Merge branch 'master' into lora-node-refactor

* init

* update

* Update model.py

* Update model.py

* remove print

* Fix text encoding

* Prevent empty negative prompt

Really doesn't work otherwise

* fp16 works

* I2V

* Update model_base.py

* Update nodes_hunyuan.py

* Better latent rgb factors

* Use the correct sigclip output...

* Support HunyuanVideo1.5 SR model

* whitespaces...

* Proper latent channel count

* SR model fixes

This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already

* vae_refiner: roll the convolution through temporal

Work in progress.

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

* Support HunyuanVideo15 latent resampler

* fix

* Some cleanup

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Proper hyvid15 I2V channels

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Fix TokenRefiner for fp16

Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.

* Bugfix for the HunyuanVideo15 SR model

* vae_refiner: roll the convolution through temporal II

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).

Fixed names, cleaned up code.

* Allow any number of input frames in VAE.

* Better VAE encode mem estimation.

* Lowvram fix.

* Fix hunyuan image 2.1 refiner.

* Fix mistake.

* Name changes.

* Rename.

* Whitespace.

* Fix.

* Fix.

---------

Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -0,0 +1,27 @@
+As of the time of writing this you need this preview driver for best results:
+https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.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_amd_base_files/run_amd_gpu_disable_smart_memory.bat

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

.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat

@@ -0,0 +1,3 @@
+..\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.
+pause

.ci/windows_nvidia_base_files/run_nvidia_gpu.bat

@@ -0,0 +1,3 @@
+.\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.
+pause

.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat

@@ -0,0 +1,3 @@
+.\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.
+pause

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -8,13 +8,15 @@ body:
         Before submitting a **Bug Report**, please ensure the following:
 
         - **1:** You are running the latest version of ComfyUI.
-        - **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+        - **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report.
         - **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.
+        `--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.
         - **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.
 
-        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.
+        ## 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.
   - type: checkboxes
     id: custom-nodes-test
     attributes:

.github/PULL_REQUEST_TEMPLATE/api-node.md

@@ -0,0 +1,21 @@
+<!-- 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

@@ -0,0 +1,58 @@
+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

@@ -0,0 +1,78 @@
+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: "9"
+      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 6.4.4"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "rocm644"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "amd"
+      rel_extra_name: ""
+      test_release: false
+    secrets: inherit

.github/workflows/ruff.yml

@@ -21,3 +21,28 @@ 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

@@ -2,17 +2,17 @@
 name: "Release Stable Version"
 
 on:
-  workflow_dispatch:
+  workflow_call:
     inputs:
       git_tag:
         description: 'Git tag'
         required: true
         type: string
-      cu:
-        description: 'CUDA version'
+      cache_tag:
+        description: 'Cached dependencies tag'
         required: true
         type: string
-        default: "129"
+        default: "cu129"
       python_minor:
         description: 'Python minor version'
         required: true
@@ -23,7 +23,57 @@ on:
         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'
+        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
 
 jobs:
   package_comfy_windows:
@@ -42,15 +92,15 @@ jobs:
         id: cache
         with:
           path: |
-            cu${{ inputs.cu }}_python_deps.tar
+            ${{ inputs.cache_tag }}_python_deps.tar
             update_comfyui_and_python_dependencies.bat
-          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
+          key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
       - shell: bash
         run: |
-          mv cu${{ inputs.cu }}_python_deps.tar ../
+          mv ${{ inputs.cache_tag }}_python_deps.tar ../
           mv update_comfyui_and_python_dependencies.bat ../
           cd ..
-          tar xf cu${{ inputs.cu }}_python_deps.tar
+          tar xf ${{ inputs.cache_tag }}_python_deps.tar
           pwd
           ls
 
@@ -65,12 +115,19 @@ jobs:
           echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
           curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
           ./python.exe get-pip.py
-          ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
+          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
+
+          grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
+          ./python.exe -s -m pip install -r requirements_comfyui.txt
+          rm requirements_comfyui.txt
+
           sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
 
-          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
+          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 ..
 
@@ -85,14 +142,18 @@ jobs:
 
           mkdir update
           cp -r ComfyUI/.ci/update_windows/* ./update/
-          cp -r ComfyUI/.ci/windows_base_files/* ./
+          cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_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_nvidia.7z
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.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
 
@@ -101,10 +162,9 @@ jobs:
           ls
 
       - name: Upload binaries to release
-        uses: svenstaro/upload-release-action@v2
+        uses: softprops/action-gh-release@v2
         with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ComfyUI_windows_portable_nvidia.7z
-          tag: ${{ inputs.git_tag }}
-          overwrite: true
+          files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
+          tag_name: ${{ inputs.git_tag }}
           draft: true
+          overwrite_files: true

.github/workflows/test-ci.yml

@@ -21,14 +21,15 @@ jobs:
       fail-fast: false
       matrix:
         # os: [macos, linux, windows]
-        os: [macos, linux]
-        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        # os: [macos, linux]
+        os: [linux]
+        python_version: ["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: ""
@@ -73,14 +74,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [macos, linux]
+        # os: [macos, linux]
+        os: [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

@@ -0,0 +1,30 @@
+name: Execution Tests
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+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-unit.yml

@@ -10,7 +10,7 @@ jobs:
   test:
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        os: [ubuntu-latest, windows-2022, macos-latest]
     runs-on: ${{ matrix.os }}
     continue-on-error: true
     steps:

.github/workflows/windows_release_dependencies.yml

@@ -17,7 +17,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "129"
+        default: "130"
 
       python_minor:
         description: 'python minor version'
@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "9"
 #  push:
 #    branches:
 #      - master
@@ -56,7 +56,8 @@ 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
 
-            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
+            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 install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

.github/workflows/windows_release_dependencies_manual.yml

@@ -0,0 +1,64 @@
+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_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
             cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause

.github/workflows/windows_release_package.yml

@@ -81,7 +81,7 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
             cp ../update_comfyui_and_python_dependencies.bat ./update/
 
             cd ..

CODEOWNERS

@@ -1,25 +1,3 @@
 # Admins
 * @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 @guill
-/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-
-# Python web server
-/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-
-# Node developers
-/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
-/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
-/comfy_api_nodes/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
+* @kosinkadink

QUANTIZATION.md

@@ -0,0 +1,168 @@
+# 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

@@ -66,6 +66,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [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/)
 - 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)
@@ -111,10 +112,11 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 
 ## Release Process
 
-ComfyUI follows a weekly release cycle targeting Friday 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 targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
-   - Releases a new stable version (e.g., v0.7.0)
+   - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
@@ -171,10 +173,20 @@ 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) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
+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\
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+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.
@@ -190,7 +202,11 @@ comfy install
 
 ## Manual Install (Windows, Linux)
 
-Python 3.13 is very well supported. If you have trouble with some custom node dependencies you can try 3.12
+Python 3.14 works but you may encounter issues with the torch compile node. The free threaded variant is still missing some dependencies.
+
+Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
+
+### Instructions:
 
 Git clone this repo.
 
@@ -199,18 +215,36 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 Put your VAE in: models/vae
 
 
-### AMD GPUs (Linux only)
+### AMD GPUs (Linux)
+
 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```
 
-This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
+```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-dgpu/```
+
+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/```
 
 ### Intel GPUs (Windows and Linux)
 
-(Option 1) 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)
+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:
 
@@ -220,19 +254,15 @@ This is the command to install the Pytorch xpu nightly which might have some per
 
 ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
 
-(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
-
-1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
-
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
 
 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/cu129```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
 
 #### Troubleshooting
 
@@ -263,12 +293,6 @@ 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:

app/frontend_management.py

@@ -10,7 +10,8 @@ import importlib
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import TypedDict, Optional
+from typing import Dict, TypedDict, Optional
+from aiohttp import web
 from importlib.metadata import version
 
 import requests
@@ -42,6 +43,7 @@ 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:
@@ -63,6 +65,7 @@ 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."""
 
@@ -203,6 +206,37 @@ 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:
@@ -224,7 +258,54 @@ comfyui-frontend-package is not installed.
             sys.exit(-1)
 
     @classmethod
-    def templates_path(cls) -> str:
+    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."""
         try:
             import comfyui_workflow_templates
 
@@ -243,6 +324,7 @@ comfyui-workflow-templates is not installed.
 ********** ERROR ***********
 """.strip()
             )
+            return None
 
     @classmethod
     def embedded_docs_path(cls) -> str:
@@ -359,3 +441,17 @@ 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/subgraph_manager.py

@@ -0,0 +1,112 @@
+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"
+
+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
+
+    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):
+        # if not forced to reload and cached, return cache
+        if not force_reload and self.cached_custom_node_subgraphs is not None:
+            return self.cached_custom_node_subgraphs
+        # Load subgraphs from custom nodes
+        subfolder = "subgraphs"
+        subgraphs_dict: dict[SubgraphEntry] = {}
+
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            pattern = os.path.join(folder, f"*/{subfolder}/*.json")
+            matched_files = glob.glob(pattern)
+            for file in matched_files:
+                # replace backslashes with forward slashes
+                file = file.replace('\\', '/')
+                info: CustomNodeSubgraphEntryInfo = {
+                    "node_pack": "custom_nodes." + file.split('/')[-3]
+                }
+                source = Source.custom_node
+                # hash source + path to make sure id will be as unique as possible, but
+                # reproducible across backend reloads
+                id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
+                entry: SubgraphEntry = {
+                    "source": Source.custom_node,
+                    "name": os.path.splitext(os.path.basename(file))[0],
+                    "path": file,
+                    "info": info,
+                }
+                subgraphs_dict[id] = entry
+        self.cached_custom_node_subgraphs = subgraphs_dict
+        return subgraphs_dict
+
+    async def get_custom_node_subgraph(self, id: str, loadedModules):
+        subgraphs = await self.get_custom_node_subgraphs(loadedModules)
+        entry: SubgraphEntry = subgraphs.get(id, None)
+        if entry is not None and entry.get('data', None) 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_custom_node_subgraphs(loadedModules)
+            # NOTE: we may want to include other sources of global subgraphs such as templates in the future;
+            # that's the reasoning for the current implementation
+            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_custom_node_subgraph(id, loadedModules)
+            return web.json_response(await self.sanitize_entry(subgraph))

comfy/audio_encoders/audio_encoders.py

@@ -1,4 +1,5 @@
 from .wav2vec2 import Wav2Vec2Model
+from .whisper import WhisperLargeV3
 import comfy.model_management
 import comfy.ops
 import comfy.utils
@@ -11,7 +12,18 @@ class AudioEncoderModel():
         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 = Wav2Vec2Model(dtype=self.dtype, device=offload_device, operations=comfy.ops.manual_cast)
+        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.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
         self.model_sample_rate = 16000
@@ -29,14 +41,51 @@ class AudioEncoderModel():
         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=""):
-    audio_encoder = AudioEncoderModel(None)
     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

@@ -13,19 +13,49 @@ class LayerNormConv(nn.Module):
         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, dtype=None, device=None, operations=None):
+    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
         super().__init__()
-        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=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-        ])
+        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)
@@ -76,6 +106,7 @@ class TransformerEncoder(nn.Module):
         num_heads=12,
         num_layers=12,
         mlp_ratio=4.0,
+        do_stable_layer_norm=True,
         dtype=None, device=None, operations=None
     ):
         super().__init__()
@@ -86,20 +117,25 @@ class TransformerEncoder(nn.Module):
                 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)
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
         all_x += (x,)
         return x, all_x
 
@@ -145,6 +181,7 @@ class TransformerEncoderLayer(nn.Module):
         embed_dim=768,
         num_heads=12,
         mlp_ratio=4.0,
+        do_stable_layer_norm=True,
         dtype=None, device=None, operations=None
     ):
         super().__init__()
@@ -154,15 +191,19 @@ class TransformerEncoderLayer(nn.Module):
         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
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
         x = self.attention(x, mask=mask)
         x = residual + x
-
-        x = x + self.feed_forward(self.final_layer_norm(x))
-        return 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):
@@ -174,34 +215,38 @@ class Wav2Vec2Model(nn.Module):
         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, device=device, dtype=dtype, operations=operations)
+        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)
 
-        x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
+        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

@@ -0,0 +1,186 @@
+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/cli_args.py

@@ -105,6 +105,7 @@ 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.")
@@ -143,8 +144,11 @@ class PerformanceFeature(enum.Enum):
     Fp16Accumulation = "fp16_accumulation"
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
+    AutoTune = "autotune"
 
-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("--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("--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.")
@@ -156,7 +160,7 @@ 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.")
+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("--multi-user", action="store_true", help="Enables per-user storage.")
 

comfy/clip_model.py

@@ -61,8 +61,12 @@ 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 < 0:
+            if intermediate_output == "all":
+                all_intermediate = []
+                intermediate_output = None
+            elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
         intermediate = None
@@ -70,6 +74,12 @@ 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):

comfy/clip_vision.py

@@ -50,7 +50,13 @@ 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])
-        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
+        model_type = config.get("model_type", "clip_vision_model")
+        model_class = IMAGE_ENCODERS.get(model_type)
+        if model_type == "siglip_vision_model":
+            self.return_all_hidden_states = True
+        else:
+            self.return_all_hidden_states = False
+
         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)
@@ -68,12 +74,18 @@ class ClipVisionModel():
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
         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)
+        out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -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["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+        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["mm_projected"] = out[3]
         return outputs
 
@@ -124,8 +136,12 @@ 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")
-    elif "embeddings.patch_embeddings.projection.weight" in sd:
+
+    # Dinov2
+    elif 'encoder.layer.39.layer_scale2.lambda1' 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/controlnet.py

@@ -253,7 +253,10 @@ 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[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+                    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)
                     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)
 
@@ -307,11 +310,13 @@ class ControlLoraOps:
             self.bias = None
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
             if self.up is not None:
-                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)
+                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)
             else:
-                return torch.nn.functional.linear(input, weight, bias)
+                x = torch.nn.functional.linear(input, weight, bias)
+            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
     class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(
@@ -347,12 +352,13 @@ class ControlLoraOps:
 
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
             if self.up is not None:
-                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)
+                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)
             else:
-                return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
-
+                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
 
 class ControlLora(ControlNet):
     def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
@@ -585,11 +591,18 @@ def load_controlnet_flux_instantx(sd, model_options={}):
 
 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_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    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, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=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):

comfy/image_encoders/dino2.py

@@ -31,6 +31,20 @@ 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):
@@ -50,12 +64,15 @@ class SwiGLUFFN(torch.nn.Module):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
         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)
-        self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+        if use_swiglu_ffn:
+            self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+        else:
+            self.mlp = Dinov2MLP(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)
 
@@ -66,9 +83,10 @@ class Dino2Block(torch.nn.Module):
 
 
 class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
+        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)])
 
     def forward(self, x, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
@@ -78,8 +96,8 @@ class Dino2Encoder(torch.nn.Module):
                 intermediate_output = len(self.layer) + intermediate_output
 
         intermediate = None
-        for i, l in enumerate(self.layer):
-            x = l(x, optimized_attention)
+        for i, layer in enumerate(self.layer):
+            x = layer(x, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
         return x, intermediate
@@ -128,9 +146,10 @@ 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)
+        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
         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

@@ -0,0 +1,22 @@
+{
+  "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

@@ -86,24 +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 = True
-        if "cpu" in kwargs:
-            self.cpu_tree = kwargs.pop("cpu")
+        self.cpu_tree = kwargs.pop("cpu", True)
         t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
+        w0 = kwargs.pop('w0', None)
+        if w0 is None:
+            w0 = torch.zeros_like(x)
+        self.batched = False
         if seed is None:
-            seed = torch.randint(0, 2 ** 63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
+            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
             w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        if self.cpu_tree:
-            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]
+            seed = (seed,)
+        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)
 
     @staticmethod
     def sort(a, b):
@@ -111,11 +111,10 @@ class BatchedBrownianTree:
 
     def __call__(self, t0, t1):
         t0, t1, sign = self.sort(t0, t1)
+        device, dtype = t0.device, t0.dtype
         if self.cpu_tree:
-            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)
-
+            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)
         return w if self.batched else w[0]
 
 
@@ -171,6 +170,16 @@ 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)."""
@@ -1550,13 +1559,12 @@ 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):
     """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
-    arXiv: https://arxiv.org/abs/2305.14267
+    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
     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')
@@ -1564,55 +1572,53 @@ 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
-        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)
+            continue
 
-            # 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()
+        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)
 
-            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])
+        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+        alpha_t = sigmas[i + 1] * lambda_t.exp()
 
-            # 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 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 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
+        # Step 2
+        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
+        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
     return x
 
 
 @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
+    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
     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')
@@ -1624,45 +1630,49 @@ 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
-        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)
+            continue
 
-            # 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()
+        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)
 
-            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])
+        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()
 
-            # 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 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 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 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 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
+        # 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
     return x
 
 

comfy/latent_formats.py

@@ -533,11 +533,154 @@ 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
+    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
+    scale_factor = 1.03682
+
 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
@@ -546,3 +689,20 @@ class Hunyuan3Dv2mini(LatentFormat):
 class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
+
+class ChromaRadiance(LatentFormat):
+    latent_channels = 3
+
+    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

@@ -133,6 +133,7 @@ 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(
@@ -140,6 +141,7 @@ class Attention(nn.Module):
             hidden_states,
             encoder_hidden_states=encoder_hidden_states,
             attention_mask=attention_mask,
+            transformer_options=transformer_options,
             **cross_attention_kwargs,
         )
 
@@ -366,6 +368,7 @@ 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:
@@ -433,7 +436,7 @@ 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,
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,
         ).to(query.dtype)
 
         # linear proj
@@ -697,6 +700,7 @@ 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]
@@ -720,6 +724,7 @@ 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(
@@ -729,6 +734,7 @@ 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:
@@ -743,6 +749,7 @@ 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

@@ -314,6 +314,7 @@ 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)
@@ -339,6 +340,7 @@ 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)
@@ -393,6 +395,7 @@ 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,
@@ -402,6 +405,7 @@ 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

@@ -23,8 +23,6 @@ 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),
         ])
@@ -37,10 +35,6 @@ 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)):
@@ -73,10 +67,8 @@ 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):
@@ -91,9 +83,7 @@ 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)
@@ -101,7 +91,6 @@ 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/audio/dit.py

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

comfy/ldm/aura/mmdit.py

@@ -85,7 +85,7 @@ class SingleAttention(nn.Module):
         )
 
     #@torch.compile()
-    def forward(self, c):
+    def forward(self, c, transformer_options={}):
 
         bsz, seqlen1, _ = c.shape
 
@@ -95,7 +95,7 @@ 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)
+        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)
         c = self.w1o(output)
         return c
 
@@ -144,7 +144,7 @@ class DoubleAttention(nn.Module):
 
 
     #@torch.compile()
-    def forward(self, c, x):
+    def forward(self, c, x, transformer_options={}):
 
         bsz, seqlen1, _ = c.shape
         bsz, seqlen2, _ = x.shape
@@ -168,7 +168,7 @@ 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)
+        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)
 
         c, x = output.split([seqlen1, seqlen2], dim=1)
         c = self.w1o(c)
@@ -207,7 +207,7 @@ class MMDiTBlock(nn.Module):
         self.is_last = is_last
 
     #@torch.compile()
-    def forward(self, c, x, global_cond, **kwargs):
+    def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
 
         cres, xres = c, x
 
@@ -225,7 +225,7 @@ class MMDiTBlock(nn.Module):
         x = modulate(self.normX1(x), xshift_msa, xscale_msa)
 
         # attention
-        c, x = self.attn(c, x)
+        c, x = self.attn(c, x, transformer_options=transformer_options)
 
 
         c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
@@ -255,13 +255,13 @@ 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, **kwargs):
+    def forward(self, cx, global_cond, transformer_options={}, **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)
+        cx = self.attn(cx, transformer_options=transformer_options)
         cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
         mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
         cx = gate_mlp.unsqueeze(1) * mlpout
@@ -473,13 +473,14 @@ class MMDiT(nn.Module):
                         out = {}
                         out["txt"], out["img"] = layer(args["txt"],
                                                        args["img"],
-                                                       args["vec"])
+                                                       args["vec"],
+                                                       transformer_options=args["transformer_options"])
                         return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
                     c = out["txt"]
                     x = out["img"]
                 else:
-                    c, x = layer(c, x, global_cond, **kwargs)
+                    c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)
 
         if len(self.single_layers) > 0:
             c_len = c.size(1)
@@ -488,13 +489,13 @@ class MMDiT(nn.Module):
                 if ("single_block", i) in blocks_replace:
                     def block_wrap(args):
                         out = {}
-                        out["img"] = layer(args["img"], args["vec"])
+                        out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"])
                         return out
 
-                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
                     cx = out["img"]
                 else:
-                    cx = layer(cx, global_cond, **kwargs)
+                    cx = layer(cx, global_cond, transformer_options=transformer_options, **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):
+    def forward(self, q, k, v, transformer_options={}):
         q = self.to_q(q)
         k = self.to_k(k)
         v = self.to_v(v)
 
-        out = optimized_attention(q, k, v, self.heads)
+        out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)
 
         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):
+    def forward(self, x, kv, self_attn=False, transformer_options={}):
         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)
+        x = self.attn(x, kv, kv, transformer_options=transformer_options)
         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):
+    def forward(self, x, kv, transformer_options={}):
         kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)
         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):
+    def _down_encode(self, x, r_embed, clip, transformer_options={}):
         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)
+                        x = block(x, clip, transformer_options=transformer_options)
                     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):
+    def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):
         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)
+                        x = block(x, clip, transformer_options=transformer_options)
                     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, **kwargs):
+    def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **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)
-        x = self._up_decode(level_outputs, r_embed, clip)
+        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)
         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):
+    def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):
         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)
+                        x = block(x, clip, transformer_options=transformer_options)
                     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):
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):
         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)
+                        x = block(x, clip, transformer_options=transformer_options)
                     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, **kwargs):
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **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)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet)
+        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)
         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):
@@ -48,124 +48,6 @@ 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

@@ -11,12 +11,12 @@ import comfy.ldm.common_dit
 from comfy.ldm.flux.layers import (
     EmbedND,
     timestep_embedding,
+    DoubleStreamBlock,
+    SingleStreamBlock,
 )
 
 from .layers import (
-    DoubleStreamBlock,
     LastLayer,
-    SingleStreamBlock,
     Approximator,
     ChromaModulationOut,
 )
@@ -90,6 +90,7 @@ 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)
@@ -98,7 +99,7 @@ class Chroma(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                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)
             ]
         )
@@ -151,8 +152,6 @@ 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)
@@ -193,14 +192,16 @@ class Chroma(nn.Module):
                                                        txt=args["txt"],
                                                        vec=args["vec"],
                                                        pe=args["pe"],
-                                                       attn_mask=args.get("attn_mask"))
+                                                       attn_mask=args.get("attn_mask"),
+                                                       transformer_options=args.get("transformer_options"))
                         return out
 
                     out = blocks_replace[("double_block", i)]({"img": img,
                                                                "txt": txt,
                                                                "vec": double_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask},
+                                                               "attn_mask": attn_mask,
+                                                               "transformer_options": transformer_options},
                                                               {"original_block": block_wrap})
                     txt = out["txt"]
                     img = out["img"]
@@ -209,7 +210,8 @@ class Chroma(nn.Module):
                                      txt=txt,
                                      vec=double_mod,
                                      pe=pe,
-                                     attn_mask=attn_mask)
+                                     attn_mask=attn_mask,
+                                     transformer_options=transformer_options)
 
                 if control is not None: # Controlnet
                     control_i = control.get("input")
@@ -229,17 +231,19 @@ class Chroma(nn.Module):
                         out["img"] = block(args["img"],
                                            vec=args["vec"],
                                            pe=args["pe"],
-                                           attn_mask=args.get("attn_mask"))
+                                           attn_mask=args.get("attn_mask"),
+                                           transformer_options=args.get("transformer_options"))
                         return out
 
                     out = blocks_replace[("single_block", i)]({"img": img,
                                                                "vec": single_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask},
+                                                               "attn_mask": attn_mask,
+                                                               "transformer_options": transformer_options},
                                                               {"original_block": block_wrap})
                     img = out["img"]
                 else:
-                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
 
                 if control is not None: # Controlnet
                     control_o = control.get("output")
@@ -249,8 +253,9 @@ class Chroma(nn.Module):
                             img[:, txt.shape[1] :, ...] += add
 
         img = img[:, txt.shape[1] :, ...]
-        final_mod = self.get_modulations(mod_vectors, "final")
-        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        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)
         return img
 
     def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
@@ -266,6 +271,9 @@ class Chroma(nn.Module):
 
         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

@@ -0,0 +1,206 @@
+# 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

@@ -0,0 +1,319 @@
+# 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]
+
+
+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
+
+    @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 type(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 _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),
+        )
+        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]

comfy/ldm/cosmos/blocks.py

@@ -176,6 +176,7 @@ class Attention(nn.Module):
         context=None,
         mask=None,
         rope_emb=None,
+        transformer_options={},
         **kwargs,
     ):
         """
@@ -184,7 +185,7 @@ 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)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
         del q, k, v
         out = rearrange(out, " b n s c -> s b (n c)")
         return self.to_out(out)
@@ -546,6 +547,7 @@ 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.
@@ -571,6 +573,7 @@ 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
@@ -665,6 +668,7 @@ 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.
@@ -702,6 +706,7 @@ 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(
@@ -709,6 +714,7 @@ 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}")
@@ -784,6 +790,7 @@ 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(
@@ -793,5 +800,6 @@ 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/model.py

@@ -520,6 +520,7 @@ 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
@@ -534,6 +535,7 @@ 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

@@ -44,7 +44,7 @@ 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) -> 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, transformer_options: Optional[dict] = {}) -> torch.Tensor:
     """Computes multi-head attention using PyTorch's native implementation.
 
     This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
@@ -71,7 +71,7 @@ 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)
+    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)
 
 
 class Attention(nn.Module):
@@ -180,8 +180,8 @@ class Attention(nn.Module):
 
         return q, k, v
 
-    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]
+    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]
         return self.output_dropout(self.output_proj(result))
 
     def forward(
@@ -189,6 +189,7 @@ 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:
@@ -196,7 +197,7 @@ 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)
+        return self.compute_attention(q, k, v, transformer_options=transformer_options)
 
 
 class Timesteps(nn.Module):
@@ -459,6 +460,7 @@ 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:
         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
@@ -512,6 +514,7 @@ class Block(nn.Module):
                 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,
@@ -525,6 +528,7 @@ class Block(nn.Module):
             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
@@ -534,6 +538,7 @@ class Block(nn.Module):
                     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,
@@ -547,6 +552,7 @@ 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 * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
@@ -865,6 +871,7 @@ 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", {}),
         }
         for block in self.blocks:
             x_B_T_H_W_D = block(

comfy/ldm/flux/layers.py

@@ -130,13 +130,17 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
 
 
 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):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, 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_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.modulation = modulation
+
+        if self.modulation:
+            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, dtype=dtype, device=device, operations=operations)
 
@@ -147,7 +151,9 @@ class DoubleStreamBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-        self.txt_mod = Modulation(hidden_size, double=True, 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_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)
 
@@ -159,47 +165,66 @@ class DoubleStreamBlock(nn.Module):
         )
         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):
-        img_mod1, img_mod2 = self.img_mod(vec)
-        txt_mod1, txt_mod2 = self.txt_mod(vec)
+    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
 
         # 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(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)
+            attn = attention(q, k, v,
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
 
             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(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)
+            attn = attention(q, k, v,
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
 
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
         # calculate the img bloks
-        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)
+        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)
 
         # 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:
@@ -220,6 +245,7 @@ class SingleStreamBlock(nn.Module):
         num_heads: int,
         mlp_ratio: float = 4.0,
         qk_scale: float = None,
+        modulation=True,
         dtype=None,
         device=None,
         operations=None
@@ -242,19 +268,29 @@ class SingleStreamBlock(nn.Module):
         self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
         self.mlp_act = nn.GELU(approximate="tanh")
-        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+        if modulation:
+            self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+        else:
+            self.modulation = None
+
+    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
 
-    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)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        del q, k, v
         # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        mlp = self.mlp_act(mlp)
+        output = self.linear2(torch.cat((attn, 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)

comfy/ldm/flux/math.py

@@ -6,18 +6,11 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
-    q_shape = q.shape
-    k_shape = k.shape
-
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
     if pe is not None:
-        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)
-
+        q, k = apply_rope(q, k, pe)
     heads = q.shape[1]
-    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
+    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
     return x
 
 
@@ -35,11 +28,13 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.to(dtype=torch.float32, device=pos.device)
 
+def apply_rope1(x: Tensor, freqs_cis: Tensor):
+    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+
+    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):
-    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)
-
+    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)

comfy/ldm/flux/model.py

@@ -106,6 +106,7 @@ class Flux(nn.Module):
         if y is None:
             y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
 
+        patches = transformer_options.get("patches", {})
         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.")
@@ -117,9 +118,17 @@ class Flux(nn.Module):
             if guidance is not None:
                 vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
+        vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
+        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)
@@ -135,14 +144,16 @@ class Flux(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"))
+                                                   attn_mask=args.get("attn_mask"),
+                                                   transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -151,7 +162,8 @@ class Flux(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask)
+                                 attn_mask=attn_mask,
+                                 transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -172,17 +184,19 @@ class Flux(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"))
+                                       attn_mask=args.get("attn_mask"),
+                                       transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -196,7 +210,7 @@ class Flux(nn.Module):
         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):
+    def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
         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))
@@ -208,10 +222,22 @@ class Flux(nn.Module):
         h_offset = ((h_offset + (patch_size // 2)) // patch_size)
         w_offset = ((w_offset + (patch_size // 2)) // patch_size)
 
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        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, 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=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)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, 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):
@@ -227,18 +253,24 @@ class Flux(nn.Module):
 
         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)
+        img, img_ids = self.process_img(x, transformer_options=transformer_options)
         img_tokens = img.shape[1]
         if ref_latents is not None:
             h = 0
             w = 0
             index = 0
-            index_ref_method = kwargs.get("ref_latents_method", "offset") == "index"
+            ref_latents_method = kwargs.get("ref_latents_method", "offset")
             for ref in ref_latents:
-                if index_ref_method:
+                if ref_latents_method == "index":
                     index += 1
                     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]
                 else:
                     index = 1
                     h_offset = 0

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -109,6 +109,7 @@ 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")
@@ -143,7 +144,7 @@ class AsymmetricAttention(nn.Module):
 
         xy = optimized_attention(q,
                                  k,
-                                 v, self.num_heads, skip_reshape=True)
+                                 v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)
 
         x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
         x = self.proj_x(x)
@@ -224,6 +225,7 @@ class AsymmetricJointBlock(nn.Module):
         x: torch.Tensor,
         c: torch.Tensor,
         y: torch.Tensor,
+        transformer_options={},
         **attn_kwargs,
     ):
         """Forward pass of a block.
@@ -256,6 +258,7 @@ class AsymmetricJointBlock(nn.Module):
             y,
             scale_x=scale_msa_x,
             scale_y=scale_msa_y,
+            transformer_options=transformer_options,
             **attn_kwargs,
         )
 
@@ -524,10 +527,11 @@ class AsymmDiTJoint(nn.Module):
                                                     args["txt"],
                                                     rope_cos=args["rope_cos"],
                                                     rope_sin=args["rope_sin"],
-                                                    crop_y=args["num_tokens"]
+                                                    crop_y=args["num_tokens"],
+                                                    transformer_options=args["transformer_options"]
                                                     )
                     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}, {"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, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 y_feat = out["txt"]
                 x = out["img"]
             else:
@@ -538,6 +542,7 @@ 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

@@ -72,8 +72,8 @@ class TimestepEmbed(nn.Module):
         return t_emb
 
 
-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])
+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)
 
 
 class HiDreamAttnProcessor_flashattn:
@@ -86,6 +86,7 @@ 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:
@@ -133,7 +134,7 @@ 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)
+        hidden_states = attention(query, key, value, transformer_options=transformer_options)
 
         if not attn.single:
             hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)
@@ -199,6 +200,7 @@ 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,
@@ -206,6 +208,7 @@ class HiDreamAttention(nn.Module):
             image_tokens_masks = image_tokens_masks,
             text_tokens = norm_text_tokens,
             rope = rope,
+            transformer_options=transformer_options,
         )
 
 
@@ -406,7 +409,7 @@ 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 = \
@@ -419,6 +422,7 @@ 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
 
@@ -483,6 +487,7 @@ 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, \
@@ -500,6 +505,7 @@ 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
@@ -550,6 +556,7 @@ 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,
@@ -557,6 +564,7 @@ class HiDreamImageBlock(nn.Module):
             text_tokens,
             adaln_input,
             rope,
+            transformer_options=transformer_options,
         )
 
 
@@ -786,6 +794,7 @@ 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
@@ -809,6 +818,7 @@ 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

@@ -99,14 +99,16 @@ class Hunyuan3Dv2(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"))
+                                                   attn_mask=args.get("attn_mask"),
+                                                   transformer_options=args["transformer_options"])
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -115,7 +117,8 @@ class Hunyuan3Dv2(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask)
+                                 attn_mask=attn_mask,
+                                 transformer_options=transformer_options)
 
         img = torch.cat((txt, img), 1)
 
@@ -126,17 +129,19 @@ class Hunyuan3Dv2(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"))
+                                       attn_mask=args.get("attn_mask"),
+                                       transformer_options=args["transformer_options"])
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
 
         img = img[:, txt.shape[1]:, ...]
         img = self.final_layer(img, vec)

comfy/ldm/hunyuan3d/vae.py

@@ -4,81 +4,458 @@
 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
-from einops import repeat, rearrange
+import math
 from tqdm import tqdm
+
+from typing import Optional
+
 import logging
 
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-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
+def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True):
 
-    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]
+    # manually create the pointer vector
+    assert src.size(0) == batch.numel()
 
-    return xyz, grid_size, length
+    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
 
 
-class VanillaVolumeDecoder:
+    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():
     @torch.no_grad()
-    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]
+    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):
 
-        # 1. generate query points
         if isinstance(bounds, float):
             bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
 
-        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)
+        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]
 
-        # 2. latents to 3d volume
         batch_logits = []
-        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc="Volume Decoding",
+        for start in tqdm(range(0, xyz.shape[0], num_chunks), desc="Volume Decoding",
                           disable=not enable_pbar):
-            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)
+
+            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)
             batch_logits.append(logits)
 
-        grid_logits = torch.cat(batch_logits, dim=1)
-        grid_logits = grid_logits.view((batch_size, *grid_size)).float()
+        grid_logits = torch.cat(batch_logits, dim = 1)
+        grid_logits = grid_logits.view((latents.shape[0], *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:
@@ -175,13 +552,11 @@ 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)
         return out
 
-
 class DropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
     """
@@ -232,38 +607,41 @@ 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()
 
-        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 = 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
 
+        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
+        out = F.scaled_dot_product_attention(q, k, v)
+
+        out = out.transpose(1, 2).reshape(bs, n_ctx, -1)
+
+        return out
 
 class MultiheadCrossAttention(nn.Module):
     def __init__(
@@ -306,7 +684,6 @@ class MultiheadCrossAttention(nn.Module):
         x = self.c_proj(x)
         return x
 
-
 class ResidualCrossAttentionBlock(nn.Module):
     def __init__(
         self,
@@ -366,7 +743,7 @@ class QKVMultiheadAttention(nn.Module):
         q = self.q_norm(q)
         k = self.k_norm(k)
 
-        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
         out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
         return out
 
@@ -383,8 +760,7 @@ 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(
@@ -491,7 +867,7 @@ 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 self.enable_ln_post == False:
+        if not self.enable_ln_post:
             qk_norm = False
         self.cross_attn_decoder = ResidualCrossAttentionBlock(
             width=width,
@@ -522,28 +898,44 @@ class CrossAttentionDecoder(nn.Module):
 
 class ShapeVAE(nn.Module):
     def __init__(
-        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,
+            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",
     ):
         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(
@@ -583,5 +975,14 @@ 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, x):
-        return None
+    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

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -0,0 +1,659 @@
+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

@@ -6,7 +6,6 @@ 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
 
@@ -40,6 +39,10 @@ class HunyuanVideoParams:
     patch_size: list
     qkv_bias: bool
     guidance_embed: bool
+    byt5: bool
+    meanflow: bool
+    use_cond_type_embedding: bool
+    vision_in_dim: int
 
 
 class SelfAttentionRef(nn.Module):
@@ -78,13 +81,13 @@ class TokenRefinerBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, x, c, mask):
+    def forward(self, x, c, mask, transformer_options={}):
         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)
+        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)
 
         x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
         x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
@@ -115,14 +118,14 @@ class IndividualTokenRefiner(nn.Module):
             ]
         )
 
-    def forward(self, x, c, mask):
+    def forward(self, x, c, mask, transformer_options={}):
         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)
+            x = block(x, c, m, transformer_options=transformer_options)
         return x
 
 
@@ -150,17 +153,45 @@ 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
-        c = x.sum(dim=1) / x.shape[1]
+        if x.dtype == torch.float16:
+            c = x.float().sum(dim=1) / x.shape[1]
+        else:
+            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)
+        x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
         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.
@@ -169,11 +200,15 @@ 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}"
@@ -185,9 +220,13 @@ 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=True, 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=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations)
         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)
+        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.guidance_in = (
             MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
         )
@@ -215,9 +254,38 @@ 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,
@@ -226,10 +294,13 @@ class HunyuanVideo(nn.Module):
         txt_ids: Tensor,
         txt_mask: Tensor,
         timesteps: Tensor,
-        y: Tensor,
+        y: Tensor = None,
+        txt_byt5=None,
+        clip_fea=None,
         guidance: Tensor = None,
         guiding_frame_index=None,
         ref_latent=None,
+        disable_time_r=False,
         control=None,
         transformer_options={},
     ) -> Tensor:
@@ -240,6 +311,14 @@ 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) / 2
+
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)
             ref_latent = self.img_in(ref_latent)
@@ -250,13 +329,17 @@ 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))
-            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)
+            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)
             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:
-            vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+            if self.vector_in is not None:
+                vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
             modulation_dims = None
             modulation_dims_txt = None
 
@@ -267,7 +350,32 @@ 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)
+        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)
 
         ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
@@ -285,14 +393,14 @@ class HunyuanVideo(nn.Module):
             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"])
+                    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"])
                     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}, {"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, 'transformer_options': transformer_options}, {"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)
+                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)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -307,13 +415,13 @@ class HunyuanVideo(nn.Module):
             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"])
+                    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"])
                     return out
 
-                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
+                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})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -328,12 +436,16 @@ class HunyuanVideo(nn.Module):
 
         img = self.final_layer(img, vec, modulation_dims=modulation_dims)  # (N, T, patch_size ** 2 * out_channels)
 
-        shape = initial_shape[-3:]
+        shape = initial_shape[-len(self.patch_size):]
         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)
-        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])
+        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])
         return img
 
     def img_ids(self, x):
@@ -348,16 +460,30 @@ 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 forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+    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, guidance, attention_mask, guiding_frame_index, ref_latent, control, transformer_options, **kwargs)
+        ).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, 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)
+    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)
         return out

comfy/ldm/hunyuan_video/upsampler.py

@@ -0,0 +1,120 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm, ResnetBlock, VideoConv3d
+import model_management, 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 = model_management.vae_device()
+        offload_device = model_management.vae_offload_device()
+        self.dtype = model_management.vae_dtype(self.load_device)
+        self.model_class = UPSAMPLERS.get(model_type)
+        self.model = self.model_class(**config).eval()
+
+        self.patcher = 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=True)
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def resample_latent(self, latent):
+        model_management.load_model_gpu(self.patcher)
+        return self.model(latent.to(self.load_device))

comfy/ldm/hunyuan_video/vae.py

@@ -0,0 +1,136 @@
+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

@@ -0,0 +1,363 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+import comfy.ops
+import comfy.ldm.models.autoencoder
+import comfy.model_management
+ops = comfy.ops.disable_weight_init
+
+class NoPadConv3d(nn.Module):
+    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
+        super().__init__()
+        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
+
+    x = xl[0]
+    xl.clear()
+
+    if conv_carry_out is not None:
+        to_push = x[:, :, -2:, :, :].clone()
+        conv_carry_out.append(to_push)
+
+    if isinstance(op, NoPadConv3d):
+        if conv_carry_in is None:
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
+        else:
+            carry_len = conv_carry_in[0].shape[2]
+            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
+
+    out = op(x)
+
+    return out
+
+
+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=True, refiner_vae=True, op=VideoConv3d):
+        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=True, refiner_vae=True, op=VideoConv3d):
+        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 HunyuanRefinerResnetBlock(ResnetBlock):
+    def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm):
+        super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
+        h = x
+        h = [ self.swish(self.norm1(x)) ]
+        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
+
+        h = [ self.dropout(self.swish(self.norm2(h))) ]
+        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x+h
+
+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 = NoPadConv3d
+            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([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                                   out_channels=tgt,
+                                                                   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 = HunyuanRefinerResnetBlock(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 = HunyuanRefinerResnetBlock(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, 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
+
+        if len(out) > 1:
+            out = torch.cat(out, dim=2)
+        else:
+            out = out[0]
+
+        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 = NoPadConv3d
+            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 = HunyuanRefinerResnetBlock(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 = HunyuanRefinerResnetBlock(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([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                                   out_channels=tgt,
+                                                                   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, 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
+
+        if len(out) > 1:
+            out = torch.cat(out, dim=2)
+        else:
+            out = out[0]
+
+        if not self.refiner_vae:
+            if z.shape[-3] == 1:
+                out = out[:, :, -1:]
+
+        return out
+

comfy/ldm/lightricks/model.py

@@ -3,12 +3,11 @@ from torch import nn
 import comfy.patcher_extension
 import comfy.ldm.modules.attention
 import comfy.ldm.common_dit
-from einops import rearrange
 import math
 from typing import Dict, Optional, Tuple
 
 from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
-
+from comfy.ldm.flux.math import apply_rope1
 
 def get_timestep_embedding(
     timesteps: torch.Tensor,
@@ -238,20 +237,6 @@ class FeedForward(nn.Module):
         return self.net(x)
 
 
-def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
-    cos_freqs = freqs_cis[0]
-    sin_freqs = freqs_cis[1]
-
-    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
-    t1, t2 = t_dup.unbind(dim=-1)
-    t_dup = torch.stack((-t2, t1), dim=-1)
-    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
-
-    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
-
-    return out
-
-
 class CrossAttention(nn.Module):
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
         super().__init__()
@@ -271,7 +256,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, mask=None, pe=None):
+    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
         q = self.to_q(x)
         context = x if context is None else context
         k = self.to_k(context)
@@ -281,13 +266,13 @@ class CrossAttention(nn.Module):
         k = self.k_norm(k)
 
         if pe is not None:
-            q = apply_rotary_emb(q, pe)
-            k = apply_rotary_emb(k, pe)
+            q = apply_rope1(q.unsqueeze(1), pe).squeeze(1)
+            k = apply_rope1(k.unsqueeze(1), pe).squeeze(1)
 
         if mask is None:
-            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
         else:
-            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
         return self.to_out(out)
 
 
@@ -303,15 +288,20 @@ class BasicTransformerBlock(nn.Module):
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
-        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+        attn1_input = comfy.ldm.common_dit.rms_norm(x)
+        attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
+        attn1_input = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
+        x.addcmul_(attn1_input, gate_msa)
+        del attn1_input
 
-        x += self.attn2(x, context=context, mask=attention_mask)
+        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
 
-        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
-        x += self.ff(y) * gate_mlp
+        y = comfy.ldm.common_dit.rms_norm(x)
+        y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
+        x.addcmul_(self.ff(y), gate_mlp)
 
         return x
 
@@ -327,41 +317,35 @@ def get_fractional_positions(indices_grid, max_pos):
 
 
 def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
-    dtype = torch.float32 #self.dtype
+    dtype = torch.float32
+    device = indices_grid.device
 
+    # Get fractional positions and compute frequency indices
     fractional_positions = get_fractional_positions(indices_grid, max_pos)
+    indices = theta ** torch.linspace(0, 1, dim // 6, device=device, dtype=dtype) * math.pi / 2
 
-    start = 1
-    end = theta
-    device = fractional_positions.device
+    # Compute frequencies and apply cos/sin
+    freqs = (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)).transpose(-1, -2).flatten(2)
+    cos_vals = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_vals = freqs.sin().repeat_interleave(2, dim=-1)
 
-    indices = theta ** (
-        torch.linspace(
-            math.log(start, theta),
-            math.log(end, theta),
-            dim // 6,
-            device=device,
-            dtype=dtype,
-        )
-    )
-    indices = indices.to(dtype=dtype)
-
-    indices = indices * math.pi / 2
-
-    freqs = (
-        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
-        .transpose(-1, -2)
-        .flatten(2)
-    )
-
-    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
-    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    # Pad if dim is not divisible by 6
     if dim % 6 != 0:
-        cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
-        sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
-        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
-        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
-    return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+        padding_size = dim % 6
+        cos_vals = torch.cat([torch.ones_like(cos_vals[:, :, :padding_size]), cos_vals], dim=-1)
+        sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
+
+    # Reshape and extract one value per pair (since repeat_interleave duplicates each value)
+    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+
+    # Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
+    freqs_cis = torch.stack([
+        torch.stack([cos_vals, -sin_vals], dim=-1),
+        torch.stack([sin_vals, cos_vals], dim=-1)
+    ], dim=-2).unsqueeze(1)  # [B, 1, N, dim//2, 2, 2]
+
+    return freqs_cis
 
 
 class LTXVModel(torch.nn.Module):
@@ -479,10 +463,10 @@ class LTXVModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -490,7 +474,8 @@ class LTXVModel(torch.nn.Module):
                     context=context,
                     attention_mask=attention_mask,
                     timestep=timestep,
-                    pe=pe
+                    pe=pe,
+                    transformer_options=transformer_options,
                 )
 
         # 3. Output
@@ -500,7 +485,7 @@ class LTXVModel(torch.nn.Module):
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
         x = self.norm_out(x)
         # Modulation
-        x = x * (1 + scale) + shift
+        x = torch.addcmul(x, x, scale).add_(shift)
         x = self.proj_out(x)
 
         x = self.patchifier.unpatchify(

comfy/ldm/lumina/model.py

@@ -104,6 +104,7 @@ class JointAttention(nn.Module):
         x: torch.Tensor,
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
+        transformer_options={},
     ) -> torch.Tensor:
         """
 
@@ -140,7 +141,7 @@ class JointAttention(nn.Module):
         if n_rep >= 1:
             xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
             xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
-        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
+        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)
 
         return self.out(output)
 
@@ -268,6 +269,7 @@ class JointTransformerBlock(nn.Module):
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor]=None,
+        transformer_options={},
     ):
         """
         Perform a forward pass through the TransformerBlock.
@@ -290,6 +292,7 @@ class JointTransformerBlock(nn.Module):
                     modulate(self.attention_norm1(x), scale_msa),
                     x_mask,
                     freqs_cis,
+                    transformer_options=transformer_options,
                 )
             )
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
@@ -304,6 +307,7 @@ class JointTransformerBlock(nn.Module):
                     self.attention_norm1(x),
                     x_mask,
                     freqs_cis,
+                    transformer_options=transformer_options,
                 )
             )
             x = x + self.ffn_norm2(
@@ -494,7 +498,7 @@ class NextDiT(nn.Module):
         return imgs
 
     def patchify_and_embed(
-        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
     ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
         bsz = len(x)
         pH = pW = self.patch_size
@@ -518,7 +522,7 @@ class NextDiT(nn.Module):
         max_cap_len = max(l_effective_cap_len)
         max_img_len = max(l_effective_img_len)
 
-        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
+        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.float32, device=device)
 
         for i in range(bsz):
             cap_len = l_effective_cap_len[i]
@@ -527,10 +531,22 @@ class NextDiT(nn.Module):
             H_tokens, W_tokens = H // pH, W // pW
             assert H_tokens * W_tokens == img_len
 
-            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
+            rope_options = transformer_options.get("rope_options", None)
+            h_scale = 1.0
+            w_scale = 1.0
+            h_start = 0
+            w_start = 0
+            if rope_options is not None:
+                h_scale = rope_options.get("scale_y", 1.0)
+                w_scale = rope_options.get("scale_x", 1.0)
+
+                h_start = rope_options.get("shift_y", 0.0)
+                w_start = rope_options.get("shift_x", 0.0)
+
+            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.float32, device=device)
             position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
-            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
-            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+            row_ids = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
+            col_ids = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
             position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
             position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
 
@@ -554,7 +570,7 @@ class NextDiT(nn.Module):
 
         # refine context
         for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)
 
         # refine image
         flat_x = []
@@ -573,7 +589,7 @@ class NextDiT(nn.Module):
         padded_img_embed = self.x_embedder(padded_img_embed)
         padded_img_mask = padded_img_mask.unsqueeze(1)
         for layer in self.noise_refiner:
-            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options)
 
         if cap_mask is not None:
             mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
@@ -616,12 +632,13 @@ class NextDiT(nn.Module):
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
+        transformer_options = kwargs.get("transformer_options", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
         freqs_cis = freqs_cis.to(x.device)
 
         for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input)
+            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
 
         x = self.final_layer(x, adaln_input)
         x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]

comfy/ldm/mmaudio/vae/activations.py

@@ -0,0 +1,120 @@
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+import comfy.model_management
+
+class Snake(nn.Module):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:
+            self.alpha = Parameter(torch.empty(in_features))
+        else:
+            self.alpha = Parameter(torch.empty(in_features))
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+class SnakeBeta(nn.Module):
+    '''
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha - trainable parameter that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:
+            self.alpha = Parameter(torch.empty(in_features))
+            self.beta = Parameter(torch.empty(in_features))
+        else:
+            self.alpha = Parameter(torch.empty(in_features))
+            self.beta = Parameter(torch.empty(in_features))
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        '''
+        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x

comfy/ldm/mmaudio/vae/alias_free_torch.py

@@ -0,0 +1,157 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import comfy.model_management
+
+if 'sinc' in dir(torch):
+    sinc = torch.sinc
+else:
+    # This code is adopted from adefossez's julius.core.sinc under the MIT License
+    # https://adefossez.github.io/julius/julius/core.html
+    #   LICENSE is in incl_licenses directory.
+    def sinc(x: torch.Tensor):
+        """
+        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+        """
+        return torch.where(x == 0,
+                           torch.tensor(1., device=x.device, dtype=x.dtype),
+                           torch.sin(math.pi * x) / math.pi / x)
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+#   LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
+    even = (kernel_size % 2 == 0)
+    half_size = kernel_size // 2
+
+    #For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.:
+        beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
+    else:
+        beta = 0.
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = (torch.arange(-half_size, half_size) + 0.5)
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(self,
+                 cutoff=0.5,
+                 half_width=0.6,
+                 stride: int = 1,
+                 padding: bool = True,
+                 padding_mode: str = 'replicate',
+                 kernel_size: int = 12):
+        # kernel_size should be even number for stylegan3 setup,
+        # in this implementation, odd number is also possible.
+        super().__init__()
+        if cutoff < -0.:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = (kernel_size % 2 == 0)
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    #input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right),
+                      mode=self.padding_mode)
+        out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device),
+                       stride=self.stride, groups=C)
+
+        return out
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
+                                      half_width=0.6 / ratio,
+                                      kernel_size=self.kernel_size)
+        self.register_buffer("filter", filter)
+
+    # x: [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode='replicate')
+        x = self.ratio * F.conv_transpose1d(
+            x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)
+        x = x[..., self.pad_left:-self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
+                                       half_width=0.6 / ratio,
+                                       stride=ratio,
+                                       kernel_size=self.kernel_size)
+
+    def forward(self, x):
+        xx = self.lowpass(x)
+
+        return xx
+
+class Activation1d(nn.Module):
+    def __init__(self,
+                 activation,
+                 up_ratio: int = 2,
+                 down_ratio: int = 2,
+                 up_kernel_size: int = 12,
+                 down_kernel_size: int = 12):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    # x: [B,C,T]
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x

comfy/ldm/mmaudio/vae/autoencoder.py

@@ -0,0 +1,156 @@
+from typing import Literal
+
+import torch
+import torch.nn as nn
+
+from .distributions import DiagonalGaussianDistribution
+from .vae import VAE_16k
+from .bigvgan import BigVGANVocoder
+import logging
+
+try:
+    import torchaudio
+except:
+    logging.warning("torchaudio missing, MMAudio VAE model will be broken")
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
+    return norm_fn(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes, norm_fn):
+    output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
+    return output
+
+class MelConverter(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        sampling_rate: float,
+        n_fft: int,
+        num_mels: int,
+        hop_size: int,
+        win_size: int,
+        fmin: float,
+        fmax: float,
+        norm_fn,
+    ):
+        super().__init__()
+        self.sampling_rate = sampling_rate
+        self.n_fft = n_fft
+        self.num_mels = num_mels
+        self.hop_size = hop_size
+        self.win_size = win_size
+        self.fmin = fmin
+        self.fmax = fmax
+        self.norm_fn = norm_fn
+
+        # mel = librosa_mel_fn(sr=self.sampling_rate,
+        #                      n_fft=self.n_fft,
+        #                      n_mels=self.num_mels,
+        #                      fmin=self.fmin,
+        #                      fmax=self.fmax)
+        # mel_basis = torch.from_numpy(mel).float()
+        mel_basis = torch.empty((num_mels, 1 + n_fft // 2))
+        hann_window = torch.hann_window(self.win_size)
+
+        self.register_buffer('mel_basis', mel_basis)
+        self.register_buffer('hann_window', hann_window)
+
+    @property
+    def device(self):
+        return self.mel_basis.device
+
+    def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
+        waveform = waveform.clamp(min=-1., max=1.).to(self.device)
+
+        waveform = torch.nn.functional.pad(
+            waveform.unsqueeze(1),
+            [int((self.n_fft - self.hop_size) / 2),
+             int((self.n_fft - self.hop_size) / 2)],
+            mode='reflect')
+        waveform = waveform.squeeze(1)
+
+        spec = torch.stft(waveform,
+                          self.n_fft,
+                          hop_length=self.hop_size,
+                          win_length=self.win_size,
+                          window=self.hann_window,
+                          center=center,
+                          pad_mode='reflect',
+                          normalized=False,
+                          onesided=True,
+                          return_complex=True)
+
+        spec = torch.view_as_real(spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        spec = torch.matmul(self.mel_basis, spec)
+        spec = spectral_normalize_torch(spec, self.norm_fn)
+
+        return spec
+
+class AudioAutoencoder(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        # ckpt_path: str,
+        mode=Literal['16k', '44k'],
+        need_vae_encoder: bool = True,
+    ):
+        super().__init__()
+
+        assert mode == "16k", "Only 16k mode is supported currently."
+        self.mel_converter = MelConverter(sampling_rate=16_000,
+                            n_fft=1024,
+                            num_mels=80,
+                            hop_size=256,
+                            win_size=1024,
+                            fmin=0,
+                            fmax=8_000,
+                            norm_fn=torch.log10)
+
+        self.vae = VAE_16k().eval()
+
+        bigvgan_config = {
+            "resblock": "1",
+            "num_mels": 80,
+            "upsample_rates": [4, 4, 2, 2, 2, 2],
+            "upsample_kernel_sizes": [8, 8, 4, 4, 4, 4],
+            "upsample_initial_channel": 1536,
+            "resblock_kernel_sizes": [3, 7, 11],
+            "resblock_dilation_sizes": [
+                [1, 3, 5],
+                [1, 3, 5],
+                [1, 3, 5],
+            ],
+            "activation": "snakebeta",
+            "snake_logscale": True,
+        }
+
+        self.vocoder = BigVGANVocoder(
+            bigvgan_config
+        ).eval()
+
+    @torch.inference_mode()
+    def encode_audio(self, x) -> DiagonalGaussianDistribution:
+        # x: (B * L)
+        mel = self.mel_converter(x)
+        dist = self.vae.encode(mel)
+
+        return dist
+
+    @torch.no_grad()
+    def decode(self, z):
+        mel_decoded = self.vae.decode(z)
+        audio = self.vocoder(mel_decoded)
+
+        audio = torchaudio.functional.resample(audio, 16000, 44100)
+        return audio
+
+    @torch.no_grad()
+    def encode(self, audio):
+        audio = audio.mean(dim=1)
+        audio = torchaudio.functional.resample(audio, 44100, 16000)
+        dist = self.encode_audio(audio)
+        return dist.mean

comfy/ldm/mmaudio/vae/bigvgan.py

@@ -0,0 +1,219 @@
+# Copyright (c) 2022 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+import torch.nn as nn
+from types import SimpleNamespace
+from . import activations
+from .alias_free_torch import Activation1d
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+class AMPBlock1(torch.nn.Module):
+
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
+        super(AMPBlock1, self).__init__()
+        self.h = h
+
+        self.convs1 = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[0],
+                       padding=get_padding(kernel_size, dilation[0])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[1],
+                       padding=get_padding(kernel_size, dilation[1])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[2],
+                       padding=get_padding(kernel_size, dilation[2]))
+        ])
+
+        self.convs2 = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1)),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1)),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1))
+        ])
+
+        self.num_layers = len(self.convs1) + len(self.convs2)  # total number of conv layers
+
+        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+    def forward(self, x):
+        acts1, acts2 = self.activations[::2], self.activations[1::2]
+        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+            xt = a1(x)
+            xt = c1(xt)
+            xt = a2(xt)
+            xt = c2(xt)
+            x = xt + x
+
+        return x
+
+
+class AMPBlock2(torch.nn.Module):
+
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
+        super(AMPBlock2, self).__init__()
+        self.h = h
+
+        self.convs = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[0],
+                       padding=get_padding(kernel_size, dilation[0])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[1],
+                       padding=get_padding(kernel_size, dilation[1]))
+        ])
+
+        self.num_layers = len(self.convs)  # total number of conv layers
+
+        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+    def forward(self, x):
+        for c, a in zip(self.convs, self.activations):
+            xt = a(x)
+            xt = c(xt)
+            x = xt + x
+
+        return x
+
+
+class BigVGANVocoder(torch.nn.Module):
+    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+    def __init__(self, h):
+        super().__init__()
+        if isinstance(h, dict):
+            h = SimpleNamespace(**h)
+        self.h = h
+
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+
+        # pre conv
+        self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)
+
+        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
+        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
+
+        # transposed conv-based upsamplers. does not apply anti-aliasing
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(
+                nn.ModuleList([
+                        ops.ConvTranspose1d(h.upsample_initial_channel // (2**i),
+                                        h.upsample_initial_channel // (2**(i + 1)),
+                                        k,
+                                        u,
+                                        padding=(k - u) // 2)
+                ]))
+
+        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2**(i + 1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
+
+        # post conv
+        if h.activation == "snake":  # periodic nonlinearity with snake function and anti-aliasing
+            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        elif h.activation == "snakebeta":  # periodic nonlinearity with snakebeta function and anti-aliasing
+            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+        self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3)
+
+
+    def forward(self, x):
+        # pre conv
+        x = self.conv_pre(x)
+
+        for i in range(self.num_upsamples):
+            # upsampling
+            for i_up in range(len(self.ups[i])):
+                x = self.ups[i][i_up](x)
+            # AMP blocks
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        # post conv
+        x = self.activation_post(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x

comfy/ldm/mmaudio/vae/distributions.py

@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )

comfy/ldm/mmaudio/vae/vae.py

@@ -0,0 +1,358 @@
+import logging
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D,
+                                                 Upsample1D, nonlinearity)
+from .distributions import DiagonalGaussianDistribution
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+log = logging.getLogger()
+
+DATA_MEAN_80D = [
+    -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,
+    -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728,
+    -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131,
+    -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280,
+    -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643,
+    -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436,
+    -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282,
+    -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673
+]
+
+DATA_STD_80D = [
+    1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263,
+    0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194,
+    0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043,
+    0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973,
+    0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939,
+    0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604,
+    1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070
+]
+
+DATA_MEAN_128D = [
+    -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597,
+    -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033,
+    -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157,
+    -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782,
+    -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647,
+    -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795,
+    -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121,
+    -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960,
+    -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712,
+    -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120,
+    -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663,
+    -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628,
+    -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861
+]
+
+DATA_STD_128D = [
+    2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659,
+    2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557,
+    2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182,
+    2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991,
+    2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900,
+    2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817,
+    2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609,
+    2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812,
+    2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451,
+    2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877,
+    2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164
+]
+
+
+class VAE(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        data_dim: int,
+        embed_dim: int,
+        hidden_dim: int,
+    ):
+        super().__init__()
+
+        if data_dim == 80:
+            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32))
+            self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32))
+        elif data_dim == 128:
+            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32))
+            self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32))
+
+        self.data_mean = self.data_mean.view(1, -1, 1)
+        self.data_std = self.data_std.view(1, -1, 1)
+
+        self.encoder = Encoder1D(
+            dim=hidden_dim,
+            ch_mult=(1, 2, 4),
+            num_res_blocks=2,
+            attn_layers=[3],
+            down_layers=[0],
+            in_dim=data_dim,
+            embed_dim=embed_dim,
+        )
+        self.decoder = Decoder1D(
+            dim=hidden_dim,
+            ch_mult=(1, 2, 4),
+            num_res_blocks=2,
+            attn_layers=[3],
+            down_layers=[0],
+            in_dim=data_dim,
+            out_dim=data_dim,
+            embed_dim=embed_dim,
+        )
+
+        self.embed_dim = embed_dim
+        # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1)
+        # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1)
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        pass
+
+    def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution:
+        if normalize:
+            x = self.normalize(x)
+        moments = self.encoder(x)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor:
+        dec = self.decoder(z)
+        if unnormalize:
+            dec = self.unnormalize(dec)
+        return dec
+
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
+        return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device)
+
+    def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
+        return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        sample_posterior: bool = True,
+        rng: Optional[torch.Generator] = None,
+        normalize: bool = True,
+        unnormalize: bool = True,
+    ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]:
+
+        posterior = self.encode(x, normalize=normalize)
+        if sample_posterior:
+            z = posterior.sample(rng)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z, unnormalize=unnormalize)
+        return dec, posterior
+
+    def load_weights(self, src_dict) -> None:
+        self.load_state_dict(src_dict, strict=True)
+
+    @property
+    def device(self) -> torch.device:
+        return next(self.parameters()).device
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def remove_weight_norm(self):
+        return self
+
+
+class Encoder1D(nn.Module):
+
+    def __init__(self,
+                 *,
+                 dim: int,
+                 ch_mult: tuple[int] = (1, 2, 4, 8),
+                 num_res_blocks: int,
+                 attn_layers: list[int] = [],
+                 down_layers: list[int] = [],
+                 resamp_with_conv: bool = True,
+                 in_dim: int,
+                 embed_dim: int,
+                 double_z: bool = True,
+                 kernel_size: int = 3,
+                 clip_act: float = 256.0):
+        super().__init__()
+        self.dim = dim
+        self.num_layers = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.in_channels = in_dim
+        self.clip_act = clip_act
+        self.down_layers = down_layers
+        self.attn_layers = attn_layers
+        self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        in_ch_mult = (1, ) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        # downsampling
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_layers):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = dim * in_ch_mult[i_level]
+            block_out = dim * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock1D(in_dim=block_in,
+                                  out_dim=block_out,
+                                  kernel_size=kernel_size,
+                                  use_norm=True))
+                block_in = block_out
+                if i_level in attn_layers:
+                    attn.append(AttnBlock1D(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level in down_layers:
+                down.downsample = Downsample1D(block_in, resamp_with_conv)
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1D(in_dim=block_in,
+                                         out_dim=block_in,
+                                         kernel_size=kernel_size,
+                                         use_norm=True)
+        self.mid.attn_1 = AttnBlock1D(block_in)
+        self.mid.block_2 = ResnetBlock1D(in_dim=block_in,
+                                         out_dim=block_in,
+                                         kernel_size=kernel_size,
+                                         use_norm=True)
+
+        # end
+        self.conv_out = ops.Conv1d(block_in,
+                                 2 * embed_dim if double_z else embed_dim,
+                                 kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        self.learnable_gain = nn.Parameter(torch.zeros([]))
+
+    def forward(self, x):
+
+        # downsampling
+        h = self.conv_in(x)
+        for i_level in range(self.num_layers):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](h)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                h = h.clamp(-self.clip_act, self.clip_act)
+            if i_level in self.down_layers:
+                h = self.down[i_level].downsample(h)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        h = h.clamp(-self.clip_act, self.clip_act)
+
+        # end
+        h = nonlinearity(h)
+        h = self.conv_out(h) * (self.learnable_gain + 1)
+        return h
+
+
+class Decoder1D(nn.Module):
+
+    def __init__(self,
+                 *,
+                 dim: int,
+                 out_dim: int,
+                 ch_mult: tuple[int] = (1, 2, 4, 8),
+                 num_res_blocks: int,
+                 attn_layers: list[int] = [],
+                 down_layers: list[int] = [],
+                 kernel_size: int = 3,
+                 resamp_with_conv: bool = True,
+                 in_dim: int,
+                 embed_dim: int,
+                 clip_act: float = 256.0):
+        super().__init__()
+        self.ch = dim
+        self.num_layers = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.in_channels = in_dim
+        self.clip_act = clip_act
+        self.down_layers = [i + 1 for i in down_layers]  # each downlayer add one
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = dim * ch_mult[self.num_layers - 1]
+
+        # z to block_in
+        self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
+        self.mid.attn_1 = AttnBlock1D(block_in)
+        self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_layers)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = dim * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True))
+                block_in = block_out
+                if i_level in attn_layers:
+                    attn.append(AttnBlock1D(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level in self.down_layers:
+                up.upsample = Upsample1D(block_in, resamp_with_conv)
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        self.learnable_gain = nn.Parameter(torch.zeros([]))
+
+    def forward(self, z):
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        h = h.clamp(-self.clip_act, self.clip_act)
+
+        # upsampling
+        for i_level in reversed(range(self.num_layers)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+                h = h.clamp(-self.clip_act, self.clip_act)
+            if i_level in self.down_layers:
+                h = self.up[i_level].upsample(h)
+
+        h = nonlinearity(h)
+        h = self.conv_out(h) * (self.learnable_gain + 1)
+        return h
+
+
+def VAE_16k(**kwargs) -> VAE:
+    return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs)
+
+
+def VAE_44k(**kwargs) -> VAE:
+    return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs)
+
+
+def get_my_vae(name: str, **kwargs) -> VAE:
+    if name == '16k':
+        return VAE_16k(**kwargs)
+    if name == '44k':
+        return VAE_44k(**kwargs)
+    raise ValueError(f'Unknown model: {name}')
+

comfy/ldm/mmaudio/vae/vae_modules.py

@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def nonlinearity(x):
+    # swish
+    return torch.nn.functional.silu(x) / 0.596
+
+def mp_sum(a, b, t=0.5):
+    return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2)
+
+def normalize(x, dim=None, eps=1e-4):
+    if dim is None:
+        dim = list(range(1, x.ndim))
+    norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
+    norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))
+    return x / norm.to(x.dtype)
+
+class ResnetBlock1D(nn.Module):
+
+    def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
+        super().__init__()
+        self.in_dim = in_dim
+        out_dim = in_dim if out_dim is None else out_dim
+        self.out_dim = out_dim
+        self.use_conv_shortcut = conv_shortcut
+        self.use_norm = use_norm
+
+        self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        if self.in_dim != self.out_dim:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+            else:
+                self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        # pixel norm
+        if self.use_norm:
+            x = normalize(x, dim=1)
+
+        h = x
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        h = nonlinearity(h)
+        h = self.conv2(h)
+
+        if self.in_dim != self.out_dim:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return mp_sum(x, h, t=0.3)
+
+
+class AttnBlock1D(nn.Module):
+
+    def __init__(self, in_channels, num_heads=1):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.num_heads = num_heads
+        self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False)
+        self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+        self.optimized_attention = vae_attention()
+
+    def forward(self, x):
+        h = x
+        y = self.qkv(h)
+        y = y.reshape(y.shape[0], -1, 3, y.shape[-1])
+        q, k, v = normalize(y, dim=1).unbind(2)
+
+        h = self.optimized_attention(q, k, v)
+        h = self.proj_out(h)
+
+        return mp_sum(x, h, t=0.3)
+
+
+class Upsample1D(nn.Module):
+
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = ops.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact')  # support 3D tensor(B,C,T)
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample1D(nn.Module):
+
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+            self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+
+    def forward(self, x):
+
+        if self.with_conv:
+            x = self.conv1(x)
+
+        x = F.avg_pool1d(x, kernel_size=2, stride=2)
+
+        if self.with_conv:
+            x = self.conv2(x)
+
+        return x

comfy/ldm/models/autoencoder.py

@@ -26,6 +26,12 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
             z = posterior.mode()
         return z, None
 
+class EmptyRegularizer(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        return z, None
 
 class AbstractAutoencoder(torch.nn.Module):
     """

comfy/ldm/modules/attention.py

@@ -5,8 +5,9 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional
+from typing import Optional, Any, Callable, Union
 import logging
+import functools
 
 from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -17,23 +18,45 @@ if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
-if model_management.sage_attention_enabled():
-    try:
-        from sageattention import sageattn
-    except ModuleNotFoundError as e:
+SAGE_ATTENTION_IS_AVAILABLE = False
+try:
+    from sageattention import sageattn
+    SAGE_ATTENTION_IS_AVAILABLE = True
+except ImportError as e:
+    if model_management.sage_attention_enabled():
         if e.name == "sageattention":
             logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
         else:
             raise e
         exit(-1)
 
-if model_management.flash_attention_enabled():
-    try:
-        from flash_attn import flash_attn_func
-    except ModuleNotFoundError:
+FLASH_ATTENTION_IS_AVAILABLE = False
+try:
+    from flash_attn import flash_attn_func
+    FLASH_ATTENTION_IS_AVAILABLE = True
+except ImportError:
+    if model_management.flash_attention_enabled():
         logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
         exit(-1)
 
+REGISTERED_ATTENTION_FUNCTIONS = {}
+def register_attention_function(name: str, func: Callable):
+    # avoid replacing existing functions
+    if name not in REGISTERED_ATTENTION_FUNCTIONS:
+        REGISTERED_ATTENTION_FUNCTIONS[name] = func
+    else:
+        logging.warning(f"Attention function {name} already registered, skipping registration.")
+
+def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]:
+    if name == "optimized":
+        return optimized_attention
+    elif name not in REGISTERED_ATTENTION_FUNCTIONS:
+        if default is ...:
+            raise KeyError(f"Attention function {name} not found.")
+        else:
+            return default
+    return REGISTERED_ATTENTION_FUNCTIONS[name]
+
 from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -91,7 +114,27 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+
+def wrap_attn(func):
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        remove_attn_wrapper_key = False
+        try:
+            if "_inside_attn_wrapper" not in kwargs:
+                transformer_options = kwargs.get("transformer_options", None)
+                remove_attn_wrapper_key = True
+                kwargs["_inside_attn_wrapper"] = True
+                if transformer_options is not None:
+                    if "optimized_attention_override" in transformer_options:
+                        return transformer_options["optimized_attention_override"](func, *args, **kwargs)
+            return func(*args, **kwargs)
+        finally:
+            if remove_attn_wrapper_key:
+                del kwargs["_inside_attn_wrapper"]
+    return wrapper
+
+@wrap_attn
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -159,8 +202,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         )
     return out
 
-
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     attn_precision = get_attn_precision(attn_precision, query.dtype)
 
     if skip_reshape:
@@ -230,7 +273,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -359,7 +403,8 @@ try:
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -374,7 +419,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -427,8 +472,8 @@ else:
     #TODO: other GPUs ?
     SDP_BATCH_LIMIT = 2**31
 
-
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -470,8 +515,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
-
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
@@ -501,7 +546,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
                 lambda t: t.transpose(1, 2),
                 (q, k, v),
             )
-        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
 
     if tensor_layout == "HND":
         if not skip_output_reshape:
@@ -534,8 +579,8 @@ except AttributeError as error:
                     dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
-
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -555,7 +600,8 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
             mask = mask.unsqueeze(1)
 
     try:
-        assert mask is None
+        if mask is not None:
+            raise RuntimeError("Mask must not be set for Flash attention")
         out = flash_attn_wrapper(
             q.transpose(1, 2),
             k.transpose(1, 2),
@@ -597,6 +643,19 @@ else:
 
 optimized_attention_masked = optimized_attention
 
+
+# register core-supported attention functions
+if SAGE_ATTENTION_IS_AVAILABLE:
+    register_attention_function("sage", attention_sage)
+if FLASH_ATTENTION_IS_AVAILABLE:
+    register_attention_function("flash", attention_flash)
+if model_management.xformers_enabled():
+    register_attention_function("xformers", attention_xformers)
+register_attention_function("pytorch", attention_pytorch)
+register_attention_function("sub_quad", attention_sub_quad)
+register_attention_function("split", attention_split)
+
+
 def optimized_attention_for_device(device, mask=False, small_input=False):
     if small_input:
         if model_management.pytorch_attention_enabled():
@@ -629,7 +688,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, value=None, mask=None):
+    def forward(self, x, context=None, value=None, mask=None, transformer_options={}):
         q = self.to_q(x)
         context = default(context, x)
         k = self.to_k(context)
@@ -640,9 +699,9 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
         return self.to_out(out)
 
 
@@ -746,7 +805,7 @@ class BasicTransformerBlock(nn.Module):
             n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
             n = self.attn1.to_out(n)
         else:
-            n = self.attn1(n, context=context_attn1, value=value_attn1)
+            n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options)
 
         if "attn1_output_patch" in transformer_patches:
             patch = transformer_patches["attn1_output_patch"]
@@ -786,7 +845,7 @@ class BasicTransformerBlock(nn.Module):
                 n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
                 n = self.attn2.to_out(n)
             else:
-                n = self.attn2(n, context=context_attn2, value=value_attn2)
+                n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options)
 
         if "attn2_output_patch" in transformer_patches:
             patch = transformer_patches["attn2_output_patch"]
@@ -1017,7 +1076,7 @@ class SpatialVideoTransformer(SpatialTransformer):
 
             B, S, C = x_mix.shape
             x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
-            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
+            x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options)
             x_mix = rearrange(
                 x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
             )

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -606,7 +606,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs):
         return _block_mixing(*args, **kwargs)
 
 
-def _block_mixing(context, x, context_block, x_block, c):
+def _block_mixing(context, x, context_block, x_block, c, transformer_options={}):
     context_qkv, context_intermediates = context_block.pre_attention(context, c)
 
     if x_block.x_block_self_attn:
@@ -622,6 +622,7 @@ def _block_mixing(context, x, context_block, x_block, c):
     attn = optimized_attention(
         qkv[0], qkv[1], qkv[2],
         heads=x_block.attn.num_heads,
+        transformer_options=transformer_options,
     )
     context_attn, x_attn = (
         attn[:, : context_qkv[0].shape[1]],
@@ -637,6 +638,7 @@ def _block_mixing(context, x, context_block, x_block, c):
         attn2 = optimized_attention(
                 x_qkv2[0], x_qkv2[1], x_qkv2[2],
                 heads=x_block.attn2.num_heads,
+                transformer_options=transformer_options,
             )
         x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
     else:
@@ -958,10 +960,10 @@ class MMDiT(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 context = out["txt"]
                 x = out["img"]
             else:
@@ -970,6 +972,7 @@ class MMDiT(nn.Module):
                     x,
                     c=c_mod,
                     use_checkpoint=self.use_checkpoint,
+                    transformer_options=transformer_options,
                 )
             if control is not None:
                 control_o = control.get("output")

comfy/ldm/modules/diffusionmodules/model.py

@@ -145,7 +145,7 @@ class Downsample(nn.Module):
 
 class ResnetBlock(nn.Module):
     def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512, conv_op=ops.Conv2d):
+                 dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize):
         super().__init__()
         self.in_channels = in_channels
         out_channels = in_channels if out_channels is None else out_channels
@@ -153,7 +153,7 @@ class ResnetBlock(nn.Module):
         self.use_conv_shortcut = conv_shortcut
 
         self.swish = torch.nn.SiLU(inplace=True)
-        self.norm1 = Normalize(in_channels)
+        self.norm1 = norm_op(in_channels)
         self.conv1 = conv_op(in_channels,
                                      out_channels,
                                      kernel_size=3,
@@ -162,7 +162,7 @@ class ResnetBlock(nn.Module):
         if temb_channels > 0:
             self.temb_proj = ops.Linear(temb_channels,
                                              out_channels)
-        self.norm2 = Normalize(out_channels)
+        self.norm2 = norm_op(out_channels)
         self.dropout = torch.nn.Dropout(dropout, inplace=True)
         self.conv2 = conv_op(out_channels,
                                      out_channels,
@@ -183,7 +183,7 @@ class ResnetBlock(nn.Module):
                                                     stride=1,
                                                     padding=0)
 
-    def forward(self, x, temb):
+    def forward(self, x, temb=None):
         h = x
         h = self.norm1(h)
         h = self.swish(h)
@@ -305,11 +305,11 @@ def vae_attention():
         return normal_attention
 
 class AttnBlock(nn.Module):
-    def __init__(self, in_channels, conv_op=ops.Conv2d):
+    def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize):
         super().__init__()
         self.in_channels = in_channels
 
-        self.norm = Normalize(in_channels)
+        self.norm = norm_op(in_channels)
         self.q = conv_op(in_channels,
                                  in_channels,
                                  kernel_size=1,

comfy/ldm/omnigen/omnigen2.py

@@ -120,7 +120,7 @@ class Attention(nn.Module):
             nn.Dropout(0.0)
         )
 
-    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
         batch_size, sequence_length, _ = hidden_states.shape
 
         query = self.to_q(hidden_states)
@@ -146,7 +146,7 @@ class Attention(nn.Module):
             key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
             value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
 
-        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
         hidden_states = self.to_out[0](hidden_states)
         return hidden_states
 
@@ -182,16 +182,16 @@ class OmniGen2TransformerBlock(nn.Module):
         self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
         self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
 
-    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
         if self.modulation:
             norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
             hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
             mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
             hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
         else:
             norm_hidden_states = self.norm1(hidden_states)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
             hidden_states = hidden_states + self.norm2(attn_output)
             mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
             hidden_states = hidden_states + self.ffn_norm2(mlp_output)
@@ -390,7 +390,7 @@ class OmniGen2Transformer2DModel(nn.Module):
             ref_img_sizes, img_sizes,
         )
 
-    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
+    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}):
         batch_size = len(hidden_states)
 
         hidden_states = self.x_embedder(hidden_states)
@@ -405,17 +405,17 @@ class OmniGen2Transformer2DModel(nn.Module):
                     shift += ref_img_len
 
         for layer in self.noise_refiner:
-            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
+            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options)
 
         if ref_image_hidden_states is not None:
             for layer in self.ref_image_refiner:
-                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
+                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options)
 
             hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
 
         return hidden_states
 
-    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):
         B, C, H, W = x.shape
         hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
         _, _, H_padded, W_padded = hidden_states.shape
@@ -444,7 +444,7 @@ class OmniGen2Transformer2DModel(nn.Module):
         )
 
         for layer in self.context_refiner:
-            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
+            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)
 
         img_len = hidden_states.shape[1]
         combined_img_hidden_states = self.img_patch_embed_and_refine(
@@ -453,13 +453,14 @@ class OmniGen2Transformer2DModel(nn.Module):
             noise_rotary_emb, ref_img_rotary_emb,
             l_effective_ref_img_len, l_effective_img_len,
             temb,
+            transformer_options=transformer_options,
         )
 
         hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
         attention_mask = None
 
         for layer in self.layers:
-            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
+            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options)
 
         hidden_states = self.norm_out(hidden_states, temb)
 

comfy/ldm/qwen_image/controlnet.py

@@ -44,7 +44,7 @@ class QwenImageControlNetModel(QwenImageTransformer2DModel):
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
         ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
         del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)

comfy/ldm/qwen_image/model.py

@@ -10,6 +10,7 @@ from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
 import comfy.ldm.common_dit
 import comfy.patcher_extension
+from comfy.ldm.flux.math import apply_rope1
 
 class GELU(nn.Module):
     def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None):
@@ -132,34 +133,36 @@ class Attention(nn.Module):
         encoder_hidden_states_mask: torch.FloatTensor = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         image_rotary_emb: Optional[torch.Tensor] = None,
+        transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size = hidden_states.shape[0]
+        seq_img = hidden_states.shape[1]
         seq_txt = encoder_hidden_states.shape[1]
 
-        img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1))
-        img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1))
-        img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1))
+        # Project and reshape to BHND format (batch, heads, seq, dim)
+        img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
+        img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
+        img_value = self.to_v(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2)
 
-        txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
+        txt_query = self.add_q_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
+        txt_key = self.add_k_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
+        txt_value = self.add_v_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2)
 
         img_query = self.norm_q(img_query)
         img_key = self.norm_k(img_key)
         txt_query = self.norm_added_q(txt_query)
         txt_key = self.norm_added_k(txt_key)
 
-        joint_query = torch.cat([txt_query, img_query], dim=1)
-        joint_key = torch.cat([txt_key, img_key], dim=1)
-        joint_value = torch.cat([txt_value, img_value], dim=1)
+        joint_query = torch.cat([txt_query, img_query], dim=2)
+        joint_key = torch.cat([txt_key, img_key], dim=2)
+        joint_value = torch.cat([txt_value, img_value], dim=2)
 
-        joint_query = apply_rotary_emb(joint_query, image_rotary_emb)
-        joint_key = apply_rotary_emb(joint_key, image_rotary_emb)
+        joint_query = apply_rope1(joint_query, image_rotary_emb)
+        joint_key = apply_rope1(joint_key, image_rotary_emb)
 
-        joint_query = joint_query.flatten(start_dim=2)
-        joint_key = joint_key.flatten(start_dim=2)
-        joint_value = joint_value.flatten(start_dim=2)
-
-        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask)
+        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
+                                                         attention_mask, transformer_options=transformer_options,
+                                                         skip_reshape=True)
 
         txt_attn_output = joint_hidden_states[:, :seq_txt, :]
         img_attn_output = joint_hidden_states[:, seq_txt:, :]
@@ -226,33 +229,39 @@ class QwenImageTransformerBlock(nn.Module):
         encoder_hidden_states_mask: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         img_mod_params = self.img_mod(temb)
         txt_mod_params = self.txt_mod(temb)
         img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)
         txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)
 
-        img_normed = self.img_norm1(hidden_states)
-        img_modulated, img_gate1 = self._modulate(img_normed, img_mod1)
-        txt_normed = self.txt_norm1(encoder_hidden_states)
-        txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1)
+        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1)
+        del img_mod1
+        txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)
+        del txt_mod1
 
         img_attn_output, txt_attn_output = self.attn(
             hidden_states=img_modulated,
             encoder_hidden_states=txt_modulated,
             encoder_hidden_states_mask=encoder_hidden_states_mask,
             image_rotary_emb=image_rotary_emb,
+            transformer_options=transformer_options,
         )
+        del img_modulated
+        del txt_modulated
 
         hidden_states = hidden_states + img_gate1 * img_attn_output
         encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
+        del img_attn_output
+        del txt_attn_output
+        del img_gate1
+        del txt_gate1
 
-        img_normed2 = self.img_norm2(hidden_states)
-        img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2)
+        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2)
         hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2))
 
-        txt_normed2 = self.txt_norm2(encoder_hidden_states)
-        txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2)
+        txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)
         encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))
 
         return encoder_hidden_states, hidden_states
@@ -410,7 +419,7 @@ class QwenImageTransformer2DModel(nn.Module):
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
         ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
         del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)
@@ -434,9 +443,9 @@ class QwenImageTransformer2DModel(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"])
+                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 hidden_states = out["img"]
                 encoder_hidden_states = out["txt"]
             else:
@@ -446,11 +455,12 @@ class QwenImageTransformer2DModel(nn.Module):
                     encoder_hidden_states_mask=encoder_hidden_states_mask,
                     temb=temb,
                     image_rotary_emb=image_rotary_emb,
+                    transformer_options=transformer_options,
                 )
 
             if "double_block" in patches:
                 for p in patches["double_block"]:
-                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i})
+                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options})
                     hidden_states = out["img"]
                     encoder_hidden_states = out["txt"]
 

comfy/ldm/wan/model.py

@@ -8,7 +8,7 @@ from einops import rearrange
 
 from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.flux.layers import EmbedND
-from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.flux.math import apply_rope1
 import comfy.ldm.common_dit
 import comfy.model_management
 import comfy.patcher_extension
@@ -34,7 +34,9 @@ class WanSelfAttention(nn.Module):
                  num_heads,
                  window_size=(-1, -1),
                  qk_norm=True,
-                 eps=1e-6, operation_settings={}):
+                 eps=1e-6,
+                 kv_dim=None,
+                 operation_settings={}):
         assert dim % num_heads == 0
         super().__init__()
         self.dim = dim
@@ -43,16 +45,18 @@ class WanSelfAttention(nn.Module):
         self.window_size = window_size
         self.qk_norm = qk_norm
         self.eps = eps
+        if kv_dim is None:
+            kv_dim = dim
 
         # layers
         self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
         self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, freqs):
+    def forward(self, x, freqs, transformer_options={}):
         r"""
         Args:
             x(Tensor): Shape [B, L, num_heads, C / num_heads]
@@ -60,21 +64,26 @@ class WanSelfAttention(nn.Module):
         """
         b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
 
-        # query, key, value function
-        def qkv_fn(x):
+        def qkv_fn_q(x):
             q = self.norm_q(self.q(x)).view(b, s, n, d)
-            k = self.norm_k(self.k(x)).view(b, s, n, d)
-            v = self.v(x).view(b, s, n * d)
-            return q, k, v
+            return apply_rope1(q, freqs)
 
-        q, k, v = qkv_fn(x)
-        q, k = apply_rope(q, k, freqs)
+        def qkv_fn_k(x):
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            return apply_rope1(k, freqs)
+
+        #These two are VRAM hogs, so we want to do all of q computation and
+        #have pytorch garbage collect the intermediates on the sub function
+        #return before we touch k
+        q = qkv_fn_q(x)
+        k = qkv_fn_k(x)
 
         x = optimized_attention(
             q.view(b, s, n * d),
             k.view(b, s, n * d),
-            v,
+            self.v(x).view(b, s, n * d),
             heads=self.num_heads,
+            transformer_options=transformer_options,
         )
 
         x = self.o(x)
@@ -83,7 +92,7 @@ class WanSelfAttention(nn.Module):
 
 class WanT2VCrossAttention(WanSelfAttention):
 
-    def forward(self, x, context, **kwargs):
+    def forward(self, x, context, transformer_options={}, **kwargs):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -95,7 +104,7 @@ class WanT2VCrossAttention(WanSelfAttention):
         v = self.v(context)
 
         # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads)
+        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)
 
         x = self.o(x)
         return x
@@ -116,7 +125,7 @@ class WanI2VCrossAttention(WanSelfAttention):
         # self.alpha = nn.Parameter(torch.zeros((1, )))
         self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, context, context_img_len):
+    def forward(self, x, context, context_img_len, transformer_options={}):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -131,9 +140,9 @@ class WanI2VCrossAttention(WanSelfAttention):
         v = self.v(context)
         k_img = self.norm_k_img(self.k_img(context_img))
         v_img = self.v_img(context_img)
-        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
+        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options)
         # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads)
+        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)
 
         # output
         x = x + img_x
@@ -206,6 +215,7 @@ class WanAttentionBlock(nn.Module):
         freqs,
         context,
         context_img_len=257,
+        transformer_options={},
     ):
         r"""
         Args:
@@ -222,14 +232,16 @@ class WanAttentionBlock(nn.Module):
         # assert e[0].dtype == torch.float32
 
         # self-attention
+        x = x.contiguous() # otherwise implicit in LayerNorm
         y = self.self_attn(
             torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
-            freqs)
+            freqs, transformer_options=transformer_options)
 
         x = torch.addcmul(x, y, repeat_e(e[2], x))
+        del y
 
         # cross-attention & ffn
-        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
         y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
         x = torch.addcmul(x, y, repeat_e(e[5], x))
         return x
@@ -396,6 +408,7 @@ class WanModel(torch.nn.Module):
                  eps=1e-6,
                  flf_pos_embed_token_number=None,
                  in_dim_ref_conv=None,
+                 wan_attn_block_class=WanAttentionBlock,
                  image_model=None,
                  device=None,
                  dtype=None,
@@ -473,8 +486,8 @@ class WanModel(torch.nn.Module):
         # blocks
         cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
         self.blocks = nn.ModuleList([
-            WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
-                              window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
+            wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,
+                                 window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
             for _ in range(num_layers)
         ])
 
@@ -559,12 +572,12 @@ class WanModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
         # head
         x = self.head(x, e)
@@ -576,7 +589,7 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None):
+    def rope_encode(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])
@@ -589,10 +602,22 @@ class WanModel(torch.nn.Module):
         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)
+
         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(0, h_len - 1, steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
-        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=steps_w, 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(img_ids).movedim(1, 2)
@@ -618,7 +643,7 @@ class WanModel(torch.nn.Module):
         if self.ref_conv is not None and "reference_latent" in kwargs:
             t_len += 1
 
-        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype)
+        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
         return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
 
     def unpatchify(self, x, grid_sizes):
@@ -742,17 +767,17 @@ class VaceWanModel(WanModel):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
             ii = self.vace_layers_mapping.get(i, None)
             if ii is not None:
                 for iii in range(len(c)):
-                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
                     x += c_skip * vace_strength[iii]
                 del c_skip
         # head
@@ -841,12 +866,12 @@ class CameraWanModel(WanModel):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
 
         # head
         x = self.head(x, e)
@@ -891,7 +916,7 @@ class MotionEncoder_tc(nn.Module):
     def __init__(self,
                  in_dim: int,
                  hidden_dim: int,
-                 num_heads=int,
+                 num_heads: int,
                  need_global=True,
                  dtype=None,
                  device=None,
@@ -1319,3 +1344,250 @@ class WanModel_S2V(WanModel):
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
+
+
+class WanT2VCrossAttentionGather(WanSelfAttention):
+
+    def forward(self, x, context, transformer_options={}, **kwargs):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C] - video tokens
+            context(Tensor): Shape [B, L2, C] - audio tokens with shape [B, frames*16, 1536]
+        """
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v = self.v(context)
+
+        # Handle audio temporal structure (16 tokens per frame)
+        k = k.reshape(-1, 16, n, d).transpose(1, 2)
+        v = v.reshape(-1, 16, n, d).transpose(1, 2)
+
+        # Handle video spatial structure
+        q = q.reshape(k.shape[0], -1, n, d).transpose(1, 2)
+
+        x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+
+        x = x.transpose(1, 2).reshape(b, -1, n * d)
+        x = self.o(x)
+        return x
+
+
+class AudioCrossAttentionWrapper(nn.Module):
+    def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, operation_settings={}):
+        super().__init__()
+
+        self.audio_cross_attn = WanT2VCrossAttentionGather(dim, num_heads, qk_norm=qk_norm, kv_dim=kv_dim, eps=eps, operation_settings=operation_settings)
+        self.norm1_audio = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x, audio, transformer_options={}):
+        x = x + self.audio_cross_attn(self.norm1_audio(x), audio, transformer_options=transformer_options)
+        return x
+
+
+class WanAttentionBlockAudio(WanAttentionBlock):
+
+    def __init__(self,
+                 cross_attn_type,
+                 dim,
+                 ffn_dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=False,
+                 eps=1e-6, operation_settings={}):
+        super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings)
+        self.audio_cross_attn_wrapper = AudioCrossAttentionWrapper(dim, 1536, num_heads, qk_norm, eps, operation_settings=operation_settings)
+
+    def forward(
+        self,
+        x,
+        e,
+        freqs,
+        context,
+        context_img_len=257,
+        audio=None,
+        transformer_options={},
+    ):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, 6, C]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        # assert e.dtype == torch.float32
+
+        if e.ndim < 4:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
+        # assert e[0].dtype == torch.float32
+
+        # self-attention
+        y = self.self_attn(
+            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
+            freqs, transformer_options=transformer_options)
+
+        x = torch.addcmul(x, y, repeat_e(e[2], x))
+
+        # cross-attention & ffn
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
+        if audio is not None:
+            x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options)
+        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
+        x = torch.addcmul(x, y, repeat_e(e[5], x))
+        return x
+
+class DummyAdapterLayer(nn.Module):
+    def __init__(self, layer):
+        super().__init__()
+        self.layer = layer
+
+    def forward(self, *args, **kwargs):
+        return self.layer(*args, **kwargs)
+
+
+class AudioProjModel(nn.Module):
+    def __init__(
+        self,
+        seq_len=5,
+        blocks=13,  # add a new parameter blocks
+        channels=768,  # add a new parameter channels
+        intermediate_dim=512,
+        output_dim=1536,
+        context_tokens=16,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        self.seq_len = seq_len
+        self.blocks = blocks
+        self.channels = channels
+        self.input_dim = seq_len * blocks * channels  # update input_dim to be the product of blocks and channels.
+        self.intermediate_dim = intermediate_dim
+        self.context_tokens = context_tokens
+        self.output_dim = output_dim
+
+        # define multiple linear layers
+        self.audio_proj_glob_1 = DummyAdapterLayer(operations.Linear(self.input_dim, intermediate_dim, dtype=dtype, device=device))
+        self.audio_proj_glob_2 = DummyAdapterLayer(operations.Linear(intermediate_dim, intermediate_dim, dtype=dtype, device=device))
+        self.audio_proj_glob_3 = DummyAdapterLayer(operations.Linear(intermediate_dim, context_tokens * output_dim, dtype=dtype, device=device))
+
+        self.audio_proj_glob_norm = DummyAdapterLayer(operations.LayerNorm(output_dim, dtype=dtype, device=device))
+
+    def forward(self, audio_embeds):
+        video_length = audio_embeds.shape[1]
+        audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
+        batch_size, window_size, blocks, channels = audio_embeds.shape
+        audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
+
+        audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds))
+        audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds))
+
+        context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim)
+
+        context_tokens = self.audio_proj_glob_norm(context_tokens)
+        context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
+
+        return context_tokens
+
+
+class HumoWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='humo',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 image_model=None,
+                 audio_token_num=16,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, wan_attn_block_class=WanAttentionBlockAudio, image_model=image_model, device=device, dtype=dtype, operations=operations)
+
+        self.audio_proj = AudioProjModel(seq_len=8, blocks=5, channels=1280, intermediate_dim=512, output_dim=1536, context_tokens=audio_token_num, dtype=dtype, device=device, operations=operations)
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        freqs=None,
+        audio_embed=None,
+        reference_latent=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        bs, _, time, height, width = x.shape
+
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        if reference_latent is not None:
+            ref = self.patch_embedding(reference_latent.float()).to(x.dtype)
+            ref = ref.flatten(2).transpose(1, 2)
+            freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=time, device=x.device, dtype=x.dtype)
+            x = torch.cat([x, ref], dim=1)
+            freqs = torch.cat([freqs, freqs_ref], dim=1)
+            del ref, freqs_ref
+
+        # context
+        context = self.text_embedding(context)
+        context_img_len = None
+
+        if audio_embed is not None:
+            if reference_latent is not None:
+                zero_audio_pad = torch.zeros(audio_embed.shape[0], reference_latent.shape[-3], *audio_embed.shape[2:], device=audio_embed.device, dtype=audio_embed.dtype)
+                audio_embed = torch.cat([audio_embed, zero_audio_pad], dim=1)
+            audio = self.audio_proj(audio_embed).permute(0, 3, 1, 2).flatten(2).transpose(1, 2)
+        else:
+            audio = None
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, audio=audio, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options)
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x

comfy/ldm/wan/model_animate.py

@@ -0,0 +1,548 @@
+from torch import nn
+import torch
+from typing import Tuple, Optional
+from einops import rearrange
+import torch.nn.functional as F
+import math
+from .model import WanModel, sinusoidal_embedding_1d
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.model_management
+
+class CausalConv1d(nn.Module):
+
+    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs):
+        super().__init__()
+
+        self.pad_mode = pad_mode
+        padding = (kernel_size - 1, 0)  # T
+        self.time_causal_padding = padding
+
+        self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class FaceEncoder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)
+        self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.act = nn.SiLU()
+        self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
+        self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
+
+        self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs)
+        self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))
+
+    def forward(self, x):
+
+        x = rearrange(x, "b t c -> b c t")
+        b, c, t = x.shape
+
+        x = self.conv1_local(x)
+        x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
+
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv2(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv3(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm3(x)
+        x = self.act(x)
+        x = self.out_proj(x)
+        x = rearrange(x, "(b n) t c -> b t n c", b=b)
+        padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)
+        x = torch.cat([x, padding], dim=-2)
+        x_local = x.clone()
+
+        return x_local
+
+
+def get_norm_layer(norm_layer, operations=None):
+    """
+    Get the normalization layer.
+
+    Args:
+        norm_layer (str): The type of normalization layer.
+
+    Returns:
+        norm_layer (nn.Module): The normalization layer.
+    """
+    if norm_layer == "layer":
+        return operations.LayerNorm
+    elif norm_layer == "rms":
+        return operations.RMSNorm
+    else:
+        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+
+
+class FaceAdapter(nn.Module):
+    def __init__(
+        self,
+        hidden_dim: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        num_adapter_layers: int = 1,
+        dtype=None, device=None, operations=None
+    ):
+
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.hidden_size = hidden_dim
+        self.heads_num = heads_num
+        self.fuser_blocks = nn.ModuleList(
+            [
+                FaceBlock(
+                    self.hidden_size,
+                    self.heads_num,
+                    qk_norm=qk_norm,
+                    qk_norm_type=qk_norm_type,
+                    operations=operations,
+                    **factory_kwargs,
+                )
+                for _ in range(num_adapter_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_embed: torch.Tensor,
+        idx: int,
+        freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
+        freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
+
+
+
+class FaceBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        qk_scale: float = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        operations=None
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+
+        self.deterministic = False
+        self.hidden_size = hidden_size
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
+        self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations)
+        self.q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+        self.k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+
+        self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_vec: torch.Tensor,
+        motion_mask: Optional[torch.Tensor] = None,
+        # use_context_parallel=False,
+    ) -> torch.Tensor:
+
+        B, T, N, C = motion_vec.shape
+        T_comp = T
+
+        x_motion = self.pre_norm_motion(motion_vec)
+        x_feat = self.pre_norm_feat(x)
+
+        kv = self.linear1_kv(x_motion)
+        q = self.linear1_q(x_feat)
+
+        k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
+        q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
+
+        # Apply QK-Norm if needed.
+        q = self.q_norm(q).to(v)
+        k = self.k_norm(k).to(v)
+
+        k = rearrange(k, "B L N H D -> (B L) N H D")
+        v = rearrange(v, "B L N H D -> (B L) N H D")
+
+        q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp)
+
+        attn = optimized_attention(q, k, v, heads=self.heads_num)
+
+        attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)
+
+        output = self.linear2(attn)
+
+        if motion_mask is not None:
+            output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
+
+        return output
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162
+def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
+    _, minor, in_h, in_w = input.shape
+    kernel_h, kernel_w = kernel.shape
+
+    out = input.view(-1, minor, in_h, 1, in_w, 1)
+    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
+    out = out.view(-1, minor, in_h * up_y, in_w * up_x)
+
+    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
+    out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)]
+
+    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
+    return out[:, :, ::down_y, ::down_x]
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81
+class FusedLeakyReLU(torch.nn.Module):
+    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None):
+        super().__init__()
+        self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device))
+        self.negative_slope = negative_slope
+        self.scale = scale
+
+    def forward(self, input):
+        return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale)
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+    return F.leaky_relu(input + bias, negative_slope) * scale
+
+class Blur(torch.nn.Module):
+    def __init__(self, kernel, pad, dtype=None, device=None):
+        super().__init__()
+        kernel = torch.tensor(kernel, dtype=dtype, device=device)
+        kernel = kernel[None, :] * kernel[:, None]
+        kernel = kernel / kernel.sum()
+        self.register_buffer('kernel', kernel)
+        self.pad = pad
+
+    def forward(self, input):
+        return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad)
+
+#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590
+class ScaledLeakyReLU(torch.nn.Module):
+    def __init__(self, negative_slope=0.2):
+        super().__init__()
+        self.negative_slope = negative_slope
+
+    def forward(self, input):
+        return F.leaky_relu(input, negative_slope=self.negative_slope)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605
+class EqualConv2d(torch.nn.Module):
+    def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype))
+        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+        self.stride = stride
+        self.padding = padding
+        self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None
+
+    def forward(self, input):
+        if self.bias is None:
+            bias = None
+        else:
+            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype)
+
+        return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134
+class EqualLinear(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype))
+        self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None
+        self.activation = activation
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+        if self.bias is None:
+            bias = None
+        else:
+            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul
+
+        if self.activation:
+            out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale)
+            return fused_leaky_relu(out, bias)
+        return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654
+class ConvLayer(torch.nn.Sequential):
+    def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None):
+        layers = []
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))
+            stride, padding = 2, 0
+        else:
+            stride, padding = 1, kernel_size // 2
+
+        layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations))
+
+        if activate:
+            layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))
+
+        super().__init__(*layers)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704
+class ResBlock(torch.nn.Module):
+    def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations)
+        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations)
+        self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, input):
+        out = self.conv2(self.conv1(input))
+        skip = self.skip(input)
+        return (out + skip) / math.sqrt(2)
+
+
+class EncoderApp(torch.nn.Module):
+    def __init__(self, w_dim=512, dtype=None, device=None, operations=None):
+        super().__init__()
+        kwargs = {"device": device, "dtype": dtype, "operations": operations}
+
+        self.convs = torch.nn.ModuleList([
+            ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs),
+            ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs),
+            ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs),
+            ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs),
+            EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs)
+        ])
+
+    def forward(self, x):
+        h = x
+        for conv in self.convs:
+            h = conv(h)
+        return h.squeeze(-1).squeeze(-1)
+
+class Encoder(torch.nn.Module):
+    def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations)
+        self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)])
+
+    def encode_motion(self, x):
+        return self.fc(self.net_app(x))
+
+class Direction(torch.nn.Module):
+    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype))
+        self.motion_dim = motion_dim
+
+    def forward(self, input):
+        stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype)
+        Q, _ = torch.linalg.qr(stabilized_weight.float())
+        if input is None:
+            return Q
+        return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1)
+
+class Synthesis(torch.nn.Module):
+    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations)
+
+class Generator(torch.nn.Module):
+    def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations)
+        self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations)
+
+    def get_motion(self, img):
+        motion_feat = self.enc.encode_motion(img)
+        return self.dec.direction(motion_feat)
+
+class AnimateWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='animate',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 motion_encoder_dim=512,
+                 image_model=None,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
+
+        self.pose_patch_embedding = operations.Conv3d(
+            16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype
+        )
+
+        self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations)
+
+        self.face_adapter = FaceAdapter(
+            heads_num=self.num_heads,
+            hidden_dim=self.dim,
+            num_adapter_layers=self.num_layers // 5,
+            device=device, dtype=dtype, operations=operations
+        )
+
+        self.face_encoder = FaceEncoder(
+            in_dim=motion_encoder_dim,
+            hidden_dim=self.dim,
+            num_heads=4,
+            device=device, dtype=dtype, operations=operations
+        )
+
+    def after_patch_embedding(self, x, pose_latents, face_pixel_values):
+        if pose_latents is not None:
+            pose_latents = self.pose_patch_embedding(pose_latents)
+            x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1]
+
+        if face_pixel_values is None:
+            return x, None
+
+        b, c, T, h, w = face_pixel_values.shape
+        face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
+        encode_bs = 8
+        face_pixel_values_tmp = []
+        for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)):
+            face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs]))
+
+        motion_vec = torch.cat(face_pixel_values_tmp)
+
+        motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
+        motion_vec = self.face_encoder(motion_vec)
+
+        B, L, H, C = motion_vec.shape
+        pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
+        motion_vec = torch.cat([pad_face, motion_vec], dim=1)
+
+        if motion_vec.shape[1] < x.shape[2]:
+            B, L, H, C = motion_vec.shape
+            pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec)
+            motion_vec = torch.cat([motion_vec, pad], dim=1)
+        else:
+            motion_vec = motion_vec[:, :x.shape[2]]
+        return x, motion_vec
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea=None,
+        pose_latents=None,
+        face_pixel_values=None,
+        freqs=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        full_ref = None
+        if self.ref_conv is not None:
+            full_ref = kwargs.get("reference_latent", None)
+            if full_ref is not None:
+                full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
+                x = torch.concat((full_ref, x), dim=1)
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+
+            if i % 5 == 0 and motion_vec is not None:
+                x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)
+
+        # head
+        x = self.head(x, e)
+
+        if full_ref is not None:
+            x = x[:, full_ref.shape[1]:]
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x

comfy/ldm/wan/vae.py

@@ -468,55 +468,46 @@ class WanVAE(nn.Module):
                                  attn_scales, self.temperal_upsample, dropout)
 
     def encode(self, x):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.decoder)
         ## cache
         t = x.shape[2]
         iter_ = 1 + (t - 1) // 4
         ## 对encode输入的x，按时间拆分为1、4、4、4....
         for i in range(iter_):
-            self._enc_conv_idx = [0]
+            conv_idx = [0]
             if i == 0:
                 out = self.encoder(
                     x[:, :, :1, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
             else:
                 out_ = self.encoder(
                     x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
                 out = torch.cat([out, out_], 2)
         mu, log_var = self.conv1(out).chunk(2, dim=1)
-        self.clear_cache()
         return mu
 
     def decode(self, z):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.decoder)
         # z: [b,c,t,h,w]
 
         iter_ = z.shape[2]
         x = self.conv2(z)
         for i in range(iter_):
-            self._conv_idx = [0]
+            conv_idx = [0]
             if i == 0:
                 out = self.decoder(
                     x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
             else:
                 out_ = self.decoder(
                     x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
                 out = torch.cat([out, out_], 2)
-        self.clear_cache()
         return out
-
-    def clear_cache(self):
-        self._conv_num = count_conv3d(self.decoder)
-        self._conv_idx = [0]
-        self._feat_map = [None] * self._conv_num
-        #cache encode
-        self._enc_conv_num = count_conv3d(self.encoder)
-        self._enc_conv_idx = [0]
-        self._enc_feat_map = [None] * self._enc_conv_num

comfy/ldm/wan/vae2_2.py

@@ -657,51 +657,51 @@ class WanVAE(nn.Module):
         )
 
     def encode(self, x):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.encoder)
         x = patchify(x, patch_size=2)
         t = x.shape[2]
         iter_ = 1 + (t - 1) // 4
         for i in range(iter_):
-            self._enc_conv_idx = [0]
+            conv_idx = [0]
             if i == 0:
                 out = self.encoder(
                     x[:, :, :1, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx,
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx,
                 )
             else:
                 out_ = self.encoder(
                     x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx,
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx,
                 )
                 out = torch.cat([out, out_], 2)
         mu, log_var = self.conv1(out).chunk(2, dim=1)
-        self.clear_cache()
         return mu
 
     def decode(self, z):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.decoder)
         iter_ = z.shape[2]
         x = self.conv2(z)
         for i in range(iter_):
-            self._conv_idx = [0]
+            conv_idx = [0]
             if i == 0:
                 out = self.decoder(
                     x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx,
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx,
                     first_chunk=True,
                 )
             else:
                 out_ = self.decoder(
                     x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx,
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx,
                 )
                 out = torch.cat([out, out_], 2)
         out = unpatchify(out, patch_size=2)
-        self.clear_cache()
         return out
 
     def reparameterize(self, mu, log_var):
@@ -715,12 +715,3 @@ class WanVAE(nn.Module):
             return mu
         std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
         return mu + std * torch.randn_like(std)
-
-    def clear_cache(self):
-        self._conv_num = count_conv3d(self.decoder)
-        self._conv_idx = [0]
-        self._feat_map = [None] * self._conv_num
-        # cache encode
-        self._enc_conv_num = count_conv3d(self.encoder)
-        self._enc_conv_idx = [0]
-        self._enc_feat_map = [None] * self._enc_conv_num

comfy/lora.py

@@ -260,6 +260,10 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
                 key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
+        for k in sdk:
+            hidden_size = model.model_config.unet_config.get("hidden_size", 0)
+            if k.endswith(".weight") and ".linear1." in k:
+                key_map["{}".format(k.replace(".linear1.weight", ".linear1_qkv"))] = (k, (0, 0, hidden_size * 3))
 
     if isinstance(model, comfy.model_base.GenmoMochi):
         for k in sdk:
@@ -293,6 +297,12 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = k[len("diffusion_model."):-len(".weight")]
                 key_map["{}".format(key_lora)] = k
 
+    if isinstance(model, comfy.model_base.Omnigen2):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+
     if isinstance(model, comfy.model_base.QwenImage):
         for k in sdk:
             if k.startswith("diffusion_model.") and k.endswith(".weight"): #QwenImage lora format

comfy/lora_convert.py

@@ -15,10 +15,29 @@ def convert_lora_bfl_control(sd): #BFL loras for Flux
 def convert_lora_wan_fun(sd): #Wan Fun loras
     return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"})
 
+def convert_uso_lora(sd):
+    sd_out = {}
+    for k in sd:
+        tensor = sd[k]
+        k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight")
+                                           .replace(".up.weight", ".lora_up.weight")
+                                           .replace(".qkv_lora2.", ".txt_attn.qkv.")
+                                           .replace(".qkv_lora1.", ".img_attn.qkv.")
+                                           .replace(".proj_lora1.", ".img_attn.proj.")
+                                           .replace(".proj_lora2.", ".txt_attn.proj.")
+                                           .replace(".qkv_lora.", ".linear1_qkv.")
+                                           .replace(".proj_lora.", ".linear2.")
+                                           .replace(".processor.", ".")
+                                           )
+        sd_out[k_to] = tensor
+    return sd_out
+
 
 def convert_lora(sd):
     if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
         return convert_lora_bfl_control(sd)
     if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd:
         return convert_lora_wan_fun(sd)
+    if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd:
+        return convert_uso_lora(sd)
     return sd

comfy/model_base.py

@@ -16,6 +16,8 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
+import comfy.ldm.hunyuan3dv2_1
+import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
@@ -37,9 +39,11 @@ import comfy.ldm.cosmos.model
 import comfy.ldm.cosmos.predict2
 import comfy.ldm.lumina.model
 import comfy.ldm.wan.model
+import comfy.ldm.wan.model_animate
 import comfy.ldm.hunyuan3d.model
 import comfy.ldm.hidream.model
 import comfy.ldm.chroma.model
+import comfy.ldm.chroma_radiance.model
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
@@ -130,10 +134,11 @@ class BaseModel(torch.nn.Module):
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
                 fp8 = model_config.optimizations.get("fp8", False)
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8, model_config=model_config)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
+            self.diffusion_model.eval()
             if comfy.model_management.force_channels_last():
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
@@ -192,8 +197,14 @@ class BaseModel(torch.nn.Module):
             extra_conds[o] = extra
 
         t = self.process_timestep(t, x=x, **extra_conds)
-        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
-        return self.model_sampling.calculate_denoised(sigma, model_output, x)
+        if "latent_shapes" in extra_conds:
+            xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes"))
+
+        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds)
+        if len(model_output) > 1 and not torch.is_tensor(model_output):
+            model_output, _ = utils.pack_latents(model_output)
+
+        return self.model_sampling.calculate_denoised(sigma, model_output.float(), x)
 
     def process_timestep(self, timestep, **kwargs):
         return timestep
@@ -322,6 +333,14 @@ class BaseModel(torch.nn.Module):
         if self.model_config.scaled_fp8 is not None:
             unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
 
+        # Save mixed precision metadata
+        if hasattr(self.model_config, 'layer_quant_config') and self.model_config.layer_quant_config:
+            metadata = {
+                "format_version": "1.0",
+                "layers": self.model_config.layer_quant_config
+            }
+            unet_state_dict["_quantization_metadata"] = metadata
+
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:
@@ -665,7 +684,6 @@ class Lotus(BaseModel):
 class StableCascade_C(BaseModel):
     def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=StageC)
-        self.diffusion_model.eval().requires_grad_(False)
 
     def extra_conds(self, **kwargs):
         out = {}
@@ -694,7 +712,6 @@ class StableCascade_C(BaseModel):
 class StableCascade_B(BaseModel):
     def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=StageB)
-        self.diffusion_model.eval().requires_grad_(False)
 
     def extra_conds(self, **kwargs):
         out = {}
@@ -1210,6 +1227,63 @@ class WAN21_Camera(WAN21):
             out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
         return out
 
+class WAN21_HuMo(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.HumoWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        noise = kwargs.get("noise", None)
+
+        audio_embed = kwargs.get("audio_embed", None)
+        if audio_embed is not None:
+            out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)
+
+        if "c_concat" not in out:  # 1.7B model
+            reference_latents = kwargs.get("reference_latents", None)
+            if reference_latents is not None:
+                out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))
+        else:
+            noise_shape = list(noise.shape)
+            noise_shape[1] += 4
+            concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
+            zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1)
+            zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1)
+            zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1)
+            concat_latent[:, 4:] = zero_vae_values
+            concat_latent[:, 4:, :1] = zero_vae_values_first
+            concat_latent[:, 4:, 1:2] = zero_vae_values_second
+            out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent)
+            reference_latents = kwargs.get("reference_latents", None)
+            if reference_latents is not None:
+                ref_latent = self.process_latent_in(reference_latents[-1])
+                ref_latent_shape = list(ref_latent.shape)
+                ref_latent_shape[1] += 4 + ref_latent_shape[1]
+                ref_latent_full = torch.zeros(ref_latent_shape, device=ref_latent.device, dtype=ref_latent.dtype)
+                ref_latent_full[:, 20:] = ref_latent
+                ref_latent_full[:, 16:20] = 1.0
+                out['reference_latent'] = comfy.conds.CONDRegular(ref_latent_full)
+
+        return out
+
+class WAN22_Animate(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        face_video_pixels = kwargs.get("face_video_pixels", None)
+        if face_video_pixels is not None:
+            out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels)
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))
+        return out
+
 class WAN22_S2V(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
@@ -1282,6 +1356,21 @@ class Hunyuan3Dv2(BaseModel):
             out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
+class Hunyuan3Dv2_1(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3dv2_1.hunyuandit.HunYuanDiTPlain)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        guidance = kwargs.get("guidance", 5.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+        return out
+
 class HiDream(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel)
@@ -1303,8 +1392,8 @@ class HiDream(BaseModel):
         return out
 
 class Chroma(Flux):
-    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma)
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma):
+        super().__init__(model_config, model_type, device=device, unet_model=unet_model)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -1314,6 +1403,10 @@ class Chroma(Flux):
             out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
+class ChromaRadiance(Chroma):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma_radiance.model.ChromaRadiance)
+
 class ACEStep(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel)
@@ -1391,3 +1484,146 @@ class QwenImage(BaseModel):
         if ref_latents is not None:
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
+
+class HunyuanImage21(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        conditioning_byt5small = kwargs.get("conditioning_byt5small", None)
+        if conditioning_byt5small is not None:
+            out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small)
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+
+        return out
+
+class HunyuanImage21Refiner(HunyuanImage21):
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        image = kwargs.get("concat_latent_image", None)
+        noise_augmentation = kwargs.get("noise_augmentation", 0.0)
+        device = kwargs["device"]
+
+        if image is None:
+            shape_image = list(noise.shape)
+            image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
+        else:
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            image = self.process_latent_in(image)
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+            if noise_augmentation > 0:
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(kwargs.get("seed", 0) - 10)
+                noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device)
+                image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image
+            else:
+                image = 0.75 * image
+        return image
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        out['disable_time_r'] = comfy.conds.CONDConstant(True)
+        return out
+
+class HunyuanVideo15(HunyuanVideo):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        extra_channels = self.diffusion_model.img_in.proj.weight.shape[1] - noise.shape[1] - 1 #noise 32 img cond 32 + mask 1
+        if extra_channels == 0:
+            return None
+
+        image = kwargs.get("concat_latent_image", None)
+        device = kwargs["device"]
+
+        if image is None:
+            shape_image = list(noise.shape)
+            shape_image[1] = extra_channels
+            image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
+        else:
+            latent_dim = self.latent_format.latent_channels
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            for i in range(0, image.shape[1], latent_dim):
+                image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim])
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.zeros_like(noise)[:, :1]
+        else:
+            mask = 1.0 - mask
+            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            if mask.shape[-3] < noise.shape[-3]:
+                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+        return torch.cat((image, mask), dim=1)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        conditioning_byt5small = kwargs.get("conditioning_byt5small", None)
+        if conditioning_byt5small is not None:
+            out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small)
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+
+        clip_vision_output = kwargs.get("clip_vision_output", None)
+        if clip_vision_output is not None:
+            out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.last_hidden_state)
+
+        return out
+
+class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        image = kwargs.get("concat_latent_image", None)
+        noise_augmentation = kwargs.get("noise_augmentation", 0.0)
+        device = kwargs["device"]
+
+        if image is None:
+            image = torch.zeros([noise.shape[0], noise.shape[1] * 2 + 2, noise.shape[-3], noise.shape[-2], noise.shape[-1]], device=comfy.model_management.intermediate_device())
+        else:
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            #image = self.process_latent_in(image) # scaling wasn't applied in reference code
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+            lq_image_slice = slice(noise.shape[1] + 1, 2 * noise.shape[1] + 1)
+            if noise_augmentation > 0:
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(kwargs.get("seed", 0) - 10)
+                noise = torch.randn(image[:, lq_image_slice].shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device)
+                image[:, lq_image_slice] = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image[:, lq_image_slice]
+            else:
+                image[:, lq_image_slice] = 0.75 * image[:, lq_image_slice]
+        return image
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        out['disable_time_r'] = comfy.conds.CONDConstant(False)
+        return out

comfy/model_detection.py

@@ -6,6 +6,20 @@ import math
 import logging
 import torch
 
+
+def detect_layer_quantization(metadata):
+    quant_key = "_quantization_metadata"
+    if metadata is not None and quant_key in metadata:
+        quant_metadata = metadata.pop(quant_key)
+        quant_metadata = json.loads(quant_metadata)
+        if isinstance(quant_metadata, dict) and "layers" in quant_metadata:
+            logging.info(f"Found quantization metadata (version {quant_metadata.get('format_version', 'unknown')})")
+            return quant_metadata["layers"]
+        else:
+            raise ValueError("Invalid quantization metadata format")
+    return None
+
+
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
     while True:
@@ -136,25 +150,55 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video
         dit_config = {}
+        in_w = state_dict['{}img_in.proj.weight'.format(key_prefix)]
+        out_w = state_dict['{}final_layer.linear.weight'.format(key_prefix)]
         dit_config["image_model"] = "hunyuan_video"
-        dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels
-        dit_config["patch_size"] = [1, 2, 2]
-        dit_config["out_channels"] = 16
-        dit_config["vec_in_dim"] = 768
-        dit_config["context_in_dim"] = 4096
-        dit_config["hidden_size"] = 3072
+        dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels
+        dit_config["patch_size"] = list(in_w.shape[2:])
+        dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"])
+        if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):
+            dit_config["vec_in_dim"] = 768
+        else:
+            dit_config["vec_in_dim"] = None
+
+        if len(dit_config["patch_size"]) == 2:
+            dit_config["axes_dim"] = [64, 64]
+        else:
+            dit_config["axes_dim"] = [16, 56, 56]
+
+        if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):
+            dit_config["meanflow"] = True
+        else:
+            dit_config["meanflow"] = False
+
+        dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]
+        dit_config["hidden_size"] = in_w.shape[0]
         dit_config["mlp_ratio"] = 4.0
-        dit_config["num_heads"] = 24
+        dit_config["num_heads"] = in_w.shape[0] // 128
         dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
         dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
-        dit_config["axes_dim"] = [16, 56, 56]
         dit_config["theta"] = 256
         dit_config["qkv_bias"] = True
+        if '{}byt5_in.fc1.weight'.format(key_prefix) in state_dict:
+            dit_config["byt5"] = True
+        else:
+            dit_config["byt5"] = False
+
         guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys))
         dit_config["guidance_embed"] = len(guidance_keys) > 0
+
+        # HunyuanVideo 1.5
+        if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["use_cond_type_embedding"] = True
+        else:
+            dit_config["use_cond_type_embedding"] = False
+        if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
+        else:
+            dit_config["vision_in_dim"] = None
         return dit_config
 
-    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
         dit_config = {}
         dit_config["image_model"] = "flux"
         dit_config["in_channels"] = 16
@@ -184,6 +228,18 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["out_dim"] = 3072
             dit_config["hidden_dim"] = 5120
             dit_config["n_layers"] = 5
+            if f"{key_prefix}nerf_blocks.0.norm.scale" in state_dict_keys: #Chroma Radiance
+                dit_config["image_model"] = "chroma_radiance"
+                dit_config["in_channels"] = 3
+                dit_config["out_channels"] = 3
+                dit_config["patch_size"] = 16
+                dit_config["nerf_hidden_size"] = 64
+                dit_config["nerf_mlp_ratio"] = 4
+                dit_config["nerf_depth"] = 4
+                dit_config["nerf_max_freqs"] = 8
+                dit_config["nerf_tile_size"] = 512
+                dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
+                dit_config["nerf_embedder_dtype"] = torch.float32
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
@@ -333,8 +389,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["patch_size"] = 2
         dit_config["in_channels"] = 16
         dit_config["dim"] = 2304
-        dit_config["cap_feat_dim"] = 2304
-        dit_config["n_layers"] = 26
+        dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1]
+        dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
         dit_config["n_heads"] = 24
         dit_config["n_kv_heads"] = 8
         dit_config["qk_norm"] = True
@@ -370,6 +426,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["model_type"] = "camera_2.2"
         elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys:
             dit_config["model_type"] = "s2v"
+        elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "humo"
+        elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "animate"
         else:
             if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                 dit_config["model_type"] = "i2v"
@@ -400,6 +460,20 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
 
+    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys:  # Hunyuan 3D 2.1
+
+        dit_config = {}
+        dit_config["image_model"] = "hunyuan3d2_1"
+        dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
+        dit_config["context_dim"] = 1024
+        dit_config["hidden_size"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[0]
+        dit_config["mlp_ratio"] = 4.0
+        dit_config["num_heads"] = 16
+        dit_config["depth"] = count_blocks(state_dict_keys, f"{key_prefix}blocks.{{}}")
+        dit_config["qkv_bias"] = False
+        dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys
+        return dit_config
+
     if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys:  # HiDream
         dit_config = {}
         dit_config["image_model"] = "hidream"
@@ -651,6 +725,12 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         else:
             model_config.optimizations["fp8"] = True
 
+    # Detect per-layer quantization (mixed precision)
+    layer_quant_config = detect_layer_quantization(metadata)
+    if layer_quant_config:
+        model_config.layer_quant_config = layer_quant_config
+        logging.info(f"Detected mixed precision quantization: {len(layer_quant_config)} layers quantized")
+
     return model_config
 
 def unet_prefix_from_state_dict(state_dict):

comfy/model_management.py

@@ -22,6 +22,7 @@ from enum import Enum
 from comfy.cli_args import args, PerformanceFeature
 import torch
 import sys
+import importlib
 import platform
 import weakref
 import gc
@@ -88,6 +89,7 @@ if args.deterministic:
 
 directml_enabled = False
 if args.directml is not None:
+    logging.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.")
     import torch_directml
     directml_enabled = True
     device_index = args.directml
@@ -289,6 +291,24 @@ def is_amd():
             return True
     return False
 
+def amd_min_version(device=None, min_rdna_version=0):
+    if not is_amd():
+        return False
+
+    if is_device_cpu(device):
+        return False
+
+    arch = torch.cuda.get_device_properties(device).gcnArchName
+    if arch.startswith('gfx') and len(arch) == 7:
+        try:
+            cmp_rdna_version = int(arch[4]) + 2
+        except:
+            cmp_rdna_version = 0
+        if cmp_rdna_version >= min_rdna_version:
+            return True
+
+    return False
+
 MIN_WEIGHT_MEMORY_RATIO = 0.4
 if is_nvidia():
     MIN_WEIGHT_MEMORY_RATIO = 0.0
@@ -311,24 +331,33 @@ except:
 
 
 SUPPORT_FP8_OPS = args.supports_fp8_compute
+
+AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]
+
 try:
     if is_amd():
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+        if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
+            torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
+            logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.")
+
         try:
             rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
         except:
             rocm_version = (6, -1)
-        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+
         logging.info("AMD arch: {}".format(arch))
         logging.info("ROCm version: {}".format(rocm_version))
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
-            if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
-                    ENABLE_PYTORCH_ATTENTION = True
-#            if torch_version_numeric >= (2, 8):
-#                if any((a in arch) for a in ["gfx1201"]):
-#                    ENABLE_PYTORCH_ATTENTION = True
+            if importlib.util.find_spec('triton') is not None:  # AMD efficient attention implementation depends on triton. TODO: better way of detecting if it's compiled in or not.
+                if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                        ENABLE_PYTORCH_ATTENTION = True
+                if rocm_version >= (7, 0):
+                   if any((a in arch) for a in ["gfx1201"]):
+                       ENABLE_PYTORCH_ATTENTION = True
         if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
-            if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
+            if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx950"]):  # TODO: more arches, "gfx942" gives error on pytorch nightly 2.10 1013 rocm7.0
                 SUPPORT_FP8_OPS = True
 
 except:
@@ -350,6 +379,9 @@ try:
 except:
     pass
 
+if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
+    torch.backends.cudnn.benchmark = True
+
 try:
     if torch_version_numeric >= (2, 5):
         torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
@@ -472,6 +504,7 @@ class LoadedModel:
         if use_more_vram == 0:
             use_more_vram = 1e32
         self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights)
+
         real_model = self.model.model
 
         if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:
@@ -625,7 +658,9 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             if loaded_model.model.is_clone(current_loaded_models[i].model):
                 to_unload = [i] + to_unload
         for i in to_unload:
-            current_loaded_models.pop(i).model.detach(unpatch_all=False)
+            model_to_unload = current_loaded_models.pop(i)
+            model_to_unload.model.detach(unpatch_all=False)
+            model_to_unload.model_finalizer.detach()
 
     total_memory_required = {}
     for loaded_model in models_to_load:
@@ -655,7 +690,10 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
             lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
-            lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
+            lowvram_model_memory = lowvram_model_memory - loaded_memory
+
+            if lowvram_model_memory == 0:
+                lowvram_model_memory = 0.1
 
         if vram_set_state == VRAMState.NO_VRAM:
             lowvram_model_memory = 0.1
@@ -903,9 +941,7 @@ def vae_dtype(device=None, allowed_dtypes=[]):
         if d == torch.float16 and should_use_fp16(device):
             return d
 
-        # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
-        # slowness still a problem on pytorch nightly 2.9.0.dev20250720+rocm6.4 tested on RDNA3
-        if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device):
+        if d == torch.bfloat16 and should_use_bf16(device):
             return d
 
     return torch.float32
@@ -967,12 +1003,6 @@ def device_supports_non_blocking(device):
         return False
     return True
 
-def device_should_use_non_blocking(device):
-    if not device_supports_non_blocking(device):
-        return False
-    return False
-    # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
-
 def force_channels_last():
     if args.force_channels_last:
         return True
@@ -987,6 +1017,16 @@ if args.async_offload:
     NUM_STREAMS = 2
     logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS))
 
+def current_stream(device):
+    if device is None:
+        return None
+    if is_device_cuda(device):
+        return torch.cuda.current_stream()
+    elif is_device_xpu(device):
+        return torch.xpu.current_stream()
+    else:
+        return None
+
 stream_counters = {}
 def get_offload_stream(device):
     stream_counter = stream_counters.get(device, 0)
@@ -995,21 +1035,17 @@ def get_offload_stream(device):
 
     if device in STREAMS:
         ss = STREAMS[device]
-        s = ss[stream_counter]
+        #Sync the oldest stream in the queue with the current
+        ss[stream_counter].wait_stream(current_stream(device))
         stream_counter = (stream_counter + 1) % len(ss)
-        if is_device_cuda(device):
-            ss[stream_counter].wait_stream(torch.cuda.current_stream())
-        elif is_device_xpu(device):
-            ss[stream_counter].wait_stream(torch.xpu.current_stream())
         stream_counters[device] = stream_counter
-        return s
+        return ss[stream_counter]
     elif is_device_cuda(device):
         ss = []
         for k in range(NUM_STREAMS):
             ss.append(torch.cuda.Stream(device=device, priority=0))
         STREAMS[device] = ss
         s = ss[stream_counter]
-        stream_counter = (stream_counter + 1) % len(ss)
         stream_counters[device] = stream_counter
         return s
     elif is_device_xpu(device):
@@ -1018,18 +1054,14 @@ def get_offload_stream(device):
             ss.append(torch.xpu.Stream(device=device, priority=0))
         STREAMS[device] = ss
         s = ss[stream_counter]
-        stream_counter = (stream_counter + 1) % len(ss)
         stream_counters[device] = stream_counter
         return s
     return None
 
 def sync_stream(device, stream):
-    if stream is None:
+    if stream is None or current_stream(device) is None:
         return
-    if is_device_cuda(device):
-        torch.cuda.current_stream().wait_stream(stream)
-    elif is_device_xpu(device):
-        torch.xpu.current_stream().wait_stream(stream)
+    current_stream(device).wait_stream(stream)
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
     if device is None or weight.device == device:
@@ -1054,6 +1086,79 @@ def cast_to_device(tensor, device, dtype, copy=False):
     non_blocking = device_supports_non_blocking(device)
     return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
 
+
+PINNED_MEMORY = {}
+TOTAL_PINNED_MEMORY = 0
+MAX_PINNED_MEMORY = -1
+if not args.disable_pinned_memory:
+    if is_nvidia() or is_amd():
+        if WINDOWS:
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
+        else:
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
+        logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
+
+
+def pin_memory(tensor):
+    global TOTAL_PINNED_MEMORY
+    if MAX_PINNED_MEMORY <= 0:
+        return False
+
+    if type(tensor) is not torch.nn.parameter.Parameter:
+        return False
+
+    if not is_device_cpu(tensor.device):
+        return False
+
+    if tensor.is_pinned():
+        #NOTE: Cuda does detect when a tensor is already pinned and would
+        #error below, but there are proven cases where this also queues an error
+        #on the GPU async. So dont trust the CUDA API and guard here
+        return False
+
+    if not tensor.is_contiguous():
+        return False
+
+    size = tensor.numel() * tensor.element_size()
+    if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
+        return False
+
+    ptr = tensor.data_ptr()
+    if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:
+        PINNED_MEMORY[ptr] = size
+        TOTAL_PINNED_MEMORY += size
+        return True
+
+    return False
+
+def unpin_memory(tensor):
+    global TOTAL_PINNED_MEMORY
+    if MAX_PINNED_MEMORY <= 0:
+        return False
+
+    if not is_device_cpu(tensor.device):
+        return False
+
+    ptr = tensor.data_ptr()
+    size = tensor.numel() * tensor.element_size()
+
+    size_stored = PINNED_MEMORY.get(ptr, None)
+    if size_stored is None:
+        logging.warning("Tried to unpin tensor not pinned by ComfyUI")
+        return False
+
+    if size != size_stored:
+        logging.warning("Size of pinned tensor changed")
+        return False
+
+    if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
+        TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
+        if len(PINNED_MEMORY) == 0:
+            TOTAL_PINNED_MEMORY = 0
+        return True
+
+    return False
+
 def sage_attention_enabled():
     return args.use_sage_attention
 
@@ -1306,7 +1411,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
 
     if is_amd():
         arch = torch.cuda.get_device_properties(device).gcnArchName
-        if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]):  # RDNA2 and older don't support bf16
+        if any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH):  # RDNA2 and older don't support bf16
             if manual_cast:
                 return True
             return False

comfy/model_patcher.py

@@ -123,16 +123,30 @@ def move_weight_functions(m, device):
     return memory
 
 class LowVramPatch:
-    def __init__(self, key, patches):
+    def __init__(self, key, patches, convert_func=None, set_func=None):
         self.key = key
         self.patches = patches
+        self.convert_func = convert_func
+        self.set_func = set_func
+
     def __call__(self, weight):
         intermediate_dtype = weight.dtype
+        if self.convert_func is not None:
+            weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True)
+
         if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
             intermediate_dtype = torch.float32
-            return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+            out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
+            if self.set_func is None:
+                return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
+            else:
+                return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
 
-        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+        out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+        if self.set_func is not None:
+            return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
+        else:
+            return out
 
 def get_key_weight(model, key):
     set_func = None
@@ -224,6 +238,7 @@ class ModelPatcher:
         self.force_cast_weights = False
         self.patches_uuid = uuid.uuid4()
         self.parent = None
+        self.pinned = set()
 
         self.attachments: dict[str] = {}
         self.additional_models: dict[str, list[ModelPatcher]] = {}
@@ -261,6 +276,9 @@ class ModelPatcher:
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
+    def get_ram_usage(self):
+        return self.model_size()
+
     def loaded_size(self):
         return self.model.model_loaded_weight_memory
 
@@ -280,6 +298,7 @@ class ModelPatcher:
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         n.parent = self
+        n.pinned = self.pinned
 
         n.force_cast_weights = self.force_cast_weights
 
@@ -433,6 +452,22 @@ class ModelPatcher:
     def set_model_double_block_patch(self, patch):
         self.set_model_patch(patch, "double_block")
 
+    def set_model_post_input_patch(self, patch):
+        self.set_model_patch(patch, "post_input")
+
+    def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
+        rope_options = self.model_options["transformer_options"].get("rope_options", {})
+        rope_options["scale_x"] = scale_x
+        rope_options["scale_y"] = scale_y
+        rope_options["scale_t"] = scale_t
+
+        rope_options["shift_x"] = shift_x
+        rope_options["shift_y"] = shift_y
+        rope_options["shift_t"] = shift_t
+
+        self.model_options["transformer_options"]["rope_options"] = rope_options
+
+
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
@@ -601,6 +636,21 @@ class ModelPatcher:
         else:
             set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
+    def pin_weight_to_device(self, key):
+        weight, set_func, convert_func = get_key_weight(self.model, key)
+        if comfy.model_management.pin_memory(weight):
+            self.pinned.add(key)
+
+    def unpin_weight(self, key):
+        if key in self.pinned:
+            weight, set_func, convert_func = get_key_weight(self.model, key)
+            comfy.model_management.unpin_memory(weight)
+            self.pinned.remove(key)
+
+    def unpin_all_weights(self):
+        for key in list(self.pinned):
+            self.unpin_weight(key)
+
     def _load_list(self):
         loading = []
         for n, m in self.model.named_modules():
@@ -622,9 +672,11 @@ class ModelPatcher:
             mem_counter = 0
             patch_counter = 0
             lowvram_counter = 0
+            lowvram_mem_counter = 0
             loading = self._load_list()
 
             load_completely = []
+            offloaded = []
             loading.sort(reverse=True)
             for x in loading:
                 n = x[1]
@@ -641,6 +693,7 @@ class ModelPatcher:
                     if mem_counter + module_mem >= lowvram_model_memory:
                         lowvram_weight = True
                         lowvram_counter += 1
+                        lowvram_mem_counter += module_mem
                         if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
                             continue
 
@@ -654,16 +707,19 @@ class ModelPatcher:
                         if force_patch_weights:
                             self.patch_weight_to_device(weight_key)
                         else:
-                            m.weight_function = [LowVramPatch(weight_key, self.patches)]
+                            _, set_func, convert_func = get_key_weight(self.model, weight_key)
+                            m.weight_function = [LowVramPatch(weight_key, self.patches, convert_func, set_func)]
                             patch_counter += 1
                     if bias_key in self.patches:
                         if force_patch_weights:
                             self.patch_weight_to_device(bias_key)
                         else:
-                            m.bias_function = [LowVramPatch(bias_key, self.patches)]
+                            _, set_func, convert_func = get_key_weight(self.model, bias_key)
+                            m.bias_function = [LowVramPatch(bias_key, self.patches, convert_func, set_func)]
                             patch_counter += 1
 
                     cast_weight = True
+                    offloaded.append((module_mem, n, m, params))
                 else:
                     if hasattr(m, "comfy_cast_weights"):
                         wipe_lowvram_weight(m)
@@ -694,7 +750,9 @@ class ModelPatcher:
                         continue
 
                 for param in params:
-                    self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
+                    key = "{}.{}".format(n, param)
+                    self.unpin_weight(key)
+                    self.patch_weight_to_device(key, device_to=device_to)
 
                 logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
                 m.comfy_patched_weights = True
@@ -702,11 +760,17 @@ class ModelPatcher:
             for x in load_completely:
                 x[2].to(device_to)
 
+            for x in offloaded:
+                n = x[1]
+                params = x[3]
+                for param in params:
+                    self.pin_weight_to_device("{}.{}".format(n, param))
+
             if lowvram_counter > 0:
-                logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
             else:
-                logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+                logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
                 self.model.model_lowvram = False
                 if full_load:
                     self.model.to(device_to)
@@ -743,6 +807,7 @@ class ModelPatcher:
         self.eject_model()
         if unpatch_weights:
             self.unpatch_hooks()
+            self.unpin_all_weights()
             if self.model.model_lowvram:
                 for m in self.model.modules():
                     move_weight_functions(m, device_to)
@@ -778,7 +843,7 @@ class ModelPatcher:
 
         self.object_patches_backup.clear()
 
-    def partially_unload(self, device_to, memory_to_free=0):
+    def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False):
         with self.use_ejected():
             hooks_unpatched = False
             memory_freed = 0
@@ -822,11 +887,19 @@ class ModelPatcher:
                         module_mem += move_weight_functions(m, device_to)
                         if lowvram_possible:
                             if weight_key in self.patches:
-                                m.weight_function.append(LowVramPatch(weight_key, self.patches))
-                                patch_counter += 1
+                                if force_patch_weights:
+                                    self.patch_weight_to_device(weight_key)
+                                else:
+                                    _, set_func, convert_func = get_key_weight(self.model, weight_key)
+                                    m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func))
+                                    patch_counter += 1
                             if bias_key in self.patches:
-                                m.bias_function.append(LowVramPatch(bias_key, self.patches))
-                                patch_counter += 1
+                                if force_patch_weights:
+                                    self.patch_weight_to_device(bias_key)
+                                else:
+                                    _, set_func, convert_func = get_key_weight(self.model, bias_key)
+                                    m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func))
+                                    patch_counter += 1
                             cast_weight = True
 
                         if cast_weight:
@@ -836,9 +909,13 @@ class ModelPatcher:
                         memory_freed += module_mem
                         logging.debug("freed {}".format(n))
 
+                        for param in params:
+                            self.pin_weight_to_device("{}.{}".format(n, param))
+
             self.model.model_lowvram = True
             self.model.lowvram_patch_counter += patch_counter
             self.model.model_loaded_weight_memory -= memory_freed
+            logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter))
             return memory_freed
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
@@ -851,6 +928,9 @@ class ModelPatcher:
                 extra_memory += (used - self.model.model_loaded_weight_memory)
 
             self.patch_model(load_weights=False)
+            if extra_memory < 0 and not unpatch_weights:
+                self.partially_unload(self.offload_device, -extra_memory, force_patch_weights=force_patch_weights)
+                return 0
             full_load = False
             if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0:
                 self.apply_hooks(self.forced_hooks, force_apply=True)
@@ -1238,5 +1318,6 @@ class ModelPatcher:
         self.clear_cached_hook_weights()
 
     def __del__(self):
+        self.unpin_all_weights()
         self.detach(unpatch_all=False)
 

comfy/model_sampling.py

@@ -21,17 +21,23 @@ def rescale_zero_terminal_snr_sigmas(sigmas):
     alphas_bar[-1] = 4.8973451890853435e-08
     return ((1 - alphas_bar) / alphas_bar) ** 0.5
 
+def reshape_sigma(sigma, noise_dim):
+    if sigma.nelement() == 1:
+        return sigma.view(())
+    else:
+        return sigma.view(sigma.shape[:1] + (1,) * (noise_dim - 1))
+
 class EPS:
     def calculate_input(self, sigma, noise):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         if max_denoise:
             noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
         else:
@@ -45,12 +51,12 @@ class EPS:
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
 class EDM(V_PREDICTION):
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
 class CONST:
@@ -58,15 +64,15 @@ class CONST:
         return noise
 
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return sigma * noise + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
+        sigma = reshape_sigma(sigma, latent.ndim)
         return latent / (1.0 - sigma)
 
 class X0(EPS):
@@ -80,16 +86,16 @@ class IMG_TO_IMG(X0):
 class COSMOS_RFLOW:
     def calculate_input(self, sigma, noise):
         sigma = (sigma / (sigma + 1))
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return noise * (1.0 - sigma)
 
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = (sigma / (sigma + 1))
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * (1.0 - sigma) - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         noise = noise * sigma
         noise += latent_image
         return noise

comfy/nested_tensor.py

@@ -0,0 +1,91 @@
+import torch
+
+class NestedTensor:
+    def __init__(self, tensors):
+        self.tensors = list(tensors)
+        self.is_nested = True
+
+    def _copy(self):
+        return NestedTensor(self.tensors)
+
+    def apply_operation(self, other, operation):
+        o = self._copy()
+        if isinstance(other, NestedTensor):
+            for i, t in enumerate(o.tensors):
+                o.tensors[i] = operation(t, other.tensors[i])
+        else:
+            for i, t in enumerate(o.tensors):
+                o.tensors[i] = operation(t, other)
+        return o
+
+    def __add__(self, b):
+        return self.apply_operation(b, lambda x, y: x + y)
+
+    def __sub__(self, b):
+        return self.apply_operation(b, lambda x, y: x - y)
+
+    def __mul__(self, b):
+        return self.apply_operation(b, lambda x, y: x * y)
+
+    # def __itruediv__(self, b):
+    #     return self.apply_operation(b, lambda x, y: x / y)
+
+    def __truediv__(self, b):
+        return self.apply_operation(b, lambda x, y: x / y)
+
+    def __getitem__(self, *args, **kwargs):
+        return self.apply_operation(None, lambda x, y: x.__getitem__(*args, **kwargs))
+
+    def unbind(self):
+        return self.tensors
+
+    def to(self, *args, **kwargs):
+        o = self._copy()
+        for i, t in enumerate(o.tensors):
+            o.tensors[i] = t.to(*args, **kwargs)
+        return o
+
+    def new_ones(self, *args, **kwargs):
+        return self.tensors[0].new_ones(*args, **kwargs)
+
+    def float(self):
+        return self.to(dtype=torch.float)
+
+    def chunk(self, *args, **kwargs):
+        return self.apply_operation(None, lambda x, y: x.chunk(*args, **kwargs))
+
+    def size(self):
+        return self.tensors[0].size()
+
+    @property
+    def shape(self):
+        return self.tensors[0].shape
+
+    @property
+    def ndim(self):
+        dims = 0
+        for t in self.tensors:
+            dims = max(t.ndim, dims)
+        return dims
+
+    @property
+    def device(self):
+        return self.tensors[0].device
+
+    @property
+    def dtype(self):
+        return self.tensors[0].dtype
+
+    @property
+    def layout(self):
+        return self.tensors[0].layout
+
+
+def cat_nested(tensors, *args, **kwargs):
+    cated_tensors = []
+    for i in range(len(tensors[0].tensors)):
+        tens = []
+        for j in range(len(tensors)):
+            tens.append(tensors[j].tensors[i])
+        cated_tensors.append(torch.cat(tens, *args, **kwargs))
+    return NestedTensor(cated_tensors)

comfy/ops.py

@@ -24,13 +24,18 @@ import comfy.float
 import comfy.rmsnorm
 import contextlib
 
+def run_every_op():
+    if torch.compiler.is_compiling():
+        return
+
+    comfy.model_management.throw_exception_if_processing_interrupted()
 
 def scaled_dot_product_attention(q, k, v, *args, **kwargs):
     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
 
 
 try:
-    if torch.cuda.is_available():
+    if torch.cuda.is_available() and comfy.model_management.WINDOWS:
         from torch.nn.attention import SDPBackend, sdpa_kernel
         import inspect
         if "set_priority" in inspect.signature(sdpa_kernel).parameters:
@@ -50,46 +55,90 @@ try:
 except (ModuleNotFoundError, TypeError):
     logging.warning("Could not set sdpa backend priority.")
 
+NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
+try:
+    if comfy.model_management.is_nvidia():
+        cudnn_version = torch.backends.cudnn.version()
+        if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
+            #TODO: change upper bound version once it's fixed'
+            NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
+            logging.info("working around nvidia conv3d memory bug.")
+except:
+    pass
+
 cast_to = comfy.model_management.cast_to #TODO: remove once no more references
 
 def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
-def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False):
+    # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
+    # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This
+    # will add async-offload support to your cast and improve performance.
     if input is not None:
         if dtype is None:
-            dtype = input.dtype
+            if isinstance(input, QuantizedTensor):
+                dtype = input._layout_params["orig_dtype"]
+            else:
+                dtype = input.dtype
         if bias_dtype is None:
             bias_dtype = dtype
         if device is None:
             device = input.device
 
-    offload_stream = comfy.model_management.get_offload_stream(device)
+    if offloadable and (device != s.weight.device or
+                        (s.bias is not None and device != s.bias.device)):
+        offload_stream = comfy.model_management.get_offload_stream(device)
+    else:
+        offload_stream = None
+
     if offload_stream is not None:
         wf_context = offload_stream
     else:
         wf_context = contextlib.nullcontext()
 
-    bias = None
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
-    if s.bias is not None:
-        has_function = len(s.bias_function) > 0
-        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
 
-        if has_function:
+    weight_has_function = len(s.weight_function) > 0
+    bias_has_function = len(s.bias_function) > 0
+
+    weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream)
+
+    bias = None
+    if s.bias is not None:
+        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
+
+        if bias_has_function:
             with wf_context:
                 for f in s.bias_function:
                     bias = f(bias)
 
-    has_function = len(s.weight_function) > 0
-    weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
-    if has_function:
+    if weight_has_function or weight.dtype != dtype:
         with wf_context:
+            weight = weight.to(dtype=dtype)
             for f in s.weight_function:
                 weight = f(weight)
 
     comfy.model_management.sync_stream(device, offload_stream)
-    return weight, bias
+    if offloadable:
+        return weight, bias, offload_stream
+    else:
+        #Legacy function signature
+        return weight, bias
+
+
+def uncast_bias_weight(s, weight, bias, offload_stream):
+    if offload_stream is None:
+        return
+    if weight is not None:
+        device = weight.device
+    else:
+        if bias is None:
+            return
+        device = bias.device
+    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+
 
 class CastWeightBiasOp:
     comfy_cast_weights = False
@@ -102,10 +151,13 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.linear(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -116,10 +168,13 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -130,10 +185,13 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -143,11 +201,23 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
+        def _conv_forward(self, input, weight, bias, *args, **kwargs):
+            if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
+                out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
+                if bias is not None:
+                    out += bias.reshape((1, -1) + (1,) * (out.ndim - 2))
+                return out
+            else:
+                return super()._conv_forward(input, weight, bias, *args, **kwargs)
+
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._conv_forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -158,10 +228,13 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -173,13 +246,17 @@ class disable_weight_init:
 
         def forward_comfy_cast_weights(self, input):
             if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
             else:
                 weight = None
                 bias = None
-            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+                offload_stream = None
+            x = torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -192,13 +269,18 @@ class disable_weight_init:
 
         def forward_comfy_cast_weights(self, input):
             if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
             else:
                 weight = None
-            return comfy.rmsnorm.rms_norm(input, weight, self.eps)  # TODO: switch to commented out line when old torch is deprecated
-            # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
+                bias = None
+                offload_stream = None
+            x = comfy.rmsnorm.rms_norm(input, weight, self.eps)  # TODO: switch to commented out line when old torch is deprecated
+            # x = torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -214,12 +296,15 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose2d(
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.conv_transpose2d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -235,12 +320,15 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose1d(
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.conv_transpose1d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -255,10 +343,14 @@ class disable_weight_init:
             output_dtype = out_dtype
             if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
                 out_dtype = None
-            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
-            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+            weight, bias, offload_stream = cast_bias_weight(self, device=input.device, dtype=out_dtype, offloadable=True)
+            x = torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
+
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -309,20 +401,18 @@ class manual_cast(disable_weight_init):
 
 
 def fp8_linear(self, input):
+    """
+    Legacy FP8 linear function for backward compatibility.
+    Uses QuantizedTensor subclass for dispatch.
+    """
     dtype = self.weight.dtype
     if dtype not in [torch.float8_e4m3fn]:
         return None
 
-    tensor_2d = False
-    if len(input.shape) == 2:
-        tensor_2d = True
-        input = input.unsqueeze(1)
-
-    input_shape = input.shape
     input_dtype = input.dtype
-    if len(input.shape) == 3:
-        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
-        w = w.t()
+
+    if input.ndim == 3 or input.ndim == 2:
+        w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
 
         scale_weight = self.scale_weight
         scale_input = self.scale_input
@@ -334,23 +424,20 @@ def fp8_linear(self, input):
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
             input = torch.clamp(input, min=-448, max=448, out=input)
-            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
+            layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
+            quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
         else:
             scale_input = scale_input.to(input.device)
-            input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous()
+            quantized_input = QuantizedTensor.from_float(input, "TensorCoreFP8Layout", scale=scale_input, dtype=dtype)
 
-        if bias is not None:
-            o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
-        else:
-            o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight)
+        # Wrap weight in QuantizedTensor - this enables unified dispatch
+        # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
+        layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
+        quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight)
+        o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
-        if isinstance(o, tuple):
-            o = o[0]
-
-        if tensor_2d:
-            return o.reshape(input_shape[0], -1)
-
-        return o.reshape((-1, input_shape[1], self.weight.shape[0]))
+        uncast_bias_weight(self, w, bias, offload_stream)
+        return o
 
     return None
 
@@ -362,15 +449,18 @@ class fp8_ops(manual_cast):
             return None
 
         def forward_comfy_cast_weights(self, input):
-            try:
-                out = fp8_linear(self, input)
-                if out is not None:
-                    return out
-            except Exception as e:
-                logging.info("Exception during fp8 op: {}".format(e))
+            if not self.training:
+                try:
+                    out = fp8_linear(self, input)
+                    if out is not None:
+                        return out
+                except Exception as e:
+                    logging.info("Exception during fp8 op: {}".format(e))
 
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = torch.nn.functional.linear(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
 def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
     logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
@@ -398,12 +488,14 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
                     if out is not None:
                         return out
 
-                weight, bias = cast_bias_weight(self, input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
 
                 if weight.numel() < input.numel(): #TODO: optimize
-                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+                    x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
                 else:
-                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+                    x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+                uncast_bias_weight(self, weight, bias, offload_stream)
+                return x
 
             def convert_weight(self, weight, inplace=False, **kwargs):
                 if inplace:
@@ -412,8 +504,10 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
                 else:
                     return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
 
-            def set_weight(self, weight, inplace_update=False, seed=None, **kwargs):
+            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
                 weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+                if return_weight:
+                    return weight
                 if inplace_update:
                     self.weight.data.copy_(weight)
                 else:
@@ -440,7 +534,120 @@ if CUBLAS_IS_AVAILABLE:
             def forward(self, *args, **kwargs):
                 return super().forward(*args, **kwargs)
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+
+# ==============================================================================
+# Mixed Precision Operations
+# ==============================================================================
+from .quant_ops import QuantizedTensor, QUANT_ALGOS
+
+class MixedPrecisionOps(disable_weight_init):
+    _layer_quant_config = {}
+    _compute_dtype = torch.bfloat16
+
+    class Linear(torch.nn.Module, CastWeightBiasOp):
+        def __init__(
+            self,
+            in_features: int,
+            out_features: int,
+            bias: bool = True,
+            device=None,
+            dtype=None,
+        ) -> None:
+            super().__init__()
+
+            self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
+            # self.factory_kwargs = {"device": device, "dtype": dtype}
+
+            self.in_features = in_features
+            self.out_features = out_features
+            if bias:
+                self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
+            else:
+                self.register_parameter("bias", None)
+
+            self.tensor_class = None
+
+        def reset_parameters(self):
+            return None
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
+                                  strict, missing_keys, unexpected_keys, error_msgs):
+
+            device = self.factory_kwargs["device"]
+            layer_name = prefix.rstrip('.')
+            weight_key = f"{prefix}weight"
+            weight = state_dict.pop(weight_key, None)
+            if weight is None:
+                raise ValueError(f"Missing weight for layer {layer_name}")
+
+            manually_loaded_keys = [weight_key]
+
+            if layer_name not in MixedPrecisionOps._layer_quant_config:
+                self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
+            else:
+                quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None)
+                if quant_format is None:
+                    raise ValueError(f"Unknown quantization format for layer {layer_name}")
+
+                qconfig = QUANT_ALGOS[quant_format]
+                self.layout_type = qconfig["comfy_tensor_layout"]
+
+                weight_scale_key = f"{prefix}weight_scale"
+                layout_params = {
+                    'scale': state_dict.pop(weight_scale_key, None),
+                    'orig_dtype': MixedPrecisionOps._compute_dtype,
+                    'block_size': qconfig.get("group_size", None),
+                }
+                if layout_params['scale'] is not None:
+                    manually_loaded_keys.append(weight_scale_key)
+
+                self.weight = torch.nn.Parameter(
+                    QuantizedTensor(weight.to(device=device), self.layout_type, layout_params),
+                    requires_grad=False
+                )
+
+                for param_name in qconfig["parameters"]:
+                    param_key = f"{prefix}{param_name}"
+                    _v = state_dict.pop(param_key, None)
+                    if _v is None:
+                        continue
+                    setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+                    manually_loaded_keys.append(param_key)
+
+            super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+            for key in manually_loaded_keys:
+                if key in missing_keys:
+                    missing_keys.remove(key)
+
+        def _forward(self, input, weight, bias):
+            return torch.nn.functional.linear(input, weight, bias)
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            x = self._forward(input, weight, bias)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
+
+        def forward(self, input, *args, **kwargs):
+            run_every_op()
+
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                return self.forward_comfy_cast_weights(input, *args, **kwargs)
+            if (getattr(self, 'layout_type', None) is not None and
+                getattr(self, 'input_scale', None) is not None and
+                not isinstance(input, QuantizedTensor)):
+                input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype)
+            return self._forward(input, self.weight, self.bias)
+
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
+    if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
+        MixedPrecisionOps._layer_quant_config = model_config.layer_quant_config
+        MixedPrecisionOps._compute_dtype = compute_dtype
+        logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
+        return MixedPrecisionOps
+
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
     if scaled_fp8 is not None:
         return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)

comfy/patcher_extension.py

@@ -150,7 +150,7 @@ def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):
     for key, value in dict2.items():
         if isinstance(value, dict):
             curr_value = merged_dict.setdefault(key, {})
-            merged_dict[key] = merge_nested_dicts(value, curr_value)
+            merged_dict[key] = merge_nested_dicts(curr_value, value)
         elif isinstance(value, list):
             merged_dict.setdefault(key, []).extend(value)
         else:

comfy/pixel_space_convert.py

@@ -0,0 +1,16 @@
+import torch
+
+
+# "Fake" VAE that converts from IMAGE B, H, W, C and values on the scale of 0..1
+# to LATENT B, C, H, W and values on the scale of -1..1.
+class PixelspaceConversionVAE(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.pixel_space_vae = torch.nn.Parameter(torch.tensor(1.0))
+
+    def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:
+        return pixels
+
+    def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:
+        return samples
+

comfy/quant_ops.py

@@ -0,0 +1,545 @@
+import torch
+import logging
+from typing import Tuple, Dict
+
+_LAYOUT_REGISTRY = {}
+_GENERIC_UTILS = {}
+
+
+def register_layout_op(torch_op, layout_type):
+    """
+    Decorator to register a layout-specific operation handler.
+    Args:
+        torch_op: PyTorch operation (e.g., torch.ops.aten.linear.default)
+        layout_type: Layout class (e.g., TensorCoreFP8Layout)
+    Example:
+        @register_layout_op(torch.ops.aten.linear.default, TensorCoreFP8Layout)
+        def fp8_linear(func, args, kwargs):
+            # FP8-specific linear implementation
+            ...
+    """
+    def decorator(handler_func):
+        if torch_op not in _LAYOUT_REGISTRY:
+            _LAYOUT_REGISTRY[torch_op] = {}
+        _LAYOUT_REGISTRY[torch_op][layout_type] = handler_func
+        return handler_func
+    return decorator
+
+
+def register_generic_util(torch_op):
+    """
+    Decorator to register a generic utility that works for all layouts.
+    Args:
+        torch_op: PyTorch operation (e.g., torch.ops.aten.detach.default)
+
+    Example:
+        @register_generic_util(torch.ops.aten.detach.default)
+        def generic_detach(func, args, kwargs):
+            # Works for any layout
+            ...
+    """
+    def decorator(handler_func):
+        _GENERIC_UTILS[torch_op] = handler_func
+        return handler_func
+    return decorator
+
+
+def _get_layout_from_args(args):
+    for arg in args:
+        if isinstance(arg, QuantizedTensor):
+            return arg._layout_type
+        elif isinstance(arg, (list, tuple)):
+            for item in arg:
+                if isinstance(item, QuantizedTensor):
+                    return item._layout_type
+    return None
+
+
+def _move_layout_params_to_device(params, device):
+    new_params = {}
+    for k, v in params.items():
+        if isinstance(v, torch.Tensor):
+            new_params[k] = v.to(device=device)
+        else:
+            new_params[k] = v
+    return new_params
+
+
+def _copy_layout_params(params):
+    new_params = {}
+    for k, v in params.items():
+        if isinstance(v, torch.Tensor):
+            new_params[k] = v.clone()
+        else:
+            new_params[k] = v
+    return new_params
+
+def _copy_layout_params_inplace(src, dst, non_blocking=False):
+    for k, v in src.items():
+        if isinstance(v, torch.Tensor):
+            dst[k].copy_(v, non_blocking=non_blocking)
+        else:
+            dst[k] = v
+
+class QuantizedLayout:
+    """
+    Base class for quantization layouts.
+
+    A layout encapsulates the format-specific logic for quantization/dequantization
+    and provides a uniform interface for extracting raw tensors needed for computation.
+
+    New quantization formats should subclass this and implement the required methods.
+    """
+    @classmethod
+    def quantize(cls, tensor, **kwargs) -> Tuple[torch.Tensor, Dict]:
+        raise NotImplementedError(f"{cls.__name__} must implement quantize()")
+
+    @staticmethod
+    def dequantize(qdata, **layout_params) -> torch.Tensor:
+        raise NotImplementedError("TensorLayout must implement dequantize()")
+
+    @classmethod
+    def get_plain_tensors(cls, qtensor) -> torch.Tensor:
+        raise NotImplementedError(f"{cls.__name__} must implement get_plain_tensors()")
+
+
+class QuantizedTensor(torch.Tensor):
+    """
+    Universal quantized tensor that works with any layout.
+
+    This tensor subclass uses a pluggable layout system to support multiple
+    quantization formats (FP8, INT4, INT8, etc.) without code duplication.
+
+    The layout_type determines format-specific behavior, while common operations
+    (detach, clone, to) are handled generically.
+
+    Attributes:
+        _qdata: The quantized tensor data
+        _layout_type: Layout class (e.g., TensorCoreFP8Layout)
+        _layout_params: Dict with layout-specific params (scale, zero_point, etc.)
+    """
+
+    @staticmethod
+    def __new__(cls, qdata, layout_type, layout_params):
+        """
+        Create a quantized tensor.
+
+        Args:
+            qdata: The quantized data tensor
+            layout_type: Layout class (subclass of QuantizedLayout)
+            layout_params: Dict with layout-specific parameters
+        """
+        return torch.Tensor._make_wrapper_subclass(cls, qdata.shape, device=qdata.device, dtype=qdata.dtype, requires_grad=False)
+
+    def __init__(self, qdata, layout_type, layout_params):
+        self._qdata = qdata
+        self._layout_type = layout_type
+        self._layout_params = layout_params
+
+    def __repr__(self):
+        layout_name = self._layout_type
+        param_str = ", ".join(f"{k}={v}" for k, v in list(self._layout_params.items())[:2])
+        return f"QuantizedTensor(shape={self.shape}, layout={layout_name}, {param_str})"
+
+    @property
+    def layout_type(self):
+        return self._layout_type
+
+    def __tensor_flatten__(self):
+        """
+        Tensor flattening protocol for proper device movement.
+        """
+        inner_tensors = ["_qdata"]
+        ctx = {
+            "layout_type": self._layout_type,
+        }
+
+        tensor_params = {}
+        non_tensor_params = {}
+        for k, v in self._layout_params.items():
+            if isinstance(v, torch.Tensor):
+                tensor_params[k] = v
+            else:
+                non_tensor_params[k] = v
+
+        ctx["tensor_param_keys"] = list(tensor_params.keys())
+        ctx["non_tensor_params"] = non_tensor_params
+
+        for k, v in tensor_params.items():
+            attr_name = f"_layout_param_{k}"
+            object.__setattr__(self, attr_name, v)
+            inner_tensors.append(attr_name)
+
+        return inner_tensors, ctx
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride):
+        """
+        Tensor unflattening protocol for proper device movement.
+        Reconstructs the QuantizedTensor after device movement.
+        """
+        layout_type = ctx["layout_type"]
+        layout_params = dict(ctx["non_tensor_params"])
+
+        for key in ctx["tensor_param_keys"]:
+            attr_name = f"_layout_param_{key}"
+            layout_params[key] = inner_tensors[attr_name]
+
+        return QuantizedTensor(inner_tensors["_qdata"], layout_type, layout_params)
+
+    @classmethod
+    def from_float(cls, tensor, layout_type, **quantize_kwargs) -> 'QuantizedTensor':
+        qdata, layout_params = LAYOUTS[layout_type].quantize(tensor, **quantize_kwargs)
+        return cls(qdata, layout_type, layout_params)
+
+    def dequantize(self) -> torch.Tensor:
+        return LAYOUTS[self._layout_type].dequantize(self._qdata, **self._layout_params)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        # Step 1: Check generic utilities first (detach, clone, to, etc.)
+        if func in _GENERIC_UTILS:
+            return _GENERIC_UTILS[func](func, args, kwargs)
+
+        # Step 2: Check layout-specific handlers (linear, matmul, etc.)
+        layout_type = _get_layout_from_args(args)
+        if layout_type and func in _LAYOUT_REGISTRY:
+            handler = _LAYOUT_REGISTRY[func].get(layout_type)
+            if handler:
+                return handler(func, args, kwargs)
+
+        # Step 3: Fallback to dequantization
+        if isinstance(args[0] if args else None, QuantizedTensor):
+            logging.info(f"QuantizedTensor: Unhandled operation {func}, falling back to dequantization. kwargs={kwargs}")
+        return cls._dequant_and_fallback(func, args, kwargs)
+
+    @classmethod
+    def _dequant_and_fallback(cls, func, args, kwargs):
+        def dequant_arg(arg):
+            if isinstance(arg, QuantizedTensor):
+                return arg.dequantize()
+            elif isinstance(arg, (list, tuple)):
+                return type(arg)(dequant_arg(a) for a in arg)
+            return arg
+
+        new_args = dequant_arg(args)
+        new_kwargs = dequant_arg(kwargs)
+        return func(*new_args, **new_kwargs)
+
+
+# ==============================================================================
+# Generic Utilities (Layout-Agnostic Operations)
+# ==============================================================================
+
+def _create_transformed_qtensor(qt, transform_fn):
+    new_data = transform_fn(qt._qdata)
+    new_params = _copy_layout_params(qt._layout_params)
+    return QuantizedTensor(new_data, qt._layout_type, new_params)
+
+
+def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
+    if target_dtype is not None and target_dtype != qt.dtype:
+        logging.warning(
+            f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
+            f"but not supported for quantized tensors. Ignoring dtype."
+        )
+
+    if target_layout is not None and target_layout != torch.strided:
+        logging.warning(
+            f"QuantizedTensor: layout change requested to {target_layout}, "
+            f"but not supported. Ignoring layout."
+        )
+
+    # Handle device transfer
+    current_device = qt._qdata.device
+    if target_device is not None:
+        # Normalize device for comparison
+        if isinstance(target_device, str):
+            target_device = torch.device(target_device)
+        if isinstance(current_device, str):
+            current_device = torch.device(current_device)
+
+        if target_device != current_device:
+            logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
+            new_q_data = qt._qdata.to(device=target_device)
+            new_params = _move_layout_params_to_device(qt._layout_params, target_device)
+            new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
+            logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
+            return new_qt
+
+    logging.debug(f"QuantizedTensor.{op_name}: No device change needed, returning original")
+    return qt
+
+
+@register_generic_util(torch.ops.aten.detach.default)
+def generic_detach(func, args, kwargs):
+    """Detach operation - creates a detached copy of the quantized tensor."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _create_transformed_qtensor(qt, lambda x: x.detach())
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.clone.default)
+def generic_clone(func, args, kwargs):
+    """Clone operation - creates a deep copy of the quantized tensor."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _create_transformed_qtensor(qt, lambda x: x.clone())
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten._to_copy.default)
+def generic_to_copy(func, args, kwargs):
+    """Device/dtype transfer operation - handles .to(device) calls."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _handle_device_transfer(
+            qt,
+            target_device=kwargs.get('device', None),
+            target_dtype=kwargs.get('dtype', None),
+            op_name="_to_copy"
+        )
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.to.dtype_layout)
+def generic_to_dtype_layout(func, args, kwargs):
+    """Handle .to(device) calls using the dtype_layout variant."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        return _handle_device_transfer(
+            qt,
+            target_device=kwargs.get('device', None),
+            target_dtype=kwargs.get('dtype', None),
+            target_layout=kwargs.get('layout', None),
+            op_name="to"
+        )
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten.copy_.default)
+def generic_copy_(func, args, kwargs):
+    qt_dest = args[0]
+    src = args[1]
+    non_blocking = args[2] if len(args) > 2 else False
+    if isinstance(qt_dest, QuantizedTensor):
+        if isinstance(src, QuantizedTensor):
+            # Copy from another quantized tensor
+            qt_dest._qdata.copy_(src._qdata, non_blocking=non_blocking)
+            qt_dest._layout_type = src._layout_type
+            _copy_layout_params_inplace(src._layout_params, qt_dest._layout_params, non_blocking=non_blocking)
+        else:
+            # Copy from regular tensor - just copy raw data
+            qt_dest._qdata.copy_(src)
+        return qt_dest
+    return func(*args, **kwargs)
+
+
+@register_generic_util(torch.ops.aten._has_compatible_shallow_copy_type.default)
+def generic_has_compatible_shallow_copy_type(func, args, kwargs):
+    return True
+
+
+@register_generic_util(torch.ops.aten.empty_like.default)
+def generic_empty_like(func, args, kwargs):
+    """Empty_like operation - creates an empty tensor with the same quantized structure."""
+    qt = args[0]
+    if isinstance(qt, QuantizedTensor):
+        # Create empty tensor with same shape and dtype as the quantized data
+        hp_dtype = kwargs.pop('dtype', qt._layout_params["orig_dtype"])
+        new_qdata = torch.empty_like(qt._qdata, **kwargs)
+
+        # Handle device transfer for layout params
+        target_device = kwargs.get('device', new_qdata.device)
+        new_params = _move_layout_params_to_device(qt._layout_params, target_device)
+
+        # Update orig_dtype if dtype is specified
+        new_params['orig_dtype'] = hp_dtype
+
+        return QuantizedTensor(new_qdata, qt._layout_type, new_params)
+    return func(*args, **kwargs)
+
+# ==============================================================================
+# FP8 Layout + Operation Handlers
+# ==============================================================================
+class TensorCoreFP8Layout(QuantizedLayout):
+    """
+    Storage format:
+    - qdata: FP8 tensor (torch.float8_e4m3fn or torch.float8_e5m2)
+    - scale: Scalar tensor (float32) for dequantization
+    - orig_dtype: Original dtype before quantization (for casting back)
+    """
+    @classmethod
+    def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn):
+        orig_dtype = tensor.dtype
+
+        if scale is None:
+            scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
+
+        if not isinstance(scale, torch.Tensor):
+            scale = torch.tensor(scale)
+        scale = scale.to(device=tensor.device, dtype=torch.float32)
+
+        tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
+        # TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
+        # lp_amax = torch.finfo(dtype).max
+        # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
+        qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
+
+        layout_params = {
+            'scale': scale,
+            'orig_dtype': orig_dtype
+        }
+        return qdata, layout_params
+
+    @staticmethod
+    def dequantize(qdata, scale, orig_dtype, **kwargs):
+        plain_tensor = torch.ops.aten._to_copy.default(qdata, dtype=orig_dtype)
+        return plain_tensor * scale
+
+    @classmethod
+    def get_plain_tensors(cls, qtensor):
+        return qtensor._qdata, qtensor._layout_params['scale']
+
+QUANT_ALGOS = {
+    "float8_e4m3fn": {
+        "storage_t": torch.float8_e4m3fn,
+        "parameters": {"weight_scale", "input_scale"},
+        "comfy_tensor_layout": "TensorCoreFP8Layout",
+    },
+}
+
+LAYOUTS = {
+    "TensorCoreFP8Layout": TensorCoreFP8Layout,
+}
+
+
+@register_layout_op(torch.ops.aten.linear.default, "TensorCoreFP8Layout")
+def fp8_linear(func, args, kwargs):
+    input_tensor = args[0]
+    weight = args[1]
+    bias = args[2] if len(args) > 2 else None
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+        plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
+
+        out_dtype = kwargs.get("out_dtype")
+        if out_dtype is None:
+            out_dtype = input_tensor._layout_params['orig_dtype']
+
+        weight_t = plain_weight.t()
+
+        tensor_2d = False
+        if len(plain_input.shape) == 2:
+            tensor_2d = True
+            plain_input = plain_input.unsqueeze(1)
+
+        input_shape = plain_input.shape
+        if len(input_shape) != 3:
+            return None
+
+        try:
+            output = torch._scaled_mm(
+                plain_input.reshape(-1, input_shape[2]).contiguous(),
+                weight_t,
+                bias=bias,
+                scale_a=scale_a,
+                scale_b=scale_b,
+                out_dtype=out_dtype,
+            )
+
+            if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
+                output = output[0]
+
+            if not tensor_2d:
+                output = output.reshape((-1, input_shape[1], weight.shape[0]))
+
+            if output.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+                output_scale = scale_a * scale_b
+                output_params = {
+                    'scale': output_scale,
+                    'orig_dtype': input_tensor._layout_params['orig_dtype']
+                }
+                return QuantizedTensor(output, "TensorCoreFP8Layout", output_params)
+            else:
+                return output
+
+        except Exception as e:
+            raise RuntimeError(f"FP8 _scaled_mm failed, falling back to dequantization: {e}")
+
+    # Case 2: DQ Fallback
+    if isinstance(weight, QuantizedTensor):
+        weight = weight.dequantize()
+    if isinstance(input_tensor, QuantizedTensor):
+        input_tensor = input_tensor.dequantize()
+
+    return torch.nn.functional.linear(input_tensor, weight, bias)
+
+def fp8_mm_(input_tensor, weight, bias=None, out_dtype=None):
+    if out_dtype is None:
+        out_dtype = input_tensor._layout_params['orig_dtype']
+
+    plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+    plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
+
+    output = torch._scaled_mm(
+        plain_input.contiguous(),
+        plain_weight,
+        bias=bias,
+        scale_a=scale_a,
+        scale_b=scale_b,
+        out_dtype=out_dtype,
+    )
+
+    if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
+        output = output[0]
+    return output
+
+@register_layout_op(torch.ops.aten.addmm.default, "TensorCoreFP8Layout")
+def fp8_addmm(func, args, kwargs):
+    input_tensor = args[1]
+    weight = args[2]
+    bias = args[0]
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        return fp8_mm_(input_tensor, weight, bias=bias, out_dtype=kwargs.get("out_dtype", None))
+
+    a = list(args)
+    if isinstance(args[0], QuantizedTensor):
+        a[0] = args[0].dequantize()
+    if isinstance(args[1], QuantizedTensor):
+        a[1] = args[1].dequantize()
+    if isinstance(args[2], QuantizedTensor):
+        a[2] = args[2].dequantize()
+
+    return func(*a, **kwargs)
+
+@register_layout_op(torch.ops.aten.mm.default, "TensorCoreFP8Layout")
+def fp8_mm(func, args, kwargs):
+    input_tensor = args[0]
+    weight = args[1]
+
+    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
+        return fp8_mm_(input_tensor, weight, bias=None, out_dtype=kwargs.get("out_dtype", None))
+
+    a = list(args)
+    if isinstance(args[0], QuantizedTensor):
+        a[0] = args[0].dequantize()
+    if isinstance(args[1], QuantizedTensor):
+        a[1] = args[1].dequantize()
+    return func(*a, **kwargs)
+
+@register_layout_op(torch.ops.aten.view.default, "TensorCoreFP8Layout")
+@register_layout_op(torch.ops.aten.t.default, "TensorCoreFP8Layout")
+def fp8_func(func, args, kwargs):
+    input_tensor = args[0]
+    if isinstance(input_tensor, QuantizedTensor):
+        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
+        ar = list(args)
+        ar[0] = plain_input
+        return QuantizedTensor(func(*ar, **kwargs), "TensorCoreFP8Layout", input_tensor._layout_params)
+    return func(*args, **kwargs)

comfy/sample.py

@@ -4,13 +4,9 @@ import comfy.samplers
 import comfy.utils
 import numpy as np
 import logging
+import comfy.nested_tensor
 
-def prepare_noise(latent_image, seed, noise_inds=None):
-    """
-    creates random noise given a latent image and a seed.
-    optional arg skip can be used to skip and discard x number of noise generations for a given seed
-    """
-    generator = torch.manual_seed(seed)
+def prepare_noise_inner(latent_image, generator, noise_inds=None):
     if noise_inds is None:
         return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
 
@@ -21,10 +17,29 @@ def prepare_noise(latent_image, seed, noise_inds=None):
         if i in unique_inds:
             noises.append(noise)
     noises = [noises[i] for i in inverse]
-    noises = torch.cat(noises, axis=0)
+    return torch.cat(noises, axis=0)
+
+def prepare_noise(latent_image, seed, noise_inds=None):
+    """
+    creates random noise given a latent image and a seed.
+    optional arg skip can be used to skip and discard x number of noise generations for a given seed
+    """
+    generator = torch.manual_seed(seed)
+
+    if latent_image.is_nested:
+        tensors = latent_image.unbind()
+        noises = []
+        for t in tensors:
+            noises.append(prepare_noise_inner(t, generator, noise_inds))
+        noises = comfy.nested_tensor.NestedTensor(noises)
+    else:
+        noises = prepare_noise_inner(latent_image, generator, noise_inds)
+
     return noises
 
 def fix_empty_latent_channels(model, latent_image):
+    if latent_image.is_nested:
+        return latent_image
     latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
     if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
         latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)

comfy/samplers.py

@@ -306,17 +306,10 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                                                                                  copy_dict1=False)
 
             if patches is not None:
-                # TODO: replace with merge_nested_dicts function
-                if "patches" in transformer_options:
-                    cur_patches = transformer_options["patches"].copy()
-                    for p in patches:
-                        if p in cur_patches:
-                            cur_patches[p] = cur_patches[p] + patches[p]
-                        else:
-                            cur_patches[p] = patches[p]
-                    transformer_options["patches"] = cur_patches
-                else:
-                    transformer_options["patches"] = patches
+                transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts(
+                    transformer_options.get("patches", {}),
+                    patches
+                )
 
             transformer_options["cond_or_uncond"] = cond_or_uncond[:]
             transformer_options["uuids"] = uuids[:]
@@ -360,7 +353,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
 def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
     if "sampler_cfg_function" in model_options:
         args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
-                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond}
         cfg_result = x - model_options["sampler_cfg_function"](args)
     else:
         cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
@@ -390,7 +383,7 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
     for fn in model_options.get("sampler_pre_cfg_function", []):
         args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
                 "input": x, "sigma": timestep, "model": model, "model_options": model_options}
-        out  = fn(args)
+        out = fn(args)
 
     return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
 
@@ -789,7 +782,7 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}):
     return KSAMPLER(sampler_function, extra_options, inpaint_options)
 
 
-def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
+def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None, latent_shapes=None):
     for k in conds:
         conds[k] = conds[k][:]
         resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
@@ -799,7 +792,7 @@ def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=N
 
     if hasattr(model, 'extra_conds'):
         for k in conds:
-            conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
+            conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed, latent_shapes=latent_shapes)
 
     #make sure each cond area has an opposite one with the same area
     for k in conds:
@@ -969,11 +962,11 @@ class CFGGuider:
     def predict_noise(self, x, timestep, model_options={}, seed=None):
         return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
 
-    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
+    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None):
         if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
             latent_image = self.inner_model.process_latent_in(latent_image)
 
-        self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
+        self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes)
 
         extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
         extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas
@@ -987,7 +980,7 @@ class CFGGuider:
         samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
         return self.inner_model.process_latent_out(samples.to(torch.float32))
 
-    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None):
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
@@ -1001,7 +994,7 @@ class CFGGuider:
 
         try:
             self.model_patcher.pre_run()
-            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
         finally:
             self.model_patcher.cleanup()
 
@@ -1014,6 +1007,12 @@ class CFGGuider:
         if sigmas.shape[-1] == 0:
             return latent_image
 
+        if latent_image.is_nested:
+            latent_image, latent_shapes = comfy.utils.pack_latents(latent_image.unbind())
+            noise, _ = comfy.utils.pack_latents(noise.unbind())
+        else:
+            latent_shapes = [latent_image.shape]
+
         self.conds = {}
         for k in self.original_conds:
             self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
@@ -1033,7 +1032,7 @@ class CFGGuider:
                 self,
                 comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True)
             )
-            output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
         finally:
             cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)
             self.model_options = orig_model_options
@@ -1041,6 +1040,9 @@ class CFGGuider:
             self.model_patcher.restore_hook_patches()
 
         del self.conds
+
+        if len(latent_shapes) > 1:
+            output = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(output, latent_shapes))
         return output
 
 

comfy/sd.py

@@ -17,6 +17,9 @@ import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
+import comfy.ldm.hunyuan_video.vae
+import comfy.ldm.mmaudio.vae.autoencoder
+import comfy.pixel_space_convert
 import yaml
 import math
 import os
@@ -48,6 +51,7 @@ import comfy.text_encoders.hidream
 import comfy.text_encoders.ace
 import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
+import comfy.text_encoders.hunyuan_image
 
 import comfy.model_patcher
 import comfy.lora
@@ -139,6 +143,9 @@ class CLIP:
         n.apply_hooks_to_conds = self.apply_hooks_to_conds
         return n
 
+    def get_ram_usage(self):
+        return self.patcher.get_ram_usage()
+
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         return self.patcher.add_patches(patches, strength_patch, strength_model)
 
@@ -272,8 +279,13 @@ class VAE:
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
-        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
-        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
+        if model_management.is_amd():
+            VAE_KL_MEM_RATIO = 2.73
+        else:
+            VAE_KL_MEM_RATIO = 1.0
+
+        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower)
+        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
@@ -283,10 +295,13 @@ class VAE:
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
         self.disable_offload = False
+        self.not_video = False
+        self.size = None
 
         self.downscale_index_formula = None
         self.upscale_index_formula = None
         self.extra_1d_channel = None
+        self.crop_input = True
 
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
@@ -329,21 +344,50 @@ class VAE:
                 self.downscale_ratio = 32
                 self.latent_channels = 16
             elif "decoder.conv_in.weight" in sd:
-                #default SD1.x/SD2.x VAE parameters
-                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
-
-                if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
-                    ddconfig['ch_mult'] = [1, 2, 4]
-                    self.downscale_ratio = 4
-                    self.upscale_ratio = 4
-
-                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
-                if 'post_quant_conv.weight' in sd:
-                    self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
-                else:
+                if sd['decoder.conv_in.weight'].shape[1] == 64:
+                    ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    self.downscale_ratio = 32
+                    self.upscale_ratio = 32
+                    self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
                     self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
-                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+                                                                encoder_config={'target': "comfy.ldm.hunyuan_video.vae.Encoder", 'params': ddconfig},
+                                                                decoder_config={'target': "comfy.ldm.hunyuan_video.vae.Decoder", 'params': ddconfig})
+
+                    self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype)
+                elif sd['decoder.conv_in.weight'].shape[1] == 32:
+                    ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True, "refiner_vae": False}
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                    self.upscale_index_formula = (4, 16, 16)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                    self.downscale_index_formula = (4, 16, 16)
+                    self.latent_dim = 3
+                    self.not_video = True
+                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                                                                encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig},
+                                                                decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
+
+                    self.memory_used_encode = lambda shape, dtype: (2800 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (2800 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
+                else:
+                    #default SD1.x/SD2.x VAE parameters
+                    ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
+
+                    if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
+                        ddconfig['ch_mult'] = [1, 2, 4]
+                        self.downscale_ratio = 4
+                        self.upscale_ratio = 4
+
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    if 'post_quant_conv.weight' in sd:
+                        self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
+                    else:
+                        self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                                                                    encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
+                                                                    decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
             elif "decoder.layers.1.layers.0.beta" in sd:
                 self.first_stage_model = AudioOobleckVAE()
                 self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
@@ -394,6 +438,23 @@ class VAE:
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
                 self.downscale_index_formula = (8, 32, 32)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
+            elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32:
+                ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True}
+                ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
+                self.latent_channels = 32
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                self.upscale_index_formula = (4, 16, 16)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                self.downscale_index_formula = (4, 16, 16)
+                self.latent_dim = 3
+                self.not_video = False
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"},
+                                                            encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig},
+                                                            decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
+
+                self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (2800 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
             elif "decoder.conv_in.conv.weight" in sd:
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                 ddconfig["conv3d"] = True
@@ -446,17 +507,29 @@ class VAE:
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                     self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
                     self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
+            # Hunyuan 3d v2 2.0 & 2.1
             elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
+
                 self.latent_dim = 1
-                ln_post = "geo_decoder.ln_post.weight" in sd
-                inner_size = sd["geo_decoder.output_proj.weight"].shape[1]
-                downsample_ratio = sd["post_kl.weight"].shape[0] // inner_size
-                mlp_expand = sd["geo_decoder.cross_attn_decoder.mlp.c_fc.weight"].shape[0] // inner_size
-                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)  # TODO
-                self.memory_used_decode = lambda shape, dtype: (1024 * 1024 * 1024 * 2.0) * model_management.dtype_size(dtype)  # TODO
-                ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post}
-                self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig)
+
+                def estimate_memory(shape, dtype, num_layers = 16, kv_cache_multiplier = 2):
+                    batch, num_tokens, hidden_dim = shape
+                    dtype_size = model_management.dtype_size(dtype)
+
+                    total_mem = batch * num_tokens * hidden_dim * dtype_size * (1 + kv_cache_multiplier * num_layers)
+                    return total_mem
+
+                # better memory estimations
+                self.memory_used_encode = lambda shape, dtype, num_layers = 8, kv_cache_multiplier = 0:\
+                    estimate_memory(shape, dtype, num_layers, kv_cache_multiplier)
+
+                self.memory_used_decode = lambda shape, dtype, num_layers = 16, kv_cache_multiplier = 2: \
+                    estimate_memory(shape, dtype, num_layers, kv_cache_multiplier)
+
+                self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE()
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+
             elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio
                 self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100)
                 self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype)
@@ -471,6 +544,34 @@ class VAE:
                 self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                 self.disable_offload = True
                 self.extra_1d_channel = 16
+            elif "pixel_space_vae" in sd:
+                self.first_stage_model = comfy.pixel_space_convert.PixelspaceConversionVAE()
+                self.memory_used_encode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.downscale_ratio = 1
+                self.upscale_ratio = 1
+                self.latent_channels = 3
+                self.latent_dim = 2
+                self.output_channels = 3
+            elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE
+                sample_rate = 16000
+                if sample_rate == 16000:
+                    mode = '16k'
+                else:
+                    mode = '44k'
+
+                self.first_stage_model = comfy.ldm.mmaudio.vae.autoencoder.AudioAutoencoder(mode=mode)
+                self.memory_used_encode = lambda shape, dtype: (30 * shape[2]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (90 * shape[2] * 1411.2) * model_management.dtype_size(dtype)
+                self.latent_channels = 20
+                self.output_channels = 2
+                self.upscale_ratio = 512 * (44100 / sample_rate)
+                self.downscale_ratio = 512 * (44100 / sample_rate)
+                self.latent_dim = 1
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
+                self.working_dtypes = [torch.float32]
+                self.crop_input = False
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -498,12 +599,25 @@ class VAE:
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
         logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+        self.model_size()
+
+    def model_size(self):
+        if self.size is not None:
+            return self.size
+        self.size = comfy.model_management.module_size(self.first_stage_model)
+        return self.size
+
+    def get_ram_usage(self):
+        return self.model_size()
 
     def throw_exception_if_invalid(self):
         if self.first_stage_model is None:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
 
     def vae_encode_crop_pixels(self, pixels):
+        if not self.crop_input:
+            return pixels
+
         downscale_ratio = self.spacial_compression_encode()
 
         dims = pixels.shape[1:-1]
@@ -581,6 +695,7 @@ class VAE:
     def decode(self, samples_in, vae_options={}):
         self.throw_exception_if_invalid()
         pixel_samples = None
+        do_tile = False
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -596,6 +711,13 @@ class VAE:
                 pixel_samples[x:x+batch_number] = out
         except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+            #NOTE: We don't know what tensors were allocated to stack variables at the time of the
+            #exception and the exception itself refs them all until we get out of this except block.
+            #So we just set a flag for tiler fallback so that tensor gc can happen once the
+            #exception is fully off the books.
+            do_tile = True
+
+        if do_tile:
             dims = samples_in.ndim - 2
             if dims == 1 or self.extra_1d_channel is not None:
                 pixel_samples = self.decode_tiled_1d(samples_in)
@@ -642,8 +764,12 @@ class VAE:
         self.throw_exception_if_invalid()
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         pixel_samples = pixel_samples.movedim(-1, 1)
+        do_tile = False
         if self.latent_dim == 3 and pixel_samples.ndim < 5:
-            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            if not self.not_video:
+                pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            else:
+                pixel_samples = pixel_samples.unsqueeze(2)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -660,6 +786,13 @@ class VAE:
 
         except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+            #NOTE: We don't know what tensors were allocated to stack variables at the time of the
+            #exception and the exception itself refs them all until we get out of this except block.
+            #So we just set a flag for tiler fallback so that tensor gc can happen once the
+            #exception is fully off the books.
+            do_tile = True
+
+        if do_tile:
             if self.latent_dim == 3:
                 tile = 256
                 overlap = tile // 4
@@ -677,7 +810,10 @@ class VAE:
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
         if dims == 3:
-            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            if not self.not_video:
+                pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            else:
+                pixel_samples = pixel_samples.unsqueeze(2)
 
         memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)  # TODO: calculate mem required for tile
         model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -734,6 +870,7 @@ class VAE:
         except:
             return None
 
+
 class StyleModel:
     def __init__(self, model, device="cpu"):
         self.model = model
@@ -773,6 +910,8 @@ class CLIPType(Enum):
     ACE = 16
     OMNIGEN2 = 17
     QWEN_IMAGE = 18
+    HUNYUAN_IMAGE = 19
+    HUNYUAN_VIDEO_15 = 20
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -794,6 +933,8 @@ class TEModel(Enum):
     GEMMA_2_2B = 9
     QWEN25_3B = 10
     QWEN25_7B = 11
+    BYT5_SMALL_GLYPH = 12
+    GEMMA_3_4B = 13
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -811,8 +952,13 @@ def detect_te_model(sd):
     if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd:
         return TEModel.T5_XXL_OLD
     if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
+        weight = sd['encoder.block.0.layer.0.SelfAttention.k.weight']
+        if weight.shape[0] == 384:
+            return TEModel.BYT5_SMALL_GLYPH
         return TEModel.T5_BASE
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
+        if 'model.layers.0.self_attn.q_norm.weight' in sd:
+            return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
     if 'model.layers.0.self_attn.k_proj.bias' in sd:
         weight = sd['model.layers.0.self_attn.k_proj.bias']
@@ -917,6 +1063,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_4B:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
+            clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
@@ -925,8 +1075,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer
         elif te_model == TEModel.QWEN25_7B:
-            clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
-            clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer
+            if clip_type == CLIPType.HUNYUAN_IMAGE:
+                clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
+            else:
+                clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer
         else:
             # clip_l
             if clip_type == CLIPType.SD3:
@@ -970,6 +1124,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
 
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
+        elif clip_type == CLIPType.HUNYUAN_IMAGE:
+            clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
+        elif clip_type == CLIPType.HUNYUAN_VIDEO_15:
+            clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
@@ -1120,7 +1280,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options={}):
+def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     """
     Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.
 
@@ -1154,7 +1314,7 @@ def load_diffusion_model_state_dict(sd, model_options={}):
     weight_dtype = comfy.utils.weight_dtype(sd)
 
     load_device = model_management.get_torch_device()
-    model_config = model_detection.model_config_from_unet(sd, "")
+    model_config = model_detection.model_config_from_unet(sd, "", metadata=metadata)
 
     if model_config is not None:
         new_sd = sd
@@ -1188,7 +1348,10 @@ def load_diffusion_model_state_dict(sd, model_options={}):
     else:
         unet_dtype = dtype
 
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    if model_config.layer_quant_config is not None:
+        manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
+    else:
+        manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
     if model_options.get("fp8_optimizations", False):
@@ -1204,8 +1367,8 @@ def load_diffusion_model_state_dict(sd, model_options={}):
 
 
 def load_diffusion_model(unet_path, model_options={}):
-    sd = comfy.utils.load_torch_file(unet_path)
-    model = load_diffusion_model_state_dict(sd, model_options=model_options)
+    sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True)
+    model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata)
     if model is None:
         logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd)))