-            mod = vec
-
-        qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim_first], dim=-1)
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor:
+        mod, _ = self.modulation(vec)
+        qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        del qkv
         q, k = self.norm(q, k, v)
 
         # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
-        del q, k, v
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
-        if self.yak_mlp:
-            mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2]
-        else:
-            mlp = self.mlp_act(mlp)
-        output = self.linear2(torch.cat((attn, mlp), 2))
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
@@ -350,11 +262,11 @@ class SingleStreamBlock(nn.Module):
 
 
 class LastLayer(nn.Module):
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, bias=True, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
         super().__init__()
         self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=bias, dtype=dtype, device=device)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=bias, dtype=dtype, device=device))
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
 
     def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor:
         if vec.ndim == 2:

comfy/ldm/flux/math.py

@@ -4,16 +4,23 @@ from torch import Tensor
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
-import logging
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
+    q_shape = q.shape
+    k_shape = k.shape
+
     if pe is not None:
-        q, k = apply_rope(q, k, pe)
+        q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2)
+        k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2)
+        q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
+        k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
+
     heads = q.shape[1]
-    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
+    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
     return x
 
+
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
     if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
@@ -29,19 +36,10 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     return out.to(dtype=torch.float32, device=pos.device)
 
 
-try:
-    import comfy.quant_ops
-    apply_rope = comfy.quant_ops.ck.apply_rope
-    apply_rope1 = comfy.quant_ops.ck.apply_rope1
-except:
-    logging.warning("No comfy kitchen, using old apply_rope functions.")
-    def apply_rope1(x: Tensor, freqs_cis: Tensor):
-        x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+    xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
 
-        x_out = freqs_cis[..., 0] * x_[..., 0]
-        x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
-
-        return x_out.reshape(*x.shape).type_as(x)
-
-    def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-        return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)

comfy/ldm/flux/model.py

@@ -6,7 +6,6 @@ import torch
 from torch import Tensor, nn
 from einops import rearrange, repeat
 import comfy.ldm.common_dit
-import comfy.patcher_extension
 
 from .layers import (
     DoubleStreamBlock,
@@ -15,8 +14,6 @@ from .layers import (
     MLPEmbedder,
     SingleStreamBlock,
     timestep_embedding,
-    Modulation,
-    RMSNorm
 )
 
 @dataclass
@@ -35,14 +32,6 @@ class FluxParams:
     patch_size: int
     qkv_bias: bool
     guidance_embed: bool
-    txt_ids_dims: list
-    global_modulation: bool = False
-    mlp_silu_act: bool = False
-    ops_bias: bool = True
-    default_ref_method: str = "offset"
-    ref_index_scale: float = 1.0
-    yak_mlp: bool = False
-    txt_norm: bool = False
 
 
 class Flux(nn.Module):
@@ -68,22 +57,13 @@ class Flux(nn.Module):
         self.hidden_size = params.hidden_size
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
-        if params.vec_in_dim is not None:
-            self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
-        else:
-            self.vector_in = None
-
+        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
         self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
         )
-        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
-
-        if params.txt_norm:
-            self.txt_norm = RMSNorm(params.context_in_dim, dtype=dtype, device=device, operations=operations)
-        else:
-            self.txt_norm = None
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
 
         self.double_blocks = nn.ModuleList(
             [
@@ -92,10 +72,6 @@ class Flux(nn.Module):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
-                    modulation=params.global_modulation is False,
-                    mlp_silu_act=params.mlp_silu_act,
-                    proj_bias=params.ops_bias,
-                    yak_mlp=params.yak_mlp,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -104,30 +80,13 @@ class Flux(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations)
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
                 for _ in range(params.depth_single_blocks)
             ]
         )
 
         if final_layer:
-            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
-
-        if params.global_modulation:
-            self.double_stream_modulation_img = Modulation(
-                self.hidden_size,
-                double=True,
-                bias=False,
-                dtype=dtype, device=device, operations=operations
-            )
-            self.double_stream_modulation_txt = Modulation(
-                self.hidden_size,
-                double=True,
-                bias=False,
-                dtype=dtype, device=device, operations=operations
-            )
-            self.single_stream_modulation = Modulation(
-                self.hidden_size, double=False, bias=False, dtype=dtype, device=device, operations=operations
-            )
+            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
 
     def forward_orig(
         self,
@@ -143,7 +102,9 @@ class Flux(nn.Module):
         attn_mask: Tensor = None,
     ) -> Tensor:
 
-        patches = transformer_options.get("patches", {})
+        if y is None:
+            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
+
         patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -155,27 +116,9 @@ class Flux(nn.Module):
             if guidance is not None:
                 vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        if self.vector_in is not None:
-            if y is None:
-                y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
-            vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
-
-        if self.txt_norm is not None:
-            txt = self.txt_norm(txt)
+        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
-        vec_orig = vec
-        if self.params.global_modulation:
-            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
-
-        if "post_input" in patches:
-            for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
-                img = out["img"]
-                txt = out["txt"]
-                img_ids = out["img_ids"]
-                txt_ids = out["txt_ids"]
-
         if img_ids is not None:
             ids = torch.cat((txt_ids, img_ids), dim=1)
             pe = self.pe_embedder(ids)
@@ -183,10 +126,7 @@ class Flux(nn.Module):
             pe = None
 
         blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.double_blocks)
-        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
-            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -194,16 +134,14 @@ class Flux(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"))
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -212,61 +150,52 @@ class Flux(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
                 if i < len(control_i):
                     add = control_i[i]
                     if add is not None:
-                        img[:, :add.shape[1]] += add
+                        img += add
 
         if img.dtype == torch.float16:
             img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504)
 
         img = torch.cat((txt, img), 1)
 
-        if self.params.global_modulation:
-            vec, _ = self.single_stream_modulation(vec_orig)
-
-        transformer_options["total_blocks"] = len(self.single_blocks)
-        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
-            transformer_options["block_index"] = i
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"))
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
                 if i < len(control_o):
                     add = control_o[i]
                     if add is not None:
-                        img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add
+                        img[:, txt.shape[1] :, ...] += add
 
         img = img[:, txt.shape[1] :, ...]
 
-        img = self.final_layer(img, vec_orig)  # (N, T, patch_size ** 2 * out_channels)
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
+    def process_img(self, x, index=0, h_offset=0, w_offset=0):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
@@ -278,76 +207,38 @@ class Flux(nn.Module):
         h_offset = ((h_offset + (patch_size // 2)) // patch_size)
         w_offset = ((w_offset + (patch_size // 2)) // patch_size)
 
-        steps_h = h_len
-        steps_w = w_len
-
-        rope_options = transformer_options.get("rope_options", None)
-        if rope_options is not None:
-            h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
-            w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
-
-            index += rope_options.get("shift_t", 0.0)
-            h_offset += rope_options.get("shift_y", 0.0)
-            w_offset += rope_options.get("shift_x", 0.0)
-
-        img_ids = torch.zeros((steps_h, steps_w, len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, 0] = img_ids[:, :, 1] + index
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=torch.float32).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=torch.float32).unsqueeze(0)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
     def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
         bs, c, h_orig, w_orig = x.shape
         patch_size = self.patch_size
 
         h_len = ((h_orig + (patch_size // 2)) // patch_size)
         w_len = ((w_orig + (patch_size // 2)) // patch_size)
-        img, img_ids = self.process_img(x, transformer_options=transformer_options)
+        img, img_ids = self.process_img(x)
         img_tokens = img.shape[1]
         if ref_latents is not None:
             h = 0
             w = 0
-            index = 0
-            ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
             for ref in ref_latents:
-                if ref_latents_method == "index":
-                    index += self.params.ref_index_scale
-                    h_offset = 0
-                    w_offset = 0
-                elif ref_latents_method == "uxo":
-                    index = 0
-                    h_offset = h_len * patch_size + h
-                    w_offset = w_len * patch_size + w
-                    h += ref.shape[-2]
-                    w += ref.shape[-1]
+                h_offset = 0
+                w_offset = 0
+                if ref.shape[-2] + h > ref.shape[-1] + w:
+                    w_offset = w
                 else:
-                    index = 1
-                    h_offset = 0
-                    w_offset = 0
-                    if ref.shape[-2] + h > ref.shape[-1] + w:
-                        w_offset = w
-                    else:
-                        h_offset = h
-                    h = max(h, ref.shape[-2] + h_offset)
-                    w = max(w, ref.shape[-1] + w_offset)
+                    h_offset = h
 
-                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
+                kontext, kontext_ids = self.process_img(ref, index=1, h_offset=h_offset, w_offset=w_offset)
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+                h = max(h, ref.shape[-2] + h_offset)
+                w = max(w, ref.shape[-1] + w_offset)
 
-        txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
-
-        if len(self.params.txt_ids_dims) > 0:
-            for i in self.params.txt_ids_dims:
-                txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
-
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig]

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -109,7 +109,6 @@ class AsymmetricAttention(nn.Module):
         scale_x: torch.Tensor,  # (B, dim_x), modulation for pre-RMSNorm.
         scale_y: torch.Tensor,  # (B, dim_y), modulation for pre-RMSNorm.
         crop_y,
-        transformer_options={},
         **rope_rotation,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         rope_cos = rope_rotation.get("rope_cos")
@@ -144,7 +143,7 @@ class AsymmetricAttention(nn.Module):
 
         xy = optimized_attention(q,
                                  k,
-                                 v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)
+                                 v, self.num_heads, skip_reshape=True)
 
         x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
         x = self.proj_x(x)
@@ -225,7 +224,6 @@ class AsymmetricJointBlock(nn.Module):
         x: torch.Tensor,
         c: torch.Tensor,
         y: torch.Tensor,
-        transformer_options={},
         **attn_kwargs,
     ):
         """Forward pass of a block.
@@ -258,7 +256,6 @@ class AsymmetricJointBlock(nn.Module):
             y,
             scale_x=scale_msa_x,
             scale_y=scale_msa_y,
-            transformer_options=transformer_options,
             **attn_kwargs,
         )
 
@@ -527,11 +524,10 @@ class AsymmDiTJoint(nn.Module):
                                                     args["txt"],
                                                     rope_cos=args["rope_cos"],
                                                     rope_sin=args["rope_sin"],
-                                                    crop_y=args["num_tokens"],
-                                                    transformer_options=args["transformer_options"]
+                                                    crop_y=args["num_tokens"]
                                                     )
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
                 y_feat = out["txt"]
                 x = out["img"]
             else:
@@ -542,7 +538,6 @@ class AsymmDiTJoint(nn.Module):
                     rope_cos=rope_cos,
                     rope_sin=rope_sin,
                     crop_y=num_tokens,
-                    transformer_options=transformer_options,
                 )  # (B, M, D), (B, L, D)
         del y_feat  # Final layers don't use dense text features.
 

comfy/ldm/hidream/model.py

@@ -13,7 +13,6 @@ from comfy.ldm.flux.layers import LastLayer
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 
@@ -72,8 +71,8 @@ class TimestepEmbed(nn.Module):
         return t_emb
 
 
-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}):
-    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options)
+def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor):
+    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2])
 
 
 class HiDreamAttnProcessor_flashattn:
@@ -86,7 +85,6 @@ class HiDreamAttnProcessor_flashattn:
         image_tokens_masks: Optional[torch.FloatTensor] = None,
         text_tokens: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.FloatTensor:
@@ -134,7 +132,7 @@ class HiDreamAttnProcessor_flashattn:
             query = torch.cat([query_1, query_2], dim=-1)
             key = torch.cat([key_1, key_2], dim=-1)
 
-        hidden_states = attention(query, key, value, transformer_options=transformer_options)
+        hidden_states = attention(query, key, value)
 
         if not attn.single:
             hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)
@@ -200,7 +198,6 @@ class HiDreamAttention(nn.Module):
         image_tokens_masks: torch.FloatTensor = None,
         norm_text_tokens: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.Tensor:
         return self.processor(
             self,
@@ -208,7 +205,6 @@ class HiDreamAttention(nn.Module):
             image_tokens_masks = image_tokens_masks,
             text_tokens = norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -409,7 +405,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
+
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \
@@ -422,7 +418,6 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
             norm_image_tokens,
             image_tokens_masks,
             rope = rope,
-            transformer_options=transformer_options,
         )
         image_tokens = gate_msa_i * attn_output_i + image_tokens
 
@@ -487,7 +482,6 @@ class HiDreamImageTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \
@@ -505,7 +499,6 @@ class HiDreamImageTransformerBlock(nn.Module):
             image_tokens_masks,
             norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
         image_tokens = gate_msa_i * attn_output_i + image_tokens
@@ -556,7 +549,6 @@ class HiDreamImageBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         return self.block(
             image_tokens,
@@ -564,7 +556,6 @@ class HiDreamImageBlock(nn.Module):
             text_tokens,
             adaln_input,
             rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -701,23 +692,7 @@ class HiDreamImageTransformer2DModel(nn.Module):
             raise NotImplementedError
         return x, x_masks, img_sizes
 
-    def forward(self,
-        x: torch.Tensor,
-        t: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-        encoder_hidden_states_llama3=None,
-        image_cond=None,
-        control = None,
-        transformer_options = {},
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options)
-
-    def _forward(
+    def forward(
         self,
         x: torch.Tensor,
         t: torch.Tensor,
@@ -794,7 +769,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens = cur_encoder_hidden_states,
                 adaln_input = adaln_input,
                 rope = rope,
-                transformer_options=transformer_options,
             )
             initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len]
             block_id += 1
@@ -818,7 +792,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens=None,
                 adaln_input=adaln_input,
                 rope=rope,
-                transformer_options=transformer_options,
             )
             hidden_states = hidden_states[:, :hidden_states_seq_len]
             block_id += 1

comfy/ldm/hunyuan3d/model.py

@@ -7,7 +7,6 @@ from comfy.ldm.flux.layers import (
     SingleStreamBlock,
     timestep_embedding,
 )
-import comfy.patcher_extension
 
 
 class Hunyuan3Dv2(nn.Module):
@@ -68,13 +67,6 @@ class Hunyuan3Dv2(nn.Module):
         self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations)
 
     def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, guidance, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):
         x = x.movedim(-1, -2)
         timestep = 1.0 - timestep
         txt = context
@@ -99,16 +91,14 @@ class Hunyuan3Dv2(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args["transformer_options"])
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -117,8 +107,7 @@ class Hunyuan3Dv2(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
         img = torch.cat((txt, img), 1)
 
@@ -129,19 +118,17 @@ class Hunyuan3Dv2(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args["transformer_options"])
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
         img = img[:, txt.shape[1]:, ...]
         img = self.final_layer(img, vec)

comfy/ldm/hunyuan3d/vae.py

@@ -4,458 +4,81 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
+
+from typing import Union, Tuple, List, Callable, Optional
+
 import numpy as np
-import math
+from einops import repeat, rearrange
 from tqdm import tqdm
-
-from typing import Optional
-
 import logging
 
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True):
-
-    # manually create the pointer vector
-    assert src.size(0) == batch.numel()
-
-    batch_size = int(batch.max()) + 1
-    deg = src.new_zeros(batch_size, dtype = torch.long)
-
-    deg.scatter_add_(0, batch, torch.ones_like(batch))
-
-    ptr_vec = deg.new_zeros(batch_size + 1)
-    torch.cumsum(deg, 0, out=ptr_vec[1:])
-
-    #return fps_sampling(src, ptr_vec, ratio)
-    sampled_indicies = []
-
-    for b in range(batch_size):
-        # start and the end of each batch
-        start, end = ptr_vec[b].item(), ptr_vec[b + 1].item()
-        # points from the point cloud
-        points = src[start:end]
-
-        num_points = points.size(0)
-        num_samples = max(1, math.ceil(num_points * sampling_ratio))
-
-        selected = torch.zeros(num_samples, device = src.device, dtype = torch.long)
-        distances = torch.full((num_points,), float("inf"), device = src.device)
-
-        # select a random start point
-        if start_random:
-            farthest = torch.randint(0, num_points, (1,), device = src.device)
-        else:
-            farthest = torch.tensor([0], device = src.device, dtype = torch.long)
-
-        for i in range(num_samples):
-            selected[i] = farthest
-            centroid = points[farthest].squeeze(0)
-            dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance
-            distances = torch.minimum(distances, dist)
-            farthest = torch.argmax(distances)
-
-        sampled_indicies.append(torch.arange(start, end)[selected])
-
-    return torch.cat(sampled_indicies, dim = 0)
-class PointCrossAttention(nn.Module):
-    def __init__(self,
-        num_latents: int,
-        downsample_ratio: float,
-        pc_size: int,
-        pc_sharpedge_size: int,
-        point_feats: int,
-        width: int,
-        heads: int,
-        layers: int,
-        fourier_embedder,
-        normal_pe: bool = False,
-        qkv_bias: bool = False,
-        use_ln_post: bool = True,
-        qk_norm: bool = True):
-
-        super().__init__()
-
-        self.fourier_embedder = fourier_embedder
-
-        self.pc_size = pc_size
-        self.normal_pe = normal_pe
-        self.downsample_ratio = downsample_ratio
-        self.pc_sharpedge_size = pc_sharpedge_size
-        self.num_latents = num_latents
-        self.point_feats = point_feats
-
-        self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width)
-
-        self.cross_attn = ResidualCrossAttentionBlock(
-            width = width,
-            heads = heads,
-            qkv_bias = qkv_bias,
-            qk_norm = qk_norm
-        )
-
-        self.self_attn = None
-        if layers > 0:
-            self.self_attn = Transformer(
-                width = width,
-                heads = heads,
-                qkv_bias = qkv_bias,
-                qk_norm = qk_norm,
-                layers = layers
-            )
-
-        if use_ln_post:
-            self.ln_post = nn.LayerNorm(width)
-        else:
-            self.ln_post = None
-
-    def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor):
-
-        """
-        Subsample points randomly from the point cloud (input_pc)
-        Further sample the subsampled points to get query_pc
-        take the fourier embeddings for both input and query pc
-
-        Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc.
-        Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc).
-        More computationally efficient.
-
-        Features are additional information for each point in the cloud
-        """
-
-        B, _, D = point_cloud.shape
-
-        num_latents = int(self.num_latents)
-
-        num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents
-        num_sharpedge_query = num_latents - num_random_query
-
-        # Split random and sharpedge surface points
-        random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1)
-
-        # assert statements
-        assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size"
-        assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size"
-
-        input_random_pc_size = int(num_random_query * self.downsample_ratio)
-        random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \
-            self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size)
-
-        input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio)
-
-        if input_sharpedge_pc_size == 0:
-            sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device)
-            sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device)
-
-        else:
-            sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \
-            self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size)
-
-        # concat the random and sharpedges
-        query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1)
-        input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1)
-
-        query = self.fourier_embedder(query_pc)
-        data = self.fourier_embedder(input_pc)
-
-        if self.point_feats > 0:
-            random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1)
-
-            input_random_surface_features, query_random_features = \
-                self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B,
-                                     input_pc_size = input_random_pc_size, idx_query = random_idx_query)
-
-            if input_sharpedge_pc_size == 0:
-                input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats,
-                                                               dtype = input_random_surface_features.dtype, device = point_cloud.device)
-
-                query_sharpedge_features = torch.zeros(B, 0, self.point_feats,
-                                                       dtype = query_random_features.dtype, device = point_cloud.device)
-            else:
-
-                input_sharpedge_surface_features, query_sharpedge_features = \
-                    self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features,
-                                         batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size)
-
-            query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1)
-            input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1)
-
-            if self.normal_pe:
-                # apply the fourier embeddings on the first 3 dims (xyz)
-                input_features_pe = self.fourier_embedder(input_features[..., :3])
-                query_features_pe = self.fourier_embedder(query_features[..., :3])
-                # replace the first 3 dims with the new PE ones
-                input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1)
-                query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1)
-
-            # concat at the channels dim
-            query = torch.cat([query, query_features], dim = -1)
-            data = torch.cat([data, input_features], dim = -1)
-
-        # don't return pc_info to avoid unnecessary memory usuage
-        return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1])
-
-    def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
-
-        query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features)
-
-        # apply projections
-        query = self.input_proj(query)
-        data = self.input_proj(data)
-
-        # apply cross attention between query and data
-        latents = self.cross_attn(query, data)
-
-        if self.self_attn is not None:
-            latents = self.self_attn(latents)
-
-        if self.ln_post is not None:
-            latents = self.ln_post(latents)
-
-        return latents
-
-
-    def subsample(self, pc, num_query, input_pc_size: int):
-
-        """
-        num_query: number of points to keep after FPS
-        input_pc_size: number of points to select before FPS
-        """
-
-        B, _, D = pc.shape
-        query_ratio = num_query / input_pc_size
-
-        # random subsampling of points inside the point cloud
-        idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size]
-        input_pc = pc[:, idx_pc, :]
-
-        # flatten to allow applying fps across the whole batch
-        flattent_input_pc = input_pc.view(B * input_pc_size, D)
-
-        # construct a batch_down tensor to tell fps
-        # which points belong to which batch
-        N_down = int(flattent_input_pc.shape[0] / B)
-        batch_down = torch.arange(B).to(pc.device)
-        batch_down = torch.repeat_interleave(batch_down, N_down)
-
-        idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio)
-        query_pc = flattent_input_pc[idx_query].view(B, -1, D)
-
-        return query_pc, input_pc, idx_pc, idx_query
-
-    def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query):
-
-        B = batch_size
-
-        input_surface_features = features[:, idx_pc, :]
-        flattent_input_features = input_surface_features.view(B * input_pc_size, -1)
-        query_features = flattent_input_features[idx_query].view(B, -1,
-                                                                 flattent_input_features.shape[-1])
-
-        return input_surface_features, query_features
-
-def normalize_mesh(mesh, scale = 0.9999):
-    """Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]"""
-
-    bbox = mesh.bounds
-    center = (bbox[1] + bbox[0]) / 2
-
-    max_extent = (bbox[1] - bbox[0]).max()
-    mesh.apply_translation(-center)
-    mesh.apply_scale((2 * scale) / max_extent)
-
-    return mesh
-
-def sample_pointcloud(mesh, num = 200000):
-    """ Uniformly sample points from the surface of the mesh """
-
-    points, face_idx = mesh.sample(num, return_index = True)
-    normals = mesh.face_normals[face_idx]
-    return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32))
-
-def detect_sharp_edges(mesh, threshold=0.985):
-    """Return edge indices (a, b) that lie on sharp boundaries of the mesh."""
-
-    V, F = mesh.vertices, mesh.faces
-    VN, FN = mesh.vertex_normals, mesh.face_normals
-
-    sharp_mask = np.ones(V.shape[0])
-    for i in range(3):
-        indices = F[:, i]
-        alignment = np.einsum('ij,ij->i', VN[indices], FN)
-        dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1)
-        sharp_mask[indices] = np.min(dot_stack, axis=-1)
-
-    edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]])
-    edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]])
-    sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold)
-
-    return edge_a[sharp_edges], edge_b[sharp_edges]
-
-
-def sharp_sample_pointcloud(mesh, num = 16384):
-    """ Sample points preferentially from sharp edges in the mesh. """
-
-    edge_a, edge_b = detect_sharp_edges(mesh)
-    V, VN = mesh.vertices, mesh.vertex_normals
-
-    va, vb = V[edge_a], V[edge_b]
-    na, nb = VN[edge_a], VN[edge_b]
-
-    edge_lengths = np.linalg.norm(vb - va, axis=-1)
-    weights = edge_lengths / edge_lengths.sum()
-
-    indices = np.searchsorted(np.cumsum(weights), np.random.rand(num))
-    t = np.random.rand(num, 1)
-
-    samples = t * va[indices] + (1 - t) * vb[indices]
-    normals = t * na[indices] + (1 - t) * nb[indices]
-
-    return samples.astype(np.float32), normals.astype(np.float32)
-
-def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"):
-    """Load a surface with optional sharp-edge annotations from a trimesh mesh."""
-
-    import trimesh
-
-    try:
-        mesh_full = trimesh.util.concatenate(mesh.dump())
-    except Exception:
-        mesh_full = trimesh.util.concatenate(mesh)
-
-    mesh_full = normalize_mesh(mesh_full)
-
-    faces = mesh_full.faces
-    vertices = mesh_full.vertices
-    origin_face_count = faces.shape[0]
-
-    mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count])
-    mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:])
-
-    area_surface = mesh_surface.area
-    area_fill = mesh_fill.area
-    total_area = area_surface + area_fill
-
-    sample_num = 499712 // 2
-    fill_ratio = area_fill / total_area if total_area > 0 else 0
-
-    num_fill = int(sample_num * fill_ratio)
-    num_surface = sample_num - num_fill
-
-    surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface)
-    fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill)
-
-    sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num)
-
-    def assemble_tensor(points, normals, label=None):
-
-        data = torch.cat([points, normals], dim=1).half().to(device)
-
-        if label is not None:
-            label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device)
-            data = torch.cat([data, label_tensor], dim=1)
-
-        return data
-
-    surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0),
-                              torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0),
-                              label = 0 if sharpedge_flag else None)
-
-    sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals),
-                                    label = 1 if sharpedge_flag else None)
-
-    rng = np.random.default_rng()
-
-    surface = surface[rng.choice(surface.shape[0], num_points, replace = False)]
-    sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)]
-
-    full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0)
-
-    return full
-
-class SharpEdgeSurfaceLoader:
-    """ Load mesh surface and sharp edge samples. """
-
-    def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
-
-        self.num_uniform_points = num_uniform_points
-        self.num_sharp_points = num_sharp_points
-        self.total_points = num_uniform_points + num_sharp_points
-
-    def __call__(self, mesh_input, device = "cuda"):
-        mesh = self._load_mesh(mesh_input)
-        return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device)
-
-    @staticmethod
-    def _load_mesh(mesh_input):
-        import trimesh
-
-        if isinstance(mesh_input, str):
-            mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True)
-        else:
-            mesh = mesh_input
-
-        if isinstance(mesh, trimesh.Scene):
-            combined = None
-            for obj in mesh.geometry.values():
-                combined = obj if combined is None else combined + obj
-            return combined
-
-        return mesh
-
-class DiagonalGaussianDistribution:
-    def __init__(self, params: torch.Tensor, feature_dim: int = -1):
-
-        # divide quant channels (8) into mean and log variance
-        self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim)
-
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.std = torch.exp(0.5 * self.logvar)
-
-    def sample(self):
-
-        eps = torch.randn_like(self.std)
-        z = self.mean + eps * self.std
-
-        return z
-
-################################################
-# Volume Decoder
-################################################
-
-class VanillaVolumeDecoder():
+def generate_dense_grid_points(
+    bbox_min: np.ndarray,
+    bbox_max: np.ndarray,
+    octree_resolution: int,
+    indexing: str = "ij",
+):
+    length = bbox_max - bbox_min
+    num_cells = octree_resolution
+
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+
+
+class VanillaVolumeDecoder:
     @torch.no_grad()
-    def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01,
-                 num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs):
+    def __call__(
+        self,
+        latents: torch.FloatTensor,
+        geo_decoder: Callable,
+        bounds: Union[Tuple[float], List[float], float] = 1.01,
+        num_chunks: int = 10000,
+        octree_resolution: int = None,
+        enable_pbar: bool = True,
+        **kwargs,
+    ):
+        device = latents.device
+        dtype = latents.dtype
+        batch_size = latents.shape[0]
 
+        # 1. generate query points
         if isinstance(bounds, float):
             bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
 
-        bbox_min, bbox_max = torch.tensor(bounds[:3]), torch.tensor(bounds[3:])
-
-        x = torch.linspace(bbox_min[0], bbox_max[0], int(octree_resolution) + 1, dtype = torch.float32)
-        y = torch.linspace(bbox_min[1], bbox_max[1], int(octree_resolution) + 1, dtype = torch.float32)
-        z = torch.linspace(bbox_min[2], bbox_max[2], int(octree_resolution) + 1, dtype = torch.float32)
-
-        [xs, ys, zs] = torch.meshgrid(x, y, z, indexing = "ij")
-        xyz = torch.stack((xs, ys, zs), axis=-1).to(latents.device, dtype = latents.dtype).contiguous().reshape(-1, 3)
-        grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]
+        bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6])
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_resolution=octree_resolution,
+            indexing="ij"
+        )
+        xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3)
 
+        # 2. latents to 3d volume
         batch_logits = []
-        for start in tqdm(range(0, xyz.shape[0], num_chunks), desc="Volume Decoding",
+        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc="Volume Decoding",
                           disable=not enable_pbar):
-
-            chunk_queries = xyz[start: start + num_chunks, :]
-            chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1)
-            logits = geo_decoder(queries = chunk_queries, latents = latents)
+            chunk_queries = xyz_samples[start: start + num_chunks, :]
+            chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size)
+            logits = geo_decoder(queries=chunk_queries, latents=latents)
             batch_logits.append(logits)
 
-        grid_logits = torch.cat(batch_logits, dim = 1)
-        grid_logits = grid_logits.view((latents.shape[0], *grid_size)).float()
+        grid_logits = torch.cat(batch_logits, dim=1)
+        grid_logits = grid_logits.view((batch_size, *grid_size)).float()
 
         return grid_logits
 
+
 class FourierEmbedder(nn.Module):
     """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
     each feature dimension of `x[..., i]` into:
@@ -552,11 +175,13 @@ class FourierEmbedder(nn.Module):
         else:
             return x
 
+
 class CrossAttentionProcessor:
     def __call__(self, attn, q, k, v):
-        out = comfy.ops.scaled_dot_product_attention(q, k, v)
+        out = F.scaled_dot_product_attention(q, k, v)
         return out
 
+
 class DropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
     """
@@ -607,42 +232,39 @@ class MLP(nn.Module):
     def forward(self, x):
         return self.drop_path(self.c_proj(self.gelu(self.c_fc(x))))
 
+
 class QKVMultiheadCrossAttention(nn.Module):
     def __init__(
         self,
+        *,
         heads: int,
-        n_data = None,
         width=None,
         qk_norm=False,
         norm_layer=ops.LayerNorm
     ):
         super().__init__()
         self.heads = heads
-        self.n_data = n_data
         self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
         self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
 
-    def forward(self, q, kv):
+        self.attn_processor = CrossAttentionProcessor()
 
+    def forward(self, q, kv):
         _, n_ctx, _ = q.shape
         bs, n_data, width = kv.shape
-
         attn_ch = width // self.heads // 2
         q = q.view(bs, n_ctx, self.heads, -1)
-
         kv = kv.view(bs, n_data, self.heads, -1)
         k, v = torch.split(kv, attn_ch, dim=-1)
 
         q = self.q_norm(q)
         k = self.k_norm(k)
-
-        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
-        out = F.scaled_dot_product_attention(q, k, v)
-
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = self.attn_processor(self, q, k, v)
         out = out.transpose(1, 2).reshape(bs, n_ctx, -1)
-
         return out
 
+
 class MultiheadCrossAttention(nn.Module):
     def __init__(
         self,
@@ -684,6 +306,7 @@ class MultiheadCrossAttention(nn.Module):
         x = self.c_proj(x)
         return x
 
+
 class ResidualCrossAttentionBlock(nn.Module):
     def __init__(
         self,
@@ -743,7 +366,7 @@ class QKVMultiheadAttention(nn.Module):
         q = self.q_norm(q)
         k = self.k_norm(k)
 
-        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
         out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
         return out
 
@@ -760,7 +383,8 @@ class MultiheadAttention(nn.Module):
         drop_path_rate: float = 0.0
     ):
         super().__init__()
-
+        self.width = width
+        self.heads = heads
         self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias)
         self.c_proj = ops.Linear(width, width)
         self.attention = QKVMultiheadAttention(
@@ -867,7 +491,7 @@ class CrossAttentionDecoder(nn.Module):
         self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width)
         if self.downsample_ratio != 1:
             self.latents_proj = ops.Linear(width * downsample_ratio, width)
-        if not self.enable_ln_post:
+        if self.enable_ln_post == False:
             qk_norm = False
         self.cross_attn_decoder = ResidualCrossAttentionBlock(
             width=width,
@@ -898,44 +522,28 @@ class CrossAttentionDecoder(nn.Module):
 
 class ShapeVAE(nn.Module):
     def __init__(
-            self,
-            *,
-            num_latents: int = 4096,
-            embed_dim: int = 64,
-            width: int = 1024,
-            heads: int = 16,
-            num_decoder_layers: int = 16,
-            num_encoder_layers: int = 8,
-            pc_size: int = 81920,
-            pc_sharpedge_size: int = 0,
-            point_feats: int = 4,
-            downsample_ratio: int = 20,
-            geo_decoder_downsample_ratio: int = 1,
-            geo_decoder_mlp_expand_ratio: int = 4,
-            geo_decoder_ln_post: bool = True,
-            num_freqs: int = 8,
-            qkv_bias: bool = False,
-            qk_norm: bool = True,
-            drop_path_rate: float = 0.0,
-            include_pi: bool = False,
-            scale_factor: float = 1.0039506158752403,
-            label_type: str = "binary",
+        self,
+        *,
+        embed_dim: int,
+        width: int,
+        heads: int,
+        num_decoder_layers: int,
+        geo_decoder_downsample_ratio: int = 1,
+        geo_decoder_mlp_expand_ratio: int = 4,
+        geo_decoder_ln_post: bool = True,
+        num_freqs: int = 8,
+        include_pi: bool = True,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        label_type: str = "binary",
+        drop_path_rate: float = 0.0,
+        scale_factor: float = 1.0,
     ):
         super().__init__()
         self.geo_decoder_ln_post = geo_decoder_ln_post
 
         self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
 
-        self.encoder = PointCrossAttention(layers = num_encoder_layers,
-                                    num_latents = num_latents,
-                                    downsample_ratio = downsample_ratio,
-                                    heads = heads,
-                                    pc_size = pc_size,
-                                    width = width,
-                                    point_feats = point_feats,
-                                    fourier_embedder = self.fourier_embedder,
-                                    pc_sharpedge_size = pc_sharpedge_size)
-
         self.post_kl = ops.Linear(embed_dim, width)
 
         self.transformer = Transformer(
@@ -975,14 +583,5 @@ class ShapeVAE(nn.Module):
         grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar)
         return grid_logits.movedim(-2, -1)
 
-    def encode(self, surface):
-
-        pc, feats = surface[:, :, :3], surface[:, :, 3:]
-        latents = self.encoder(pc, feats)
-
-        moments = self.pre_kl(latents)
-        posterior = DiagonalGaussianDistribution(moments, feature_dim = -1)
-
-        latents = posterior.sample()
-
-        return latents
+    def encode(self, x):
+        return None

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -1,659 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.attention import optimized_attention
-import comfy.model_management
-
-class GELU(nn.Module):
-
-    def __init__(self, dim_in: int, dim_out: int, operations, device, dtype):
-        super().__init__()
-        self.proj = operations.Linear(dim_in, dim_out, device = device, dtype = dtype)
-
-    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
-
-        if gate.device.type == "mps":
-            return F.gelu(gate.to(dtype = torch.float32)).to(dtype = gate.dtype)
-
-        return F.gelu(gate)
-
-    def forward(self, hidden_states):
-
-        hidden_states = self.proj(hidden_states)
-        hidden_states = self.gelu(hidden_states)
-
-        return hidden_states
-
-class FeedForward(nn.Module):
-
-    def __init__(self, dim: int, dim_out = None, mult: int = 4,
-                dropout: float = 0.0, inner_dim = None, operations = None, device = None, dtype = None):
-
-        super().__init__()
-        if inner_dim is None:
-            inner_dim = int(dim * mult)
-
-        dim_out = dim_out if dim_out is not None else dim
-
-        act_fn = GELU(dim, inner_dim, operations = operations, device = device, dtype = dtype)
-
-        self.net = nn.ModuleList([])
-        self.net.append(act_fn)
-
-        self.net.append(nn.Dropout(dropout))
-        self.net.append(operations.Linear(inner_dim, dim_out, device = device, dtype = dtype))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for module in self.net:
-            hidden_states = module(hidden_states)
-        return hidden_states
-
-class AddAuxLoss(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, loss):
-        # do nothing in forward (no computation)
-        ctx.requires_aux_loss = loss.requires_grad
-        ctx.dtype = loss.dtype
-
-        return x
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        # add the aux loss gradients
-        grad_loss = None
-        # put the aux grad the same as the main grad loss
-        # aux grad contributes equally
-        if ctx.requires_aux_loss:
-            grad_loss = torch.ones(1, dtype = ctx.dtype, device = grad_output.device)
-
-        return grad_output, grad_loss
-
-class MoEGate(nn.Module):
-
-    def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01, device = None, dtype = None):
-
-        super().__init__()
-        self.top_k = num_experts_per_tok
-        self.n_routed_experts = num_experts
-
-        self.alpha = aux_loss_alpha
-
-        self.gating_dim = embed_dim
-        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), device = device, dtype = dtype))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-
-        # flatten hidden states
-        hidden_states = hidden_states.view(-1, hidden_states.size(-1))
-
-        # get logits and pass it to softmax
-        logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), bias = None)
-        scores = logits.softmax(dim = -1)
-
-        topk_weight, topk_idx = torch.topk(scores, k = self.top_k, dim = -1, sorted = False)
-
-        if self.training and self.alpha > 0.0:
-            scores_for_aux = scores
-
-            # used bincount instead of one hot encoding
-            counts = torch.bincount(topk_idx.view(-1), minlength = self.n_routed_experts).float()
-            ce = counts / topk_idx.numel()  # normalized expert usage
-
-            # mean expert score
-            Pi = scores_for_aux.mean(0)
-
-            # expert balance loss
-            aux_loss = (Pi * ce * self.n_routed_experts).sum() * self.alpha
-        else:
-            aux_loss = None
-
-        return topk_idx, topk_weight, aux_loss
-
-class MoEBlock(nn.Module):
-    def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, dropout: float = 0.0,
-                 ff_inner_dim: int = None, operations = None, device = None, dtype = None):
-        super().__init__()
-
-        self.moe_top_k = moe_top_k
-        self.num_experts = num_experts
-
-        self.experts = nn.ModuleList([
-            FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)
-            for _ in range(num_experts)
-        ])
-
-        self.gate = MoEGate(dim, num_experts = num_experts, num_experts_per_tok = moe_top_k, device = device, dtype = dtype)
-        self.shared_experts = FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)
-
-    def forward(self, hidden_states) -> torch.Tensor:
-
-        identity = hidden_states
-        orig_shape = hidden_states.shape
-        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
-
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        flat_topk_idx = topk_idx.view(-1)
-
-        if self.training:
-
-            hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim = 0)
-            y = torch.empty_like(hidden_states, dtype = hidden_states.dtype)
-
-            for i, expert in enumerate(self.experts):
-                tmp = expert(hidden_states[flat_topk_idx == i])
-                y[flat_topk_idx == i] = tmp.to(hidden_states.dtype)
-
-            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim = 1)
-            y =  y.view(*orig_shape)
-
-            y = AddAuxLoss.apply(y, aux_loss)
-        else:
-            y = self.moe_infer(hidden_states, flat_expert_indices = flat_topk_idx,flat_expert_weights = topk_weight.view(-1, 1)).view(*orig_shape)
-
-        y = y + self.shared_experts(identity)
-
-        return y
-
-    @torch.no_grad()
-    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
-
-        expert_cache = torch.zeros_like(x)
-        idxs = flat_expert_indices.argsort()
-
-        # no need for .numpy().cpu() here
-        tokens_per_expert = flat_expert_indices.bincount().cumsum(0)
-        token_idxs = idxs // self.moe_top_k
-
-        for i, end_idx in enumerate(tokens_per_expert):
-
-            start_idx = 0 if i == 0 else tokens_per_expert[i-1]
-
-            if start_idx == end_idx:
-                continue
-
-            expert = self.experts[i]
-            exp_token_idx = token_idxs[start_idx:end_idx]
-
-            expert_tokens = x[exp_token_idx]
-            expert_out = expert(expert_tokens)
-
-            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
-
-            # use index_add_ with a 1-D index tensor directly avoids building a large [N, D] index map and extra memcopy required by scatter_reduce_
-            # + avoid dtype conversion
-            expert_cache.index_add_(0, exp_token_idx, expert_out)
-
-        return expert_cache
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0,
-                 scale: float = 1.0, max_period: int = 10000):
-        super().__init__()
-
-        self.num_channels = num_channels
-        half_dim = num_channels // 2
-
-        # precompute the “inv_freq” vector once
-        exponent = -math.log(max_period) * torch.arange(
-            half_dim, dtype=torch.float32
-        ) / (half_dim - downscale_freq_shift)
-
-        inv_freq = torch.exp(exponent)
-
-        # pad
-        if num_channels % 2 == 1:
-            # we’ll pad a zero at the end of the cos-half
-            inv_freq = torch.cat([inv_freq, inv_freq.new_zeros(1)])
-
-        # register to buffer so it moves with the device
-        self.register_buffer("inv_freq", inv_freq, persistent = False)
-        self.scale = scale
-
-    def forward(self, timesteps: torch.Tensor):
-
-        x = timesteps.float().unsqueeze(1) * self.inv_freq.to(timesteps.device).unsqueeze(0)
-
-
-        # fused CUDA kernels for sin and cos
-        sin_emb = x.sin()
-        cos_emb = x.cos()
-
-        emb = torch.cat([sin_emb, cos_emb], dim = 1)
-
-        # scale factor
-        if self.scale != 1.0:
-            emb = emb * self.scale
-
-        # If we padded inv_freq for odd, emb is already wide enough; otherwise:
-        if emb.shape[1] > self.num_channels:
-            emb = emb[:, :self.num_channels]
-
-        return emb
-
-class TimestepEmbedder(nn.Module):
-    def __init__(self, hidden_size, frequency_embedding_size = 256, cond_proj_dim = None, operations = None, device = None, dtype = None):
-        super().__init__()
-
-        self.mlp = nn.Sequential(
-            operations.Linear(hidden_size, frequency_embedding_size, bias=True, device = device, dtype = dtype),
-            nn.GELU(),
-            operations.Linear(frequency_embedding_size, hidden_size, bias=True, device = device, dtype = dtype),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-        if cond_proj_dim is not None:
-            self.cond_proj = operations.Linear(cond_proj_dim, frequency_embedding_size, bias=False, device = device, dtype = dtype)
-
-        self.time_embed = Timesteps(hidden_size)
-
-    def forward(self, timesteps, condition):
-
-        timestep_embed = self.time_embed(timesteps).type(self.mlp[0].weight.dtype)
-
-        if condition is not None:
-            cond_embed = self.cond_proj(condition)
-            timestep_embed = timestep_embed + cond_embed
-
-        time_conditioned = self.mlp(timestep_embed)
-
-        # for broadcasting with image tokens
-        return time_conditioned.unsqueeze(1)
-
-class MLP(nn.Module):
-    def __init__(self, *, width: int, operations = None, device = None, dtype = None):
-        super().__init__()
-        self.width = width
-        self.fc1 = operations.Linear(width, width * 4, device = device, dtype = dtype)
-        self.fc2 = operations.Linear(width * 4, width, device = device, dtype = dtype)
-        self.gelu = nn.GELU()
-
-    def forward(self, x):
-        return self.fc2(self.gelu(self.fc1(x)))
-
-class CrossAttention(nn.Module):
-    def __init__(
-        self,
-        qdim,
-        kdim,
-        num_heads,
-        qkv_bias=True,
-        qk_norm=False,
-        norm_layer=nn.LayerNorm,
-        use_fp16: bool = False,
-        operations = None,
-        dtype = None,
-        device = None,
-        **kwargs,
-    ):
-        super().__init__()
-        self.qdim = qdim
-        self.kdim = kdim
-
-        self.num_heads = num_heads
-        self.head_dim = self.qdim // num_heads
-
-        self.scale = self.head_dim ** -0.5
-
-        self.to_q = operations.Linear(qdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-        self.to_k = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-        self.to_v = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        if norm_layer == nn.LayerNorm:
-            norm_layer = operations.LayerNorm
-        else:
-            norm_layer = operations.RMSNorm
-
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.out_proj = operations.Linear(qdim, qdim, bias=True, device = device, dtype = dtype)
-
-    def forward(self, x, y):
-
-        b, s1, _ = x.shape
-        _, s2, _ = y.shape
-
-        y = y.to(next(self.to_k.parameters()).dtype)
-
-        q = self.to_q(x)
-        k = self.to_k(y)
-        v = self.to_v(y)
-
-        kv = torch.cat((k, v), dim=-1)
-        split_size = kv.shape[-1] // self.num_heads // 2
-
-        kv = kv.view(1, -1, self.num_heads, split_size * 2)
-        k, v = torch.split(kv, split_size, dim=-1)
-
-        q = q.view(b, s1, self.num_heads, self.head_dim)
-        k = k.view(b, s2, self.num_heads, self.head_dim)
-        v = v.reshape(b, s2, self.num_heads * self.head_dim)
-
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        x = optimized_attention(
-            q.reshape(b, s1, self.num_heads * self.head_dim),
-            k.reshape(b, s2, self.num_heads * self.head_dim),
-            v,
-            heads=self.num_heads,
-        )
-
-        out = self.out_proj(x)
-
-        return out
-
-class Attention(nn.Module):
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        qkv_bias = True,
-        qk_norm = False,
-        norm_layer = nn.LayerNorm,
-        use_fp16: bool = False,
-        operations = None,
-        device = None,
-        dtype = None
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = self.dim // num_heads
-        self.scale = self.head_dim ** -0.5
-
-        self.to_q = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-        self.to_k = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-        self.to_v = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        if norm_layer == nn.LayerNorm:
-            norm_layer = operations.LayerNorm
-        else:
-            norm_layer = operations.RMSNorm
-
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.out_proj = operations.Linear(dim, dim, device = device, dtype = dtype)
-
-    def forward(self, x):
-        B, N, _ = x.shape
-
-        query = self.to_q(x)
-        key = self.to_k(x)
-        value = self.to_v(x)
-
-        qkv_combined = torch.cat((query, key, value), dim=-1)
-        split_size = qkv_combined.shape[-1] // self.num_heads // 3
-
-        qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)
-        query, key, value = torch.split(qkv, split_size, dim=-1)
-
-        query = query.reshape(B, N, self.num_heads, self.head_dim)
-        key = key.reshape(B, N, self.num_heads, self.head_dim)
-        value = value.reshape(B, N, self.num_heads * self.head_dim)
-
-        query = self.q_norm(query)
-        key = self.k_norm(key)
-
-        x = optimized_attention(
-            query.reshape(B, N, self.num_heads * self.head_dim),
-            key.reshape(B, N, self.num_heads * self.head_dim),
-            value,
-            heads=self.num_heads,
-        )
-
-        x = self.out_proj(x)
-        return x
-
-class HunYuanDiTBlock(nn.Module):
-    def __init__(
-        self,
-        hidden_size,
-        c_emb_size,
-        num_heads,
-        text_states_dim=1024,
-        qk_norm=False,
-        norm_layer=nn.LayerNorm,
-        qk_norm_layer=True,
-        qkv_bias=True,
-        skip_connection=True,
-        timested_modulate=False,
-        use_moe: bool = False,
-        num_experts: int = 8,
-        moe_top_k: int = 2,
-        use_fp16: bool = False,
-        operations = None,
-        device = None, dtype = None
-    ):
-        super().__init__()
-
-        # eps can't be 1e-6 in fp16 mode because of numerical stability issues
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        self.norm1 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm,
-                               norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations)
-
-        self.norm2 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        self.timested_modulate = timested_modulate
-        if self.timested_modulate:
-            self.default_modulation = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(c_emb_size, hidden_size, bias=True, device = device, dtype = dtype)
-            )
-
-        self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias,
-                                    qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16,
-                                    device = device, dtype = dtype, operations = operations)
-
-        self.norm3 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        if skip_connection:
-            self.skip_norm = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-            self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, device = device, dtype = dtype)
-        else:
-            self.skip_linear = None
-
-        self.use_moe = use_moe
-
-        if self.use_moe:
-            self.moe = MoEBlock(
-                hidden_size,
-                num_experts = num_experts,
-                moe_top_k = moe_top_k,
-                dropout = 0.0,
-                ff_inner_dim = int(hidden_size * 4.0),
-                device = device, dtype = dtype,
-                operations = operations
-            )
-        else:
-            self.mlp = MLP(width=hidden_size, operations=operations, device = device, dtype = dtype)
-
-    def forward(self, hidden_states, conditioning=None, text_states=None, skip_tensor=None):
-
-        if self.skip_linear is not None:
-            combined = torch.cat([skip_tensor, hidden_states], dim=-1)
-            hidden_states = self.skip_linear(combined)
-            hidden_states = self.skip_norm(hidden_states)
-
-        # self attention
-        if self.timested_modulate:
-            modulation_shift = self.default_modulation(conditioning).unsqueeze(dim=1)
-            hidden_states = hidden_states + modulation_shift
-
-        self_attn_out = self.attn1(self.norm1(hidden_states))
-        hidden_states = hidden_states + self_attn_out
-
-        # cross attention
-        hidden_states = hidden_states + self.attn2(self.norm2(hidden_states), text_states)
-
-        # MLP Layer
-        mlp_input = self.norm3(hidden_states)
-
-        if self.use_moe:
-            hidden_states = hidden_states + self.moe(mlp_input)
-        else:
-            hidden_states = hidden_states + self.mlp(mlp_input)
-
-        return hidden_states
-
-class FinalLayer(nn.Module):
-
-    def __init__(self, final_hidden_size, out_channels, operations, use_fp16: bool = False, device = None, dtype = None):
-        super().__init__()
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-        self.linear = operations.Linear(final_hidden_size, out_channels, bias = True, device = device, dtype = dtype)
-
-    def forward(self, x):
-        x = self.norm_final(x)
-        x = x[:, 1:]
-        x = self.linear(x)
-        return x
-
-class HunYuanDiTPlain(nn.Module):
-
-    # init with the defaults values from https://huggingface.co/tencent/Hunyuan3D-2.1/blob/main/hunyuan3d-dit-v2-1/config.yaml
-    def __init__(
-        self,
-        in_channels: int = 64,
-        hidden_size: int = 2048,
-        context_dim: int = 1024,
-        depth: int = 21,
-        num_heads: int = 16,
-        qk_norm: bool = True,
-        qkv_bias: bool = False,
-        num_moe_layers: int = 6,
-        guidance_cond_proj_dim = 2048,
-        norm_type = 'layer',
-        num_experts: int = 8,
-        moe_top_k: int = 2,
-        use_fp16: bool = False,
-        dtype = None,
-        device = None,
-        operations = None,
-        **kwargs
-        ):
-
-        self.dtype = dtype
-
-        super().__init__()
-
-        self.depth = depth
-
-        self.in_channels = in_channels
-        self.out_channels = in_channels
-
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-
-        norm = operations.LayerNorm if norm_type == 'layer' else operations.RMSNorm
-        qk_norm = operations.RMSNorm
-
-        self.context_dim = context_dim
-        self.guidance_cond_proj_dim = guidance_cond_proj_dim
-
-        self.x_embedder = operations.Linear(in_channels, hidden_size, bias = True, device = device, dtype = dtype)
-        self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim = guidance_cond_proj_dim, device = device, dtype = dtype, operations = operations)
-
-
-        # HUnYuanDiT Blocks
-        self.blocks = nn.ModuleList([
-            HunYuanDiTBlock(hidden_size=hidden_size,
-                            c_emb_size=hidden_size,
-                            num_heads=num_heads,
-                            text_states_dim=context_dim,
-                            qk_norm=qk_norm,
-                            norm_layer = norm,
-                            qk_norm_layer = qk_norm,
-                            skip_connection=layer > depth // 2,
-                            qkv_bias=qkv_bias,
-                            use_moe=True if depth - layer <= num_moe_layers else False,
-                            num_experts=num_experts,
-                            moe_top_k=moe_top_k,
-                            use_fp16 = use_fp16,
-                            device = device, dtype = dtype, operations = operations)
-            for layer in range(depth)
-        ])
-
-        self.depth = depth
-
-        self.final_layer = FinalLayer(hidden_size, self.out_channels, use_fp16 = use_fp16, operations = operations, device = device, dtype = dtype)
-
-    def forward(self, x, t, context, transformer_options = {}, **kwargs):
-
-        x = x.movedim(-1, -2)
-        uncond_emb, cond_emb = context.chunk(2, dim = 0)
-
-        context = torch.cat([cond_emb, uncond_emb], dim = 0)
-        main_condition = context
-
-        t = 1.0 - t
-
-        time_embedded = self.t_embedder(t, condition = kwargs.get('guidance_cond'))
-
-        x = x.to(dtype = next(self.x_embedder.parameters()).dtype)
-        x_embedded = self.x_embedder(x)
-
-        combined = torch.cat([time_embedded, x_embedded], dim=1)
-
-        def block_wrap(args):
-            return block(
-                args["x"],
-                args["t"],
-                args["cond"],
-                skip_tensor=args.get("skip"),)
-
-        skip_stack = []
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        for idx, block in enumerate(self.blocks):
-            if idx <= self.depth // 2:
-                skip_input = None
-            else:
-                skip_input = skip_stack.pop()
-
-            if ("block", idx) in blocks_replace:
-
-                combined = blocks_replace[("block", idx)](
-                    {
-                        "x": combined,
-                        "t": time_embedded,
-                        "cond": main_condition,
-                        "skip": skip_input,
-                    },
-                    {"original_block": block_wrap},
-                )
-            else:
-                combined = block(combined, time_embedded, main_condition, skip_tensor=skip_input)
-
-            if idx < self.depth // 2:
-                skip_stack.append(combined)
-
-        output = self.final_layer(combined)
-        output =  output.movedim(-2, -1) * (-1.0)
-
-        cond_emb, uncond_emb = output.chunk(2, dim = 0)
-        return torch.cat([uncond_emb, cond_emb])

comfy/ldm/hunyuan_video/model.py

@@ -1,11 +1,11 @@
 #Based on Flux code because of weird hunyuan video code license.
 
 import torch
-import comfy.patcher_extension
 import comfy.ldm.flux.layers
 import comfy.ldm.modules.diffusionmodules.mmdit
 from comfy.ldm.modules.attention import optimized_attention
 
+
 from dataclasses import dataclass
 from einops import repeat
 
@@ -39,11 +39,6 @@ class HunyuanVideoParams:
     patch_size: list
     qkv_bias: bool
     guidance_embed: bool
-    byt5: bool
-    meanflow: bool
-    use_cond_type_embedding: bool
-    vision_in_dim: int
-    meanflow_sum: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -82,13 +77,13 @@ class TokenRefinerBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)
 
         norm_x = self.norm1(x)
         qkv = self.self_attn.qkv(norm_x)
         q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
-        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)
+        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True)
 
         x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
         x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
@@ -119,14 +114,14 @@ class IndividualTokenRefiner(nn.Module):
             ]
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         m = None
         if mask is not None:
             m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
             m = m + m.transpose(2, 3)
 
         for block in self.blocks:
-            x = block(x, c, m, transformer_options=transformer_options)
+            x = block(x, c, m)
         return x
 
 
@@ -154,45 +149,17 @@ class TokenRefiner(nn.Module):
         x,
         timesteps,
         mask,
-        transformer_options={},
     ):
         t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
         # m = mask.float().unsqueeze(-1)
         # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise
-        if x.dtype == torch.float16:
-            c = x.float().sum(dim=1) / x.shape[1]
-        else:
-            c = x.sum(dim=1) / x.shape[1]
+        c = x.sum(dim=1) / x.shape[1]
 
         c = t + self.c_embedder(c.to(x.dtype))
         x = self.input_embedder(x)
-        x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
+        x = self.individual_token_refiner(x, c, mask)
         return x
 
-
-class ByT5Mapper(nn.Module):
-    def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device)
-        self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device)
-        self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
-        self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device)
-        self.use_res = use_res
-        self.act_fn = nn.GELU()
-
-    def forward(self, x):
-        if self.use_res:
-            res = x
-        x = self.layernorm(x)
-        x = self.fc1(x)
-        x = self.act_fn(x)
-        x = self.fc2(x)
-        x2 = self.act_fn(x)
-        x2 = self.fc3(x2)
-        if self.use_res:
-            x2 = x2 + res
-        return x2
-
 class HunyuanVideo(nn.Module):
     """
     Transformer model for flow matching on sequences.
@@ -201,15 +168,11 @@ class HunyuanVideo(nn.Module):
     def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
         super().__init__()
         self.dtype = dtype
-        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
-
         params = HunyuanVideoParams(**kwargs)
         self.params = params
         self.patch_size = params.patch_size
         self.in_channels = params.in_channels
         self.out_channels = params.out_channels
-        self.use_cond_type_embedding = params.use_cond_type_embedding
-        self.vision_in_dim = params.vision_in_dim
         if params.hidden_size % params.num_heads != 0:
             raise ValueError(
                 f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
@@ -221,13 +184,9 @@ class HunyuanVideo(nn.Module):
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
 
-        self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations)
+        self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, dtype=dtype, device=device, operations=operations)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
-        if params.vec_in_dim is not None:
-            self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
-        else:
-            self.vector_in = None
-
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
         self.guidance_in = (
             MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
         )
@@ -255,38 +214,9 @@ class HunyuanVideo(nn.Module):
             ]
         )
 
-        if params.byt5:
-            self.byt5_in = ByT5Mapper(
-                in_dim=1472,
-                out_dim=2048,
-                hidden_dim=2048,
-                out_dim1=self.hidden_size,
-                use_res=False,
-                dtype=dtype, device=device, operations=operations
-            )
-        else:
-            self.byt5_in = None
-
-        if params.meanflow:
-            self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
-        else:
-            self.time_r_in = None
-
         if final_layer:
             self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
 
-        # HunyuanVideo 1.5 specific modules
-        if self.vision_in_dim is not None:
-            from comfy.ldm.wan.model import MLPProj
-            self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings)
-        else:
-            self.vision_in = None
-        if self.use_cond_type_embedding:
-            # 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature
-            self.cond_type_embedding = nn.Embedding(3, self.hidden_size)
-        else:
-            self.cond_type_embedding = None
-
     def forward_orig(
         self,
         img: Tensor,
@@ -295,13 +225,10 @@ class HunyuanVideo(nn.Module):
         txt_ids: Tensor,
         txt_mask: Tensor,
         timesteps: Tensor,
-        y: Tensor = None,
-        txt_byt5=None,
-        clip_fea=None,
+        y: Tensor,
         guidance: Tensor = None,
         guiding_frame_index=None,
         ref_latent=None,
-        disable_time_r=False,
         control=None,
         transformer_options={},
     ) -> Tensor:
@@ -312,14 +239,6 @@ class HunyuanVideo(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
 
-        if (self.time_r_in is not None) and (not disable_time_r):
-            w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0]  # This most likely could be improved
-            if len(w) > 0:
-                timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
-                timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
-                vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
-                vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2
-
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)
             ref_latent = self.img_in(ref_latent)
@@ -330,17 +249,13 @@ class HunyuanVideo(nn.Module):
 
         if guiding_frame_index is not None:
             token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))
-            if self.vector_in is not None:
-                vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
-                vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
-            else:
-                vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1)
+            vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
+            vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
             frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2])
             modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)]
             modulation_dims_txt = [(0, None, 1)]
         else:
-            if self.vector_in is not None:
-                vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+            vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
             modulation_dims = None
             modulation_dims_txt = None
 
@@ -351,32 +266,7 @@ class HunyuanVideo(nn.Module):
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
 
-        txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)
-
-        if self.cond_type_embedding is not None:
-            self.cond_type_embedding.to(txt.device)
-            cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long))
-            txt = txt + cond_emb.to(txt.dtype)
-
-        if self.byt5_in is not None and txt_byt5 is not None:
-            txt_byt5 = self.byt5_in(txt_byt5)
-            if self.cond_type_embedding is not None:
-                cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long))
-                txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype)
-                txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5
-            else:
-                txt = torch.cat((txt, txt_byt5), dim=1)
-            txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
-            txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1)
-
-        if clip_fea is not None:
-            txt_vision_states = self.vision_in(clip_fea)
-            if self.cond_type_embedding is not None:
-                cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device))
-                txt_vision_states = txt_vision_states + cond_emb
-            txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1)
-            extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
-            txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1)
+        txt = self.txt_in(txt, timesteps, txt_mask)
 
         ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
@@ -390,21 +280,18 @@ class HunyuanVideo(nn.Module):
             attn_mask = None
 
         blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.double_blocks)
-        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
-            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"])
+                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
             else:
-                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -415,20 +302,17 @@ class HunyuanVideo(nn.Module):
 
         img = torch.cat((img, txt), 1)
 
-        transformer_options["total_blocks"] = len(self.single_blocks)
-        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
-            transformer_options["block_index"] = i
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"])
                     return out
 
-                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -443,16 +327,12 @@ class HunyuanVideo(nn.Module):
 
         img = self.final_layer(img, vec, modulation_dims=modulation_dims)  # (N, T, patch_size ** 2 * out_channels)
 
-        shape = initial_shape[-len(self.patch_size):]
+        shape = initial_shape[-3:]
         for i in range(len(shape)):
             shape[i] = shape[i] // self.patch_size[i]
         img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
-        if img.ndim == 8:
-            img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
-            img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
-        else:
-            img = img.permute(0, 3, 1, 4, 2, 5)
-            img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3])
+        img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
+        img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
     def img_ids(self, x):
@@ -467,30 +347,9 @@ class HunyuanVideo(nn.Module):
         img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
         return repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
 
-    def img_ids_2d(self, x):
-        bs, c, h, w = x.shape
-        patch_size = self.patch_size
-        h_len = ((h + (patch_size[0] // 2)) // patch_size[0])
-        w_len = ((w + (patch_size[1] // 2)) // patch_size[1])
-        img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        return repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
-        bs = x.shape[0]
-        if len(self.patch_size) == 3:
-            img_ids = self.img_ids(x)
-            txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        else:
-            img_ids = self.img_ids_2d(x)
-            txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+        bs, c, t, h, w = x.shape
+        img_ids = self.img_ids(x)
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options)
         return out

comfy/ldm/hunyuan_video/upsampler.py

@@ -1,122 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
-from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
-import comfy.model_management
-import comfy.model_patcher
-
-class SRResidualCausalBlock3D(nn.Module):
-    def __init__(self, channels: int):
-        super().__init__()
-        self.block = nn.Sequential(
-            VideoConv3d(channels, channels, kernel_size=3),
-            nn.SiLU(inplace=True),
-            VideoConv3d(channels, channels, kernel_size=3),
-            nn.SiLU(inplace=True),
-            VideoConv3d(channels, channels, kernel_size=3),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return x + self.block(x)
-
-class SRModel3DV2(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        hidden_channels: int = 64,
-        num_blocks: int = 6,
-        global_residual: bool = False,
-    ):
-        super().__init__()
-        self.in_conv = VideoConv3d(in_channels, hidden_channels, kernel_size=3)
-        self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)])
-        self.out_conv = VideoConv3d(hidden_channels, out_channels, kernel_size=3)
-        self.global_residual = bool(global_residual)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        residual = x
-        y = self.in_conv(x)
-        for blk in self.blocks:
-            y = blk(y)
-        y = self.out_conv(y)
-        if self.global_residual and (y.shape == residual.shape):
-            y = y + residual
-        return y
-
-
-class Upsampler(nn.Module):
-    def __init__(
-        self,
-        z_channels: int,
-        out_channels: int,
-        block_out_channels: tuple[int, ...],
-        num_res_blocks: int = 2,
-    ):
-        super().__init__()
-        self.num_res_blocks = num_res_blocks
-        self.block_out_channels = block_out_channels
-        self.z_channels = z_channels
-
-        ch = block_out_channels[0]
-        self.conv_in = VideoConv3d(z_channels, ch, kernel_size=3)
-
-        self.up = nn.ModuleList()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                    out_channels=tgt,
-                                                    temb_channels=0,
-                                                    conv_shortcut=False,
-                                                    conv_op=VideoConv3d, norm_op=RMS_norm)
-                                        for j in range(num_res_blocks + 1)])
-            ch = tgt
-            self.up.append(stage)
-
-        self.norm_out = RMS_norm(ch)
-        self.conv_out = VideoConv3d(ch, out_channels, kernel_size=3)
-
-    def forward(self, z):
-        """
-        Args:
-            z: (B, C, T, H, W)
-            target_shape: (H, W)
-        """
-        # z to block_in
-        repeats = self.block_out_channels[0] // (self.z_channels)
-        x = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)
-
-        # upsampling
-        for stage in self.up:
-            for blk in stage.block:
-                x = blk(x)
-
-        out = self.conv_out(F.silu(self.norm_out(x)))
-        return out
-
-UPSAMPLERS = {
-    "720p": SRModel3DV2,
-    "1080p": Upsampler,
-}
-
-class HunyuanVideo15SRModel():
-    def __init__(self, model_type, config):
-        self.load_device = comfy.model_management.vae_device()
-        offload_device = comfy.model_management.vae_offload_device()
-        self.dtype = comfy.model_management.vae_dtype(self.load_device)
-        self.model_class = UPSAMPLERS.get(model_type)
-        self.model = self.model_class(**config).eval()
-
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=True, assign=self.patcher.is_dynamic())
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def resample_latent(self, latent):
-        comfy.model_management.load_model_gpu(self.patcher)
-        return self.model(latent.to(self.load_device))

comfy/ldm/hunyuan_video/vae.py

@@ -1,136 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-
-class PixelShuffle2D(nn.Module):
-    def __init__(self, in_dim, out_dim, op=ops.Conv2d):
-        super().__init__()
-        self.conv = op(in_dim, out_dim >> 2, 3, 1, 1)
-        self.ratio = (in_dim << 2) // out_dim
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        h2, w2 = h >> 1, w >> 1
-        y = self.conv(x).view(b, -1, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, -1, h2, w2)
-        r = x.view(b, c, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, c << 2, h2, w2)
-        return y + r.view(b, y.shape[1], self.ratio, h2, w2).mean(2)
-
-
-class PixelUnshuffle2D(nn.Module):
-    def __init__(self, in_dim, out_dim, op=ops.Conv2d):
-        super().__init__()
-        self.conv = op(in_dim, out_dim << 2, 3, 1, 1)
-        self.scale = (out_dim << 2) // in_dim
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        h2, w2 = h << 1, w << 1
-        y = self.conv(x).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)
-        r = x.repeat_interleave(self.scale, 1).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)
-        return y + r
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, downsample_match_channel=True, **_):
-        super().__init__()
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.conv_in = ops.Conv2d(in_channels, block_out_channels[0], 3, 1, 1)
-
-        self.down = nn.ModuleList()
-        ch = block_out_channels[0]
-        depth = (ffactor_spatial >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=ops.Conv2d)
-                                        for j in range(num_res_blocks)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
-                stage.downsample = PixelShuffle2D(ch, nxt, ops.Conv2d)
-                ch = nxt
-            self.down.append(stage)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-
-        self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)
-        self.conv_out = ops.Conv2d(ch, z_channels << 1, 3, 1, 1)
-
-    def forward(self, x):
-        x = self.conv_in(x)
-
-        for stage in self.down:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'downsample'):
-                x = stage.downsample(x)
-
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        b, c, h, w = x.shape
-        grp = c // (self.z_channels << 1)
-        skip = x.view(b, c // grp, grp, h, w).mean(2)
-
-        return self.conv_out(F.silu(self.norm_out(x))) + skip
-
-
-class Decoder(nn.Module):
-    def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, upsample_match_channel=True, **_):
-        super().__init__()
-        block_out_channels = block_out_channels[::-1]
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-
-        ch = block_out_channels[0]
-        self.conv_in = ops.Conv2d(z_channels, ch, 3, 1, 1)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-
-        self.up = nn.ModuleList()
-        depth = (ffactor_spatial >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=ops.Conv2d)
-                                        for j in range(num_res_blocks + 1)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
-                stage.upsample = PixelUnshuffle2D(ch, nxt, ops.Conv2d)
-                ch = nxt
-            self.up.append(stage)
-
-        self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)
-        self.conv_out = ops.Conv2d(ch, out_channels, 3, 1, 1)
-
-    def forward(self, z):
-        x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        for stage in self.up:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'upsample'):
-                x = stage.upsample(x)
-
-        return self.conv_out(F.silu(self.norm_out(x)))

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,313 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed
-import comfy.ops
-import comfy.ldm.models.autoencoder
-import comfy.model_management
-ops = comfy.ops.disable_weight_init
-
-
-class RMS_norm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        shape = (dim, 1, 1, 1)
-        self.scale = dim**0.5
-        self.gamma = nn.Parameter(torch.empty(shape))
-
-    def forward(self, x):
-        return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
-
-class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds, refiner_vae, op):
-        super().__init__()
-        fct = 2 * 2 * 2 if tds else 1 * 2 * 2
-        assert oc % fct == 0
-        self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1)
-        self.refiner_vae = refiner_vae
-
-        self.tds = tds
-        self.gs = fct * ic // oc
-
-    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
-        r1 = 2 if self.tds else 1
-        h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
-
-        if self.tds and self.refiner_vae and conv_carry_in is None:
-
-            hf = h[:, :, :1, :, :]
-            b, c, f, ht, wd = hf.shape
-            hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
-            hf = hf.permute(0, 4, 6, 1, 2, 3, 5)
-            hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
-            hf = torch.cat([hf, hf], dim=1)
-
-            h = h[:, :, 1:, :, :]
-
-            xf = x[:, :, :1, :, :]
-            b, ci, f, ht, wd = xf.shape
-            xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2)
-            xf = xf.permute(0, 4, 6, 1, 2, 3, 5)
-            xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)
-            B, C, T, H, W = xf.shape
-            xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2)
-
-            x = x[:, :, 1:, :, :]
-
-        if h.shape[2] == 0:
-            return hf + xf
-
-        b, c, frms, ht, wd = h.shape
-        nf = frms // r1
-        h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
-        h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
-        h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
-
-        b, ci, frms, ht, wd = x.shape
-        nf = frms // r1
-        x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
-        x = x.permute(0, 3, 5, 7, 1, 2, 4, 6)
-        x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
-        B, C, T, H, W = x.shape
-        x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
-
-        if self.tds and self.refiner_vae and conv_carry_in is None:
-            h = torch.cat([hf, h], dim=2)
-            x = torch.cat([xf, x], dim=2)
-
-        return h + x
-
-
-class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus, refiner_vae, op):
-        super().__init__()
-        fct = 2 * 2 * 2 if tus else 1 * 2 * 2
-        self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
-        self.refiner_vae = refiner_vae
-
-        self.tus = tus
-        self.rp = fct * oc // ic
-
-    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
-        r1 = 2 if self.tus else 1
-        h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
-
-        if self.tus and self.refiner_vae and conv_carry_in is None:
-            hf = h[:, :, :1, :, :]
-            b, c, f, ht, wd = hf.shape
-            nc = c // (2 * 2)
-            hf = hf.reshape(b, 2, 2, nc, f, ht, wd)
-            hf = hf.permute(0, 3, 4, 5, 1, 6, 2)
-            hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
-            hf = hf[:, : hf.shape[1] // 2]
-
-            h = h[:, :, 1:, :, :]
-
-            xf = x[:, :, :1, :, :]
-            b, ci, f, ht, wd = xf.shape
-            xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1)
-            b, c, f, ht, wd = xf.shape
-            nc = c // (2 * 2)
-            xf = xf.reshape(b, 2, 2, nc, f, ht, wd)
-            xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
-            xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
-
-            x = x[:, :, 1:, :, :]
-
-        b, c, frms, ht, wd = h.shape
-        nc = c // (r1 * 2 * 2)
-        h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-        h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
-        h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-        x = x.repeat_interleave(repeats=self.rp, dim=1)
-        b, c, frms, ht, wd = x.shape
-        nc = c // (r1 * 2 * 2)
-        x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-        x = x.permute(0, 4, 5, 1, 6, 2, 7, 3)
-        x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-        if self.tus and self.refiner_vae and conv_carry_in is None:
-            h = torch.cat([hf, h], dim=2)
-            x = torch.cat([xf, x], dim=2)
-
-        return h + x
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
-        super().__init__()
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.ffactor_temporal = ffactor_temporal
-
-        self.refiner_vae = refiner_vae
-        if self.refiner_vae:
-            conv_op = CarriedConv3d
-            norm_op = RMS_norm
-        else:
-            conv_op = ops.Conv3d
-            norm_op = Normalize
-
-        self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1)
-
-        self.down = nn.ModuleList()
-        ch = block_out_channels[0]
-        depth = (ffactor_spatial >> 1).bit_length()
-        depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=conv_op, norm_op=norm_op)
-                                        for j in range(num_res_blocks)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
-                stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
-                ch = nxt
-            self.down.append(stage)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
-
-        self.norm_out = norm_op(ch)
-        self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
-
-        self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer()
-
-    def forward(self, x):
-        if not self.refiner_vae and x.shape[2] == 1:
-            x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
-
-        if self.refiner_vae:
-            xl = [x[:, :, :1, :, :]]
-            if x.shape[2] > self.ffactor_temporal:
-                xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2)
-            x = xl
-        else:
-            x = [x]
-        out = []
-
-        conv_carry_in = None
-
-        for i, x1 in enumerate(x):
-            conv_carry_out = []
-            if i == len(x) - 1:
-                conv_carry_out = None
-
-            x1 = [ x1 ]
-            x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
-
-            for stage in self.down:
-                for blk in stage.block:
-                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
-                if hasattr(stage, 'downsample'):
-                    x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
-
-            out.append(x1)
-            conv_carry_in = conv_carry_out
-
-        out = torch_cat_if_needed(out, dim=2)
-
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
-        del out
-
-        b, c, t, h, w = x.shape
-        grp = c // (self.z_channels << 1)
-        skip = x.view(b, c // grp, grp, t, h, w).mean(2)
-
-        out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip
-
-        if self.refiner_vae:
-            out = self.regul(out)[0]
-
-        return out
-
-class Decoder(nn.Module):
-    def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_):
-        super().__init__()
-        block_out_channels = block_out_channels[::-1]
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-
-        self.refiner_vae = refiner_vae
-        if self.refiner_vae:
-            conv_op = CarriedConv3d
-            norm_op = RMS_norm
-        else:
-            conv_op = ops.Conv3d
-            norm_op = Normalize
-
-        ch = block_out_channels[0]
-        self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
-
-        self.up = nn.ModuleList()
-        depth = (ffactor_spatial >> 1).bit_length()
-        depth_temporal = (ffactor_temporal >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=conv_op, norm_op=norm_op)
-                                        for j in range(num_res_blocks + 1)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
-                stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
-                ch = nxt
-            self.up.append(stage)
-
-        self.norm_out = norm_op(ch)
-        self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
-
-    def forward(self, z):
-        x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        if self.refiner_vae:
-            x = torch.split(x, 2, dim=2)
-        else:
-            x = [ x ]
-        out = []
-
-        conv_carry_in = None
-
-        for i, x1 in enumerate(x):
-            conv_carry_out = []
-            if i == len(x) - 1:
-                conv_carry_out = None
-            for stage in self.up:
-                for blk in stage.block:
-                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
-                if hasattr(stage, 'upsample'):
-                    x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
-
-            x1 = [ F.silu(self.norm_out(x1)) ]
-            x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out)
-            out.append(x1)
-            conv_carry_in = conv_carry_out
-        del x
-
-        out = torch_cat_if_needed(out, dim=2)
-
-        if not self.refiner_vae:
-            if z.shape[-3] == 1:
-                out = out[:, :, -1:]
-
-        return out
-

comfy/ldm/kandinsky5/model.py

@@ -1,413 +0,0 @@
-import torch
-from torch import nn
-import math
-
-import comfy.ldm.common_dit
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.flux.math import apply_rope1
-from comfy.ldm.flux.layers import EmbedND
-
-def attention(q, k, v, heads, transformer_options={}):
-    return optimized_attention(
-        q.transpose(1, 2),
-        k.transpose(1, 2),
-        v.transpose(1, 2),
-        heads=heads,
-        skip_reshape=True,
-        transformer_options=transformer_options
-    )
-
-def apply_scale_shift_norm(norm, x, scale, shift):
-    return torch.addcmul(shift, norm(x), scale + 1.0)
-
-def apply_gate_sum(x, out, gate):
-    return torch.addcmul(x, gate, out)
-
-def get_shift_scale_gate(params):
-    shift, scale, gate = torch.chunk(params, 3, dim=-1)
-    return tuple(x.unsqueeze(1) for x in (shift, scale, gate))
-
-def get_freqs(dim, max_period=10000.0):
-    return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim)
-
-
-class TimeEmbeddings(nn.Module):
-    def __init__(self, model_dim, time_dim, max_period=10000.0, operation_settings=None):
-        super().__init__()
-        assert model_dim % 2 == 0
-        self.model_dim = model_dim
-        self.max_period = max_period
-        self.register_buffer("freqs", get_freqs(model_dim // 2, max_period), persistent=False)
-        operations = operation_settings.get("operations")
-        self.in_layer = operations.Linear(model_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.activation = nn.SiLU()
-        self.out_layer = operations.Linear(time_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-    def forward(self, timestep, dtype):
-        args = torch.outer(timestep, self.freqs.to(device=timestep.device))
-        time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype)
-        time_embed = self.out_layer(self.activation(self.in_layer(time_embed)))
-        return time_embed
-
-
-class TextEmbeddings(nn.Module):
-    def __init__(self, text_dim, model_dim, operation_settings=None):
-        super().__init__()
-        operations = operation_settings.get("operations")
-        self.in_layer = operations.Linear(text_dim, model_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.norm = operations.LayerNorm(model_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-    def forward(self, text_embed):
-        text_embed = self.in_layer(text_embed)
-        return self.norm(text_embed).type_as(text_embed)
-
-
-class VisualEmbeddings(nn.Module):
-    def __init__(self, visual_dim, model_dim, patch_size, operation_settings=None):
-        super().__init__()
-        self.patch_size = patch_size
-        operations = operation_settings.get("operations")
-        self.in_layer = operations.Linear(visual_dim, model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-    def forward(self, x):
-        x = x.movedim(1, -1)  # B C T H W -> B T H W C
-        B, T, H, W, dim = x.shape
-        pt, ph, pw = self.patch_size
-
-        x = x.view(
-            B,
-            T // pt, pt,
-            H // ph, ph,
-            W // pw, pw,
-            dim,
-        ).permute(0, 1, 3, 5, 2, 4, 6, 7).flatten(4, 7)
-
-        return self.in_layer(x)
-
-
-class Modulation(nn.Module):
-    def __init__(self, time_dim, model_dim, num_params, operation_settings=None):
-        super().__init__()
-        self.activation = nn.SiLU()
-        self.out_layer = operation_settings.get("operations").Linear(time_dim, num_params * model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-    def forward(self, x):
-        return self.out_layer(self.activation(x))
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, num_channels, head_dim, operation_settings=None):
-        super().__init__()
-        assert num_channels % head_dim == 0
-        self.num_heads = num_channels // head_dim
-        self.head_dim = head_dim
-
-        operations = operation_settings.get("operations")
-        self.to_query = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.to_key = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.to_value = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.query_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.key_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-        self.out_layer = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.num_chunks = 2
-
-    def _compute_qk(self, x, freqs, proj_fn, norm_fn):
-        result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1)
-        return apply_rope1(norm_fn(result), freqs)
-
-    def _forward(self, x, freqs, transformer_options={}):
-        q = self._compute_qk(x, freqs, self.to_query, self.query_norm)
-        k = self._compute_qk(x, freqs, self.to_key, self.key_norm)
-        v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
-        out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
-        return self.out_layer(out)
-
-    def _forward_chunked(self, x, freqs, transformer_options={}):
-        def process_chunks(proj_fn, norm_fn):
-            x_chunks = torch.chunk(x, self.num_chunks, dim=1)
-            freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1)
-            chunks = []
-            for x_chunk, freqs_chunk in zip(x_chunks, freqs_chunks):
-                chunks.append(self._compute_qk(x_chunk, freqs_chunk, proj_fn, norm_fn))
-            return torch.cat(chunks, dim=1)
-
-        q = process_chunks(self.to_query, self.query_norm)
-        k = process_chunks(self.to_key, self.key_norm)
-        v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
-        out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
-        return self.out_layer(out)
-
-    def forward(self, x, freqs, transformer_options={}):
-        if x.shape[1] > 8192:
-            return self._forward_chunked(x, freqs, transformer_options=transformer_options)
-        else:
-            return self._forward(x, freqs, transformer_options=transformer_options)
-
-
-class CrossAttention(SelfAttention):
-    def get_qkv(self, x, context):
-        q = self.to_query(x).view(*x.shape[:-1], self.num_heads, -1)
-        k = self.to_key(context).view(*context.shape[:-1], self.num_heads, -1)
-        v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1)
-        return q, k, v
-
-    def forward(self, x, context, transformer_options={}):
-        q, k, v = self.get_qkv(x, context)
-        out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options)
-        return self.out_layer(out)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, ff_dim, operation_settings=None):
-        super().__init__()
-        operations = operation_settings.get("operations")
-        self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.activation = nn.GELU()
-        self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.num_chunks = 4
-
-    def _forward(self, x):
-        return self.out_layer(self.activation(self.in_layer(x)))
-
-    def _forward_chunked(self, x):
-        chunks = torch.chunk(x, self.num_chunks, dim=1)
-        output_chunks = []
-        for chunk in chunks:
-            output_chunks.append(self._forward(chunk))
-        return torch.cat(output_chunks, dim=1)
-
-    def forward(self, x):
-        if x.shape[1] > 8192:
-            return self._forward_chunked(x)
-        else:
-            return self._forward(x)
-
-
-class OutLayer(nn.Module):
-    def __init__(self, model_dim, time_dim, visual_dim, patch_size, operation_settings=None):
-        super().__init__()
-        self.patch_size = patch_size
-        self.modulation = Modulation(time_dim, model_dim, 2, operation_settings=operation_settings)
-        operations = operation_settings.get("operations")
-        self.norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.out_layer = operations.Linear(model_dim, math.prod(patch_size) * visual_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-
-    def forward(self, visual_embed, time_embed):
-        B, T, H, W, _ = visual_embed.shape
-        shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1)
-        scale = scale[:, None, None, None, :]
-        shift = shift[:, None, None, None, :]
-        visual_embed = apply_scale_shift_norm(self.norm, visual_embed, scale, shift)
-        x = self.out_layer(visual_embed)
-
-        out_dim = x.shape[-1] // (self.patch_size[0] * self.patch_size[1] * self.patch_size[2])
-        x = x.view(
-            B, T, H, W,
-            out_dim,
-            self.patch_size[0], self.patch_size[1], self.patch_size[2]
-        )
-        return x.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(2, 3).flatten(3, 4).flatten(4, 5)
-
-
-class TransformerEncoderBlock(nn.Module):
-    def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
-        super().__init__()
-        self.text_modulation = Modulation(time_dim, model_dim, 6, operation_settings=operation_settings)
-        operations = operation_settings.get("operations")
-
-        self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
-
-        self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
-
-    def forward(self, x, time_embed, freqs, transformer_options={}):
-        self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1)
-        shift, scale, gate = get_shift_scale_gate(self_attn_params)
-        out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift)
-        out = self.self_attention(out, freqs, transformer_options=transformer_options)
-        x = apply_gate_sum(x, out, gate)
-
-        shift, scale, gate = get_shift_scale_gate(ff_params)
-        out = apply_scale_shift_norm(self.feed_forward_norm, x, scale, shift)
-        out = self.feed_forward(out)
-        x = apply_gate_sum(x, out, gate)
-        return x
-
-
-class TransformerDecoderBlock(nn.Module):
-    def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
-        super().__init__()
-        self.visual_modulation = Modulation(time_dim, model_dim, 9, operation_settings=operation_settings)
-
-        operations = operation_settings.get("operations")
-        self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
-
-        self.cross_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.cross_attention = CrossAttention(model_dim, head_dim, operation_settings=operation_settings)
-
-        self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
-
-    def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}):
-        self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1)
-        # self attention
-        shift, scale, gate = get_shift_scale_gate(self_attn_params)
-        visual_out = apply_scale_shift_norm(self.self_attention_norm, visual_embed, scale, shift)
-        visual_out = self.self_attention(visual_out, freqs, transformer_options=transformer_options)
-        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
-        # cross attention
-        shift, scale, gate = get_shift_scale_gate(cross_attn_params)
-        visual_out = apply_scale_shift_norm(self.cross_attention_norm, visual_embed, scale, shift)
-        visual_out = self.cross_attention(visual_out, text_embed, transformer_options=transformer_options)
-        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
-        # feed forward
-        shift, scale, gate = get_shift_scale_gate(ff_params)
-        visual_out = apply_scale_shift_norm(self.feed_forward_norm, visual_embed, scale, shift)
-        visual_out = self.feed_forward(visual_out)
-        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
-        return visual_embed
-
-
-class Kandinsky5(nn.Module):
-    def __init__(
-        self,
-        in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,
-        model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,
-        axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),
-        dtype=None, device=None, operations=None, **kwargs
-    ):
-        super().__init__()
-        head_dim = sum(axes_dims)
-        self.rope_scale_factor = rope_scale_factor
-        self.in_visual_dim = in_visual_dim
-        self.model_dim = model_dim
-        self.patch_size = patch_size
-        self.visual_embed_dim = visual_embed_dim
-        self.dtype = dtype
-        self.device = device
-        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
-
-        self.time_embeddings = TimeEmbeddings(model_dim, time_dim, operation_settings=operation_settings)
-        self.text_embeddings = TextEmbeddings(in_text_dim, model_dim, operation_settings=operation_settings)
-        self.pooled_text_embeddings = TextEmbeddings(in_text_dim2, time_dim, operation_settings=operation_settings)
-        self.visual_embeddings = VisualEmbeddings(visual_embed_dim, model_dim, patch_size, operation_settings=operation_settings)
-
-        self.text_transformer_blocks = nn.ModuleList(
-            [TransformerEncoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_text_blocks)]
-        )
-
-        self.visual_transformer_blocks = nn.ModuleList(
-            [TransformerDecoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_visual_blocks)]
-        )
-
-        self.out_layer = OutLayer(model_dim, time_dim, out_visual_dim, patch_size, operation_settings=operation_settings)
-
-        self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims)
-        self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim])
-
-    def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}):
-        steps = seq_len if steps is None else steps
-        seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype)
-        seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0)  # Shape: (1, steps, 1)
-        freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2)
-        return freqs
-
-    def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
-
-        patch_size = self.patch_size
-        t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
-        h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
-        w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
-
-        if steps_t is None:
-            steps_t = t_len
-        if steps_h is None:
-            steps_h = h_len
-        if steps_w is None:
-            steps_w = w_len
-
-        h_start = 0
-        w_start = 0
-        rope_options = transformer_options.get("rope_options", None)
-        if rope_options is not None:
-            t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
-            h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
-            w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
-
-            t_start += rope_options.get("shift_t", 0.0)
-            h_start += rope_options.get("shift_y", 0.0)
-            w_start += rope_options.get("shift_x", 0.0)
-        else:
-            rope_scale_factor = self.rope_scale_factor
-            if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions
-                if h * w >= 14080:
-                    rope_scale_factor = (1.0, 3.16, 3.16)
-
-            t_len = (t_len - 1.0) / rope_scale_factor[0] + 1.0
-            h_len = (h_len - 1.0) / rope_scale_factor[1] + 1.0
-            w_len = (w_len - 1.0) / rope_scale_factor[2] + 1.0
-
-        img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
-        img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
-        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
-        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
-        img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
-
-        freqs = self.rope_embedder_3d(img_ids).movedim(1, 2)
-        return freqs
-
-    def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs):
-        patches_replace = transformer_options.get("patches_replace", {})
-        context = self.text_embeddings(context)
-        time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y)
-
-        for block in self.text_transformer_blocks:
-            context = block(context, time_embed, freqs_text, transformer_options=transformer_options)
-
-        visual_embed = self.visual_embeddings(x)
-        visual_shape = visual_embed.shape[:-1]
-        visual_embed = visual_embed.flatten(1, -2)
-
-        blocks_replace = patches_replace.get("dit", {})
-        transformer_options["total_blocks"] = len(self.visual_transformer_blocks)
-        transformer_options["block_type"] = "double"
-        for i, block in enumerate(self.visual_transformer_blocks):
-            transformer_options["block_index"] = i
-            if ("double_block", i) in blocks_replace:
-                def block_wrap(args):
-                    return block(x=args["x"], context=args["context"], time_embed=args["time_embed"], freqs=args["freqs"], transformer_options=args.get("transformer_options"))
-                visual_embed = blocks_replace[("double_block", i)]({"x": visual_embed, "context": context, "time_embed": time_embed, "freqs": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})["x"]
-            else:
-                visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options)
-
-        visual_embed = visual_embed.reshape(*visual_shape, -1)
-        return self.out_layer(visual_embed, time_embed)
-
-    def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
-        original_dims = x.ndim
-        if original_dims == 4:
-            x = x.unsqueeze(2)
-        bs, c, t_len, h, w = x.shape
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
-
-        if time_dim_replace is not None:
-            time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size)
-            x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace
-
-        freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
-        freqs_text = self.rope_encode_1d(context.shape[1], device=x.device, dtype=x.dtype, transformer_options=transformer_options)
-
-        out = self.forward_orig(x, timestep, context, y, freqs, freqs_text, transformer_options=transformer_options, **kwargs)
-        if original_dims == 4:
-            out = out.squeeze(2)
-        return out
-
-    def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs)

comfy/ldm/lightricks/av_model.py

@@ -1,871 +0,0 @@
-from typing import Tuple
-import torch
-import torch.nn as nn
-from comfy.ldm.lightricks.model import (
-    CrossAttention,
-    FeedForward,
-    AdaLayerNormSingle,
-    PixArtAlphaTextProjection,
-    LTXVModel,
-)
-from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
-import comfy.ldm.common_dit
-
-class CompressedTimestep:
-    """Store video timestep embeddings in compressed form using per-frame indexing."""
-    __slots__ = ('data', 'batch_size', 'num_frames', 'patches_per_frame', 'feature_dim')
-
-    def __init__(self, tensor: torch.Tensor, patches_per_frame: int):
-        """
-        tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame
-        patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression
-        """
-        self.batch_size, num_tokens, self.feature_dim = tensor.shape
-
-        # Check if compression is valid (num_tokens must be divisible by patches_per_frame)
-        if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame:
-            self.patches_per_frame = patches_per_frame
-            self.num_frames = num_tokens // patches_per_frame
-
-            # Reshape to [batch, frames, patches_per_frame, feature_dim] and store one value per frame
-            # All patches in a frame are identical, so we only keep the first one
-            reshaped = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim)
-            self.data = reshaped[:, :, 0, :].contiguous()  # [batch, frames, feature_dim]
-        else:
-            # Not divisible or too small - store directly without compression
-            self.patches_per_frame = 1
-            self.num_frames = num_tokens
-            self.data = tensor
-
-    def expand(self):
-        """Expand back to original tensor."""
-        if self.patches_per_frame == 1:
-            return self.data
-
-        # [batch, frames, feature_dim] -> [batch, frames, patches_per_frame, feature_dim] -> [batch, tokens, feature_dim]
-        expanded = self.data.unsqueeze(2).expand(self.batch_size, self.num_frames, self.patches_per_frame, self.feature_dim)
-        return expanded.reshape(self.batch_size, -1, self.feature_dim)
-
-    def expand_for_computation(self, scale_shift_table: torch.Tensor, batch_size: int, indices: slice = slice(None, None)):
-        """Compute ada values on compressed per-frame data, then expand spatially."""
-        num_ada_params = scale_shift_table.shape[0]
-
-        # No compression - compute directly
-        if self.patches_per_frame == 1:
-            num_tokens = self.data.shape[1]
-            dim_per_param = self.feature_dim // num_ada_params
-            reshaped = self.data.reshape(batch_size, num_tokens, num_ada_params, dim_per_param)[:, :, indices, :]
-            table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=self.data.device, dtype=self.data.dtype)
-            ada_values = (table_values + reshaped).unbind(dim=2)
-            return ada_values
-
-        # Compressed: compute on per-frame data then expand spatially
-        # Reshape: [batch, frames, feature_dim] -> [batch, frames, num_ada_params, dim_per_param]
-        frame_reshaped = self.data.reshape(batch_size, self.num_frames, num_ada_params, -1)[:, :, indices, :]
-        table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(
-            device=self.data.device, dtype=self.data.dtype
-        )
-        frame_ada = (table_values + frame_reshaped).unbind(dim=2)
-
-        # Expand each ada parameter spatially: [batch, frames, dim] -> [batch, frames, patches, dim] -> [batch, tokens, dim]
-        return tuple(
-            frame_val.unsqueeze(2).expand(batch_size, self.num_frames, self.patches_per_frame, -1)
-            .reshape(batch_size, -1, frame_val.shape[-1])
-            for frame_val in frame_ada
-        )
-
-class BasicAVTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        v_dim,
-        a_dim,
-        v_heads,
-        a_heads,
-        vd_head,
-        ad_head,
-        v_context_dim=None,
-        a_context_dim=None,
-        attn_precision=None,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-
-        self.attn_precision = attn_precision
-
-        self.attn1 = CrossAttention(
-            query_dim=v_dim,
-            heads=v_heads,
-            dim_head=vd_head,
-            context_dim=None,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-        self.audio_attn1 = CrossAttention(
-            query_dim=a_dim,
-            heads=a_heads,
-            dim_head=ad_head,
-            context_dim=None,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.attn2 = CrossAttention(
-            query_dim=v_dim,
-            context_dim=v_context_dim,
-            heads=v_heads,
-            dim_head=vd_head,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-        self.audio_attn2 = CrossAttention(
-            query_dim=a_dim,
-            context_dim=a_context_dim,
-            heads=a_heads,
-            dim_head=ad_head,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        # Q: Video, K,V: Audio
-        self.audio_to_video_attn = CrossAttention(
-            query_dim=v_dim,
-            context_dim=a_dim,
-            heads=a_heads,
-            dim_head=ad_head,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        # Q: Audio, K,V: Video
-        self.video_to_audio_attn = CrossAttention(
-            query_dim=a_dim,
-            context_dim=v_dim,
-            heads=a_heads,
-            dim_head=ad_head,
-            attn_precision=self.attn_precision,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.ff = FeedForward(
-            v_dim, dim_out=v_dim, glu=True, dtype=dtype, device=device, operations=operations
-        )
-        self.audio_ff = FeedForward(
-            a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations
-        )
-
-        self.scale_shift_table = nn.Parameter(torch.empty(6, v_dim, device=device, dtype=dtype))
-        self.audio_scale_shift_table = nn.Parameter(
-            torch.empty(6, a_dim, device=device, dtype=dtype)
-        )
-
-        self.scale_shift_table_a2v_ca_audio = nn.Parameter(
-            torch.empty(5, a_dim, device=device, dtype=dtype)
-        )
-        self.scale_shift_table_a2v_ca_video = nn.Parameter(
-            torch.empty(5, v_dim, device=device, dtype=dtype)
-        )
-
-    def get_ada_values(
-        self, scale_shift_table: torch.Tensor, batch_size: int, timestep: torch.Tensor, indices: slice = slice(None, None)
-    ):
-        if isinstance(timestep, CompressedTimestep):
-            return timestep.expand_for_computation(scale_shift_table, batch_size, indices)
-
-        num_ada_params = scale_shift_table.shape[0]
-
-        ada_values = (
-            scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=timestep.device, dtype=timestep.dtype)
-            + timestep.reshape(batch_size, timestep.shape[1], num_ada_params, -1)[:, :, indices, :]
-        ).unbind(dim=2)
-        return ada_values
-
-    def get_av_ca_ada_values(
-        self,
-        scale_shift_table: torch.Tensor,
-        batch_size: int,
-        scale_shift_timestep: torch.Tensor,
-        gate_timestep: torch.Tensor,
-        num_scale_shift_values: int = 4,
-    ):
-        scale_shift_ada_values = self.get_ada_values(
-            scale_shift_table[:num_scale_shift_values, :],
-            batch_size,
-            scale_shift_timestep,
-        )
-        gate_ada_values = self.get_ada_values(
-            scale_shift_table[num_scale_shift_values:, :],
-            batch_size,
-            gate_timestep,
-        )
-
-        return (*scale_shift_ada_values, *gate_ada_values)
-
-    def forward(
-        self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,
-        v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
-        v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        run_vx = transformer_options.get("run_vx", True)
-        run_ax = transformer_options.get("run_ax", True)
-
-        vx, ax = x
-        run_ax = run_ax and ax.numel() > 0
-        run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0
-        run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True)
-
-        # video
-        if run_vx:
-            # video self-attention
-            vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2)))
-            norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa
-            del vshift_msa, vscale_msa
-            attn1_out = self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options)
-            del norm_vx
-            # video cross-attention
-            vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
-            vx.addcmul_(attn1_out, vgate_msa)
-            del vgate_msa, attn1_out
-            vx.add_(self.attn2(comfy.ldm.common_dit.rms_norm(vx), context=v_context, mask=attention_mask, transformer_options=transformer_options))
-
-        # audio
-        if run_ax:
-            # audio self-attention
-            ashift_msa, ascale_msa = (self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 2)))
-            norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa
-            del ashift_msa, ascale_msa
-            attn1_out = self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
-            del norm_ax
-            # audio cross-attention
-            agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]
-            ax.addcmul_(attn1_out, agate_msa)
-            del agate_msa, attn1_out
-            ax.add_(self.audio_attn2(comfy.ldm.common_dit.rms_norm(ax), context=a_context, mask=attention_mask, transformer_options=transformer_options))
-
-        # video - audio cross attention.
-        if run_a2v or run_v2a:
-            vx_norm3 = comfy.ldm.common_dit.rms_norm(vx)
-            ax_norm3 = comfy.ldm.common_dit.rms_norm(ax)
-
-            # audio to video cross attention
-            if run_a2v:
-                scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v = self.get_ada_values(
-                    self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[:2]
-                scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v = self.get_ada_values(
-                    self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[:2]
-
-                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v_v) + shift_ca_video_hidden_states_a2v_v
-                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v
-                del scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v, scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v
-
-                a2v_out = self.audio_to_video_attn(vx_scaled, context=ax_scaled, pe=v_cross_pe, k_pe=a_cross_pe, transformer_options=transformer_options)
-                del vx_scaled, ax_scaled
-
-                gate_out_a2v = self.get_ada_values(self.scale_shift_table_a2v_ca_video[4:, :], vx.shape[0], v_cross_gate_timestep)[0]
-                vx.addcmul_(a2v_out, gate_out_a2v)
-                del gate_out_a2v, a2v_out
-
-            # video to audio cross attention
-            if run_v2a:
-                scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a = self.get_ada_values(
-                    self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[2:4]
-                scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a = self.get_ada_values(
-                    self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[2:4]
-
-                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a
-                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a
-                del scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a, scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a
-
-                v2a_out = self.video_to_audio_attn(ax_scaled, context=vx_scaled, pe=a_cross_pe, k_pe=v_cross_pe, transformer_options=transformer_options)
-                del ax_scaled, vx_scaled
-
-                gate_out_v2a = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[4:, :], ax.shape[0], a_cross_gate_timestep)[0]
-                ax.addcmul_(v2a_out, gate_out_v2a)
-                del gate_out_v2a, v2a_out
-
-            del vx_norm3, ax_norm3
-
-        # video feedforward
-        if run_vx:
-            vshift_mlp, vscale_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, 5))
-            vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp
-            del vshift_mlp, vscale_mlp
-
-            ff_out = self.ff(vx_scaled)
-            del vx_scaled
-
-            vgate_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(5, 6))[0]
-            vx.addcmul_(ff_out, vgate_mlp)
-            del vgate_mlp, ff_out
-
-        # audio feedforward
-        if run_ax:
-            ashift_mlp, ascale_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, 5))
-            ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp
-            del ashift_mlp, ascale_mlp
-
-            ff_out = self.audio_ff(ax_scaled)
-            del ax_scaled
-
-            agate_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(5, 6))[0]
-            ax.addcmul_(ff_out, agate_mlp)
-            del agate_mlp, ff_out
-
-        return vx, ax
-
-
-class LTXAVModel(LTXVModel):
-    """LTXAV model for audio-video generation."""
-
-    def __init__(
-        self,
-        in_channels=128,
-        audio_in_channels=128,
-        cross_attention_dim=4096,
-        audio_cross_attention_dim=2048,
-        attention_head_dim=128,
-        audio_attention_head_dim=64,
-        num_attention_heads=32,
-        audio_num_attention_heads=32,
-        caption_channels=3840,
-        num_layers=48,
-        positional_embedding_theta=10000.0,
-        positional_embedding_max_pos=[20, 2048, 2048],
-        audio_positional_embedding_max_pos=[20],
-        causal_temporal_positioning=False,
-        vae_scale_factors=(8, 32, 32),
-        use_middle_indices_grid=False,
-        timestep_scale_multiplier=1000.0,
-        av_ca_timestep_scale_multiplier=1.0,
-        dtype=None,
-        device=None,
-        operations=None,
-        **kwargs,
-    ):
-        # Store audio-specific parameters
-        self.audio_in_channels = audio_in_channels
-        self.audio_cross_attention_dim = audio_cross_attention_dim
-        self.audio_attention_head_dim = audio_attention_head_dim
-        self.audio_num_attention_heads = audio_num_attention_heads
-        self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos
-
-        # Calculate audio dimensions
-        self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim
-        self.audio_out_channels = audio_in_channels
-
-        # Audio-specific constants
-        self.num_audio_channels = 8
-        self.audio_frequency_bins = 16
-
-        self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier
-
-        super().__init__(
-            in_channels=in_channels,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            num_attention_heads=num_attention_heads,
-            caption_channels=caption_channels,
-            num_layers=num_layers,
-            positional_embedding_theta=positional_embedding_theta,
-            positional_embedding_max_pos=positional_embedding_max_pos,
-            causal_temporal_positioning=causal_temporal_positioning,
-            vae_scale_factors=vae_scale_factors,
-            use_middle_indices_grid=use_middle_indices_grid,
-            timestep_scale_multiplier=timestep_scale_multiplier,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-            **kwargs,
-        )
-
-    def _init_model_components(self, device, dtype, **kwargs):
-        """Initialize LTXAV-specific components."""
-        # Audio-specific projections
-        self.audio_patchify_proj = self.operations.Linear(
-            self.audio_in_channels, self.audio_inner_dim, bias=True, dtype=dtype, device=device
-        )
-
-        # Audio-specific AdaLN
-        self.audio_adaln_single = AdaLayerNormSingle(
-            self.audio_inner_dim,
-            use_additional_conditions=False,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-
-        num_scale_shift_values = 4
-        self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(
-            self.inner_dim,
-            use_additional_conditions=False,
-            embedding_coefficient=num_scale_shift_values,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-        self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle(
-            self.inner_dim,
-            use_additional_conditions=False,
-            embedding_coefficient=1,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-        self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle(
-            self.audio_inner_dim,
-            use_additional_conditions=False,
-            embedding_coefficient=num_scale_shift_values,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-        self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle(
-            self.audio_inner_dim,
-            use_additional_conditions=False,
-            embedding_coefficient=1,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-
-        # Audio caption projection
-        self.audio_caption_projection = PixArtAlphaTextProjection(
-            in_features=self.caption_channels,
-            hidden_size=self.audio_inner_dim,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
-
-    def _init_transformer_blocks(self, device, dtype, **kwargs):
-        """Initialize transformer blocks for LTXAV."""
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicAVTransformerBlock(
-                    v_dim=self.inner_dim,
-                    a_dim=self.audio_inner_dim,
-                    v_heads=self.num_attention_heads,
-                    a_heads=self.audio_num_attention_heads,
-                    vd_head=self.attention_head_dim,
-                    ad_head=self.audio_attention_head_dim,
-                    v_context_dim=self.cross_attention_dim,
-                    a_context_dim=self.audio_cross_attention_dim,
-                    dtype=dtype,
-                    device=device,
-                    operations=self.operations,
-                )
-                for _ in range(self.num_layers)
-            ]
-        )
-
-    def _init_output_components(self, device, dtype):
-        """Initialize output components for LTXAV."""
-        # Video output components
-        super()._init_output_components(device, dtype)
-        # Audio output components
-        self.audio_scale_shift_table = nn.Parameter(
-            torch.empty(2, self.audio_inner_dim, dtype=dtype, device=device)
-        )
-        self.audio_norm_out = self.operations.LayerNorm(
-            self.audio_inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
-        )
-        self.audio_proj_out = self.operations.Linear(
-            self.audio_inner_dim, self.audio_out_channels, dtype=dtype, device=device
-        )
-        self.a_patchifier = AudioPatchifier(1, start_end=True)
-
-    def separate_audio_and_video_latents(self, x, audio_length):
-        """Separate audio and video latents from combined input."""
-        # vx = x[:, : self.in_channels]
-        # ax = x[:, self.in_channels :]
-        #
-        # ax = ax.reshape(ax.shape[0], -1)
-        # ax = ax[:, : audio_length * self.num_audio_channels * self.audio_frequency_bins]
-        #
-        # ax = ax.reshape(
-        #     ax.shape[0], self.num_audio_channels, audio_length, self.audio_frequency_bins
-        # )
-
-        vx = x[0]
-        ax = x[1] if len(x) > 1 else torch.zeros(
-            (vx.shape[0], self.num_audio_channels, 0, self.audio_frequency_bins),
-            device=vx.device, dtype=vx.dtype
-        )
-        return vx, ax
-
-    def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):
-        if ax.numel() == 0:
-            return vx
-        else:
-            return [vx, ax]
-        """Recombine audio and video latents for output."""
-        # if ax.device != vx.device or ax.dtype != vx.dtype:
-        #     logging.warning("Audio and video latents are on different devices or dtypes.")
-        #     ax = ax.to(device=vx.device, dtype=vx.dtype)
-        #     logging.warning(f"Audio audio latent moved to device: {ax.device}, dtype: {ax.dtype}")
-        #
-        # ax = ax.reshape(ax.shape[0], -1)
-        # # pad to f x h x w of the video latents
-        # divisor = vx.shape[-1] * vx.shape[-2] * vx.shape[-3]
-        # if target_shape is None:
-        #     repetitions = math.ceil(ax.shape[-1] / divisor)
-        # else:
-        #     repetitions = target_shape[1] - vx.shape[1]
-        # padded_len = repetitions * divisor
-        # ax = F.pad(ax, (0, padded_len - ax.shape[-1]))
-        # ax = ax.reshape(ax.shape[0], -1, vx.shape[-3], vx.shape[-2], vx.shape[-1])
-        # return torch.cat([vx, ax], dim=1)
-
-    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
-        """Process input for LTXAV - separate audio and video, then patchify."""
-        audio_length = kwargs.get("audio_length", 0)
-        # Separate audio and video latents
-        vx, ax = self.separate_audio_and_video_latents(x, audio_length)
-
-        has_spatial_mask = False
-        if denoise_mask is not None:
-            # check if any frame has spatial variation (inpainting)
-            for frame_idx in range(denoise_mask.shape[2]):
-                frame_mask = denoise_mask[0, 0, frame_idx]
-                if frame_mask.numel() > 0 and frame_mask.min() != frame_mask.max():
-                    has_spatial_mask = True
-                    break
-
-        [vx, v_pixel_coords, additional_args] = super()._process_input(
-            vx, keyframe_idxs, denoise_mask, **kwargs
-        )
-        additional_args["has_spatial_mask"] = has_spatial_mask
-
-        ax, a_latent_coords = self.a_patchifier.patchify(ax)
-        ax = self.audio_patchify_proj(ax)
-
-        # additional_args.update({"av_orig_shape": list(x.shape)})
-        return [vx, ax], [v_pixel_coords, a_latent_coords], additional_args
-
-    def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
-        """Prepare timestep embeddings."""
-        # TODO: some code reuse is needed here.
-        grid_mask = kwargs.get("grid_mask", None)
-        if grid_mask is not None:
-            timestep = timestep[:, grid_mask]
-
-        timestep_scaled = timestep * self.timestep_scale_multiplier
-
-        v_timestep, v_embedded_timestep = self.adaln_single(
-            timestep_scaled.flatten(),
-            {"resolution": None, "aspect_ratio": None},
-            batch_size=batch_size,
-            hidden_dtype=hidden_dtype,
-        )
-
-        # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width]
-        # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width
-        orig_shape = kwargs.get("orig_shape")
-        has_spatial_mask = kwargs.get("has_spatial_mask", None)
-        v_patches_per_frame = None
-        if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5:
-            # orig_shape[3] = height, orig_shape[4] = width (in latent space)
-            v_patches_per_frame = orig_shape[3] * orig_shape[4]
-
-        # Reshape to [batch_size, num_tokens, dim] and compress for storage
-        v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame)
-        v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame)
-
-        # Prepare audio timestep
-        a_timestep = kwargs.get("a_timestep")
-        if a_timestep is not None:
-            a_timestep_scaled = a_timestep * self.timestep_scale_multiplier
-            a_timestep_flat = a_timestep_scaled.flatten()
-            timestep_flat = timestep_scaled.flatten()
-            av_ca_factor = self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier
-
-            # Cross-attention timesteps - compress these too
-            av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(
-                a_timestep_flat,
-                {"resolution": None, "aspect_ratio": None},
-                batch_size=batch_size,
-                hidden_dtype=hidden_dtype,
-            )
-            av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single(
-                timestep_flat,
-                {"resolution": None, "aspect_ratio": None},
-                batch_size=batch_size,
-                hidden_dtype=hidden_dtype,
-            )
-            av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single(
-                timestep_flat * av_ca_factor,
-                {"resolution": None, "aspect_ratio": None},
-                batch_size=batch_size,
-                hidden_dtype=hidden_dtype,
-            )
-            av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single(
-                a_timestep_flat * av_ca_factor,
-                {"resolution": None, "aspect_ratio": None},
-                batch_size=batch_size,
-                hidden_dtype=hidden_dtype,
-            )
-
-            # Compress cross-attention timesteps (only video side, audio is too small to benefit)
-            # v_patches_per_frame is None for spatial masks, set for temporal masks or no mask
-            cross_av_timestep_ss = [
-                av_ca_audio_scale_shift_timestep.view(batch_size, -1, av_ca_audio_scale_shift_timestep.shape[-1]),
-                CompressedTimestep(av_ca_video_scale_shift_timestep.view(batch_size, -1, av_ca_video_scale_shift_timestep.shape[-1]), v_patches_per_frame),  # video - compressed if possible
-                CompressedTimestep(av_ca_a2v_gate_noise_timestep.view(batch_size, -1, av_ca_a2v_gate_noise_timestep.shape[-1]), v_patches_per_frame),  # video - compressed if possible
-                av_ca_v2a_gate_noise_timestep.view(batch_size, -1, av_ca_v2a_gate_noise_timestep.shape[-1]),
-            ]
-
-            a_timestep, a_embedded_timestep = self.audio_adaln_single(
-                a_timestep_flat,
-                {"resolution": None, "aspect_ratio": None},
-                batch_size=batch_size,
-                hidden_dtype=hidden_dtype,
-            )
-            # Audio timesteps
-            a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1])
-            a_embedded_timestep = a_embedded_timestep.view(batch_size, -1, a_embedded_timestep.shape[-1])
-        else:
-            a_timestep = timestep_scaled
-            a_embedded_timestep = kwargs.get("embedded_timestep")
-            cross_av_timestep_ss = []
-
-        return [v_timestep, a_timestep, cross_av_timestep_ss], [
-            v_embedded_timestep,
-            a_embedded_timestep,
-        ]
-
-    def _prepare_context(self, context, batch_size, x, attention_mask=None):
-        vx = x[0]
-        ax = x[1]
-        v_context, a_context = torch.split(
-            context, int(context.shape[-1] / 2), len(context.shape) - 1
-        )
-
-        v_context, attention_mask = super()._prepare_context(
-            v_context, batch_size, vx, attention_mask
-        )
-        if self.audio_caption_projection is not None:
-            a_context = self.audio_caption_projection(a_context)
-            a_context = a_context.view(batch_size, -1, ax.shape[-1])
-
-        return [v_context, a_context], attention_mask
-
-    def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
-        v_pixel_coords = pixel_coords[0]
-        v_pe = super()._prepare_positional_embeddings(v_pixel_coords, frame_rate, x_dtype)
-
-        a_latent_coords = pixel_coords[1]
-        a_pe = self._precompute_freqs_cis(
-            a_latent_coords,
-            dim=self.audio_inner_dim,
-            out_dtype=x_dtype,
-            max_pos=self.audio_positional_embedding_max_pos,
-            use_middle_indices_grid=self.use_middle_indices_grid,
-            num_attention_heads=self.audio_num_attention_heads,
-        )
-
-        # calculate positional embeddings for the middle of the token duration, to use in av cross attention layers.
-        max_pos = max(
-            self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0]
-        )
-        v_pixel_coords = v_pixel_coords.to(torch.float32)
-        v_pixel_coords[:, 0] = v_pixel_coords[:, 0] * (1.0 / frame_rate)
-        av_cross_video_freq_cis = self._precompute_freqs_cis(
-            v_pixel_coords[:, 0:1, :],
-            dim=self.audio_cross_attention_dim,
-            out_dtype=x_dtype,
-            max_pos=[max_pos],
-            use_middle_indices_grid=True,
-            num_attention_heads=self.audio_num_attention_heads,
-        )
-        av_cross_audio_freq_cis = self._precompute_freqs_cis(
-            a_latent_coords[:, 0:1, :],
-            dim=self.audio_cross_attention_dim,
-            out_dtype=x_dtype,
-            max_pos=[max_pos],
-            use_middle_indices_grid=True,
-            num_attention_heads=self.audio_num_attention_heads,
-        )
-
-        return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)]
-
-    def _process_transformer_blocks(
-        self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs
-    ):
-        vx = x[0]
-        ax = x[1]
-        v_context = context[0]
-        a_context = context[1]
-        v_timestep = timestep[0]
-        a_timestep = timestep[1]
-        v_pe, av_cross_video_freq_cis = pe[0]
-        a_pe, av_cross_audio_freq_cis = pe[1]
-
-        (
-            av_ca_audio_scale_shift_timestep,
-            av_ca_video_scale_shift_timestep,
-            av_ca_a2v_gate_noise_timestep,
-            av_ca_v2a_gate_noise_timestep,
-        ) = timestep[2]
-
-        """Process transformer blocks for LTXAV."""
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-
-        # Process transformer blocks
-        for i, block in enumerate(self.transformer_blocks):
-            if ("double_block", i) in blocks_replace:
-
-                def block_wrap(args):
-                    out = {}
-                    out["img"] = block(
-                        args["img"],
-                        v_context=args["v_context"],
-                        a_context=args["a_context"],
-                        attention_mask=args["attention_mask"],
-                        v_timestep=args["v_timestep"],
-                        a_timestep=args["a_timestep"],
-                        v_pe=args["v_pe"],
-                        a_pe=args["a_pe"],
-                        v_cross_pe=args["v_cross_pe"],
-                        a_cross_pe=args["a_cross_pe"],
-                        v_cross_scale_shift_timestep=args["v_cross_scale_shift_timestep"],
-                        a_cross_scale_shift_timestep=args["a_cross_scale_shift_timestep"],
-                        v_cross_gate_timestep=args["v_cross_gate_timestep"],
-                        a_cross_gate_timestep=args["a_cross_gate_timestep"],
-                        transformer_options=args["transformer_options"],
-                    )
-                    return out
-
-                out = blocks_replace[("double_block", i)](
-                    {
-                        "img": (vx, ax),
-                        "v_context": v_context,
-                        "a_context": a_context,
-                        "attention_mask": attention_mask,
-                        "v_timestep": v_timestep,
-                        "a_timestep": a_timestep,
-                        "v_pe": v_pe,
-                        "a_pe": a_pe,
-                        "v_cross_pe": av_cross_video_freq_cis,
-                        "a_cross_pe": av_cross_audio_freq_cis,
-                        "v_cross_scale_shift_timestep": av_ca_video_scale_shift_timestep,
-                        "a_cross_scale_shift_timestep": av_ca_audio_scale_shift_timestep,
-                        "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep,
-                        "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
-                        "transformer_options": transformer_options,
-                    },
-                    {"original_block": block_wrap},
-                )
-                vx, ax = out["img"]
-            else:
-                vx, ax = block(
-                    (vx, ax),
-                    v_context=v_context,
-                    a_context=a_context,
-                    attention_mask=attention_mask,
-                    v_timestep=v_timestep,
-                    a_timestep=a_timestep,
-                    v_pe=v_pe,
-                    a_pe=a_pe,
-                    v_cross_pe=av_cross_video_freq_cis,
-                    a_cross_pe=av_cross_audio_freq_cis,
-                    v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep,
-                    a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep,
-                    v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,
-                    a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
-                    transformer_options=transformer_options,
-                )
-
-        return [vx, ax]
-
-    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
-        vx = x[0]
-        ax = x[1]
-        v_embedded_timestep = embedded_timestep[0]
-        a_embedded_timestep = embedded_timestep[1]
-
-        # Expand compressed video timestep if needed
-        if isinstance(v_embedded_timestep, CompressedTimestep):
-            v_embedded_timestep = v_embedded_timestep.expand()
-
-        vx = super()._process_output(vx, v_embedded_timestep, keyframe_idxs, **kwargs)
-
-        # Process audio output
-        a_scale_shift_values = (
-            self.audio_scale_shift_table[None, None].to(device=a_embedded_timestep.device, dtype=a_embedded_timestep.dtype)
-            + a_embedded_timestep[:, :, None]
-        )
-        a_shift, a_scale = a_scale_shift_values[:, :, 0], a_scale_shift_values[:, :, 1]
-
-        ax = self.audio_norm_out(ax)
-        ax = ax * (1 + a_scale) + a_shift
-        ax = self.audio_proj_out(ax)
-
-        # Unpatchify audio
-        ax = self.a_patchifier.unpatchify(
-            ax, channels=self.num_audio_channels, freq=self.audio_frequency_bins
-        )
-
-        # Recombine audio and video
-        original_shape = kwargs.get("av_orig_shape")
-        return self.recombine_audio_and_video_latents(vx, ax, original_shape)
-
-    def forward(
-        self,
-        x,
-        timestep,
-        context,
-        attention_mask=None,
-        frame_rate=25,
-        transformer_options={},
-        keyframe_idxs=None,
-        **kwargs,
-    ):
-        """
-        Forward pass for LTXAV model.
-
-        Args:
-            x: Combined audio-video input tensor
-            timestep: Tuple of (video_timestep, audio_timestep) or single timestep
-            context: Context tensor (e.g., text embeddings)
-            attention_mask: Attention mask tensor
-            frame_rate: Frame rate for temporal processing
-            transformer_options: Additional options for transformer blocks
-            keyframe_idxs: Keyframe indices for temporal processing
-            **kwargs: Additional keyword arguments including audio_length
-
-        Returns:
-            Combined audio-video output tensor
-        """
-        # Handle timestep format
-        if isinstance(timestep, (tuple, list)) and len(timestep) == 2:
-            v_timestep, a_timestep = timestep
-            kwargs["a_timestep"] = a_timestep
-            timestep = v_timestep
-        else:
-            kwargs["a_timestep"] = timestep
-
-        # Call parent forward method
-        return super().forward(
-            x,
-            timestep,
-            context,
-            attention_mask,
-            frame_rate,
-            transformer_options,
-            keyframe_idxs,
-            **kwargs,
-        )