* feat: Add CacheProvider API for external distributed caching Introduces a public API for external cache providers, enabling distributed caching across multiple ComfyUI instances (e.g., Kubernetes pods). New files: - comfy_execution/cache_provider.py: CacheProvider ABC, CacheContext/CacheValue dataclasses, thread-safe provider registry, serialization utilities Modified files: - comfy_execution/caching.py: Add provider hooks to BasicCache (_notify_providers_store, _check_providers_lookup), subcache exclusion, prompt ID propagation - execution.py: Add prompt lifecycle hooks (on_prompt_start/on_prompt_end) to PromptExecutor, set _current_prompt_id on caches Key features: - Local-first caching (check local before external for performance) - NaN detection to prevent incorrect external cache hits - Subcache exclusion (ephemeral subgraph results not cached externally) - Thread-safe provider snapshot caching - Graceful error handling (provider errors logged, never break execution) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: use deterministic hash for cache keys instead of pickle Pickle serialization is NOT deterministic across Python sessions due to hash randomization affecting frozenset iteration order. This causes distributed caching to fail because different pods compute different hashes for identical cache keys. Fix: Use _canonicalize() + JSON serialization which ensures deterministic ordering regardless of Python's hash randomization. This is critical for cross-pod cache key consistency in Kubernetes deployments. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * test: add unit tests for CacheProvider API - Add comprehensive tests for _canonicalize deterministic ordering - Add tests for serialize_cache_key hash consistency - Add tests for contains_nan utility - Add tests for estimate_value_size - Add tests for provider registry (register, unregister, clear) - Move json import to top-level (fix inline import) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * style: remove unused imports in test_cache_provider.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: move _torch_available before usage and use importlib.util.find_spec Fixes ruff F821 (undefined name) and F401 (unused import) errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: use hashable types in frozenset test and add dict test Frozensets can only contain hashable types, so use nested frozensets instead of dicts. Added separate test for dict handling via serialize_cache_key. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: expose CacheProvider API via comfy_api.latest.Caching - Add Caching class to comfy_api/latest/__init__.py that re-exports from comfy_execution.cache_provider (source of truth) - Fix docstring: "Skip large values" instead of "Skip small values" (small compute-heavy values are good cache targets) - Maintain backward compatibility: comfy_execution.cache_provider imports still work Usage: from comfy_api.latest import Caching class MyProvider(Caching.CacheProvider): def on_lookup(self, context): ... def on_store(self, context, value): ... Caching.register_provider(MyProvider()) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * docs: clarify should_cache filtering criteria Change docstring from "Skip large values" to "Skip if download time > compute time" which better captures the cost/benefit tradeoff for external caching. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * docs: make should_cache docstring implementation-agnostic Remove prescriptive filtering suggestions - let implementations decide their own caching logic based on their use case. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add optional ui field to CacheValue - Add ui field to CacheValue dataclass (default None) - Pass ui when creating CacheValue for external providers - Use result.ui (or default {}) when returning from external cache lookup This allows external cache implementations to store/retrieve UI data if desired, while remaining optional for implementations that skip it. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: rename _is_cacheable_value to _is_external_cacheable_value Clearer name since objects are also cached locally - this specifically checks for external caching eligibility. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: async CacheProvider API + reduce public surface - Make on_lookup/on_store async on CacheProvider ABC - Simplify CacheContext: replace cache_key + cache_key_bytes with cache_key_hash (str hex digest) - Make registry/utility functions internal (_prefix) - Trim comfy_api.latest.Caching exports to core API only - Make cache get/set async throughout caching.py hierarchy - Use asyncio.create_task for fire-and-forget on_store - Add NaN gating before provider calls in Core - Add await to 5 cache call sites in execution.py Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: remove unused imports (ruff) and update tests for internal API - Remove unused CacheContext and _serialize_cache_key imports from caching.py (now handled by _build_context helper) - Update test_cache_provider.py to use _-prefixed internal names - Update tests for new CacheContext.cache_key_hash field (str) - Make MockCacheProvider methods async to match ABC Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: address coderabbit review feedback - Add try/except to _build_context, return None when hash fails - Return None from _serialize_cache_key on total failure (no id()-based fallback) - Replace hex-like test literal with non-secret placeholder Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: use _-prefixed imports in _notify_prompt_lifecycle The lifecycle notification method was importing the old non-prefixed names (has_cache_providers, get_cache_providers, logger) which no longer exist after the API cleanup. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: add sync get_local/set_local for graph traversal ExecutionList in graph.py calls output_cache.get() and .set() from sync methods (is_cached, cache_link, get_cache). These cannot await the now-async get/set. Add get_local/set_local that bypass external providers and only access the local dict — which is all graph traversal needs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: remove cloud-specific language from cache provider API Make all docstrings and comments generic for the OSS codebase. Remove references to Kubernetes, Redis, GCS, pods, and other infrastructure-specific terminology. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * style: align documentation with codebase conventions Strip verbose docstrings and section banners to match existing minimal documentation style used throughout the codebase. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: add usage example to Caching class, remove pickle fallback - Add docstring with usage example to Caching class matching the convention used by sibling APIs (Execution.set_progress, ComfyExtension) - Remove non-deterministic pickle fallback from _serialize_cache_key; return None on JSON failure instead of producing unretrievable hashes - Move cache_provider imports to top of execution.py (no circular dep) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: move public types to comfy_api, eager provider snapshot Address review feedback: - Move CacheProvider/CacheContext/CacheValue definitions to comfy_api/latest/_caching.py (source of truth for public API) - comfy_execution/cache_provider.py re-exports types from there - Build _providers_snapshot eagerly on register/unregister instead of lazy memoization in _get_cache_providers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: generalize self-inequality check, fail-closed canonicalization Address review feedback from guill: - Rename _contains_nan to _contains_self_unequal, use not (x == x) instead of math.isnan to catch any self-unequal value - Remove Unhashable and repr() fallbacks from _canonicalize; raise ValueError for unknown types so _serialize_cache_key returns None and external caching is skipped (fail-closed) - Update tests for renamed function and new fail-closed behavior Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: suppress ruff F401 for re-exported CacheContext CacheContext is imported from _caching and re-exported for use by caching.py. Add noqa comment to satisfy the linter. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: enable external caching for subcache (expanded) nodes Subcache nodes (from node expansion) now participate in external provider store/lookup. Previously skipped to avoid duplicates, but the cost of missing partial-expansion cache hits outweighs redundant stores — especially with looping behavior on the horizon. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: wrap register/unregister as explicit static methods Define register_provider and unregister_provider as wrapper functions in the Caching class instead of re-importing. This locks the public API signature in comfy_api/ so internal changes can't accidentally break it. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: use debug-level logging for provider registration Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: follow ProxiedSingleton pattern for Caching class Add Caching as a nested class inside ComfyAPI_latest inheriting from ProxiedSingleton with async instance methods, matching the Execution and NodeReplacement patterns. Retains standalone Caching class for direct import convenience. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: inline registration logic in Caching class Follow the Execution/NodeReplacement pattern — the public API methods contain the actual logic operating on cache_provider module state, not wrapper functions delegating to free functions. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: single Caching definition inside ComfyAPI_latest Remove duplicate standalone Caching class. Define it once as a nested class in ComfyAPI_latest (matching Execution/NodeReplacement pattern), with a module-level alias for import convenience. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: remove prompt_id from CacheContext, type-safe canonicalization Remove prompt_id from CacheContext — it's not relevant for cache matching and added unnecessary plumbing (_current_prompt_id on every cache). Lifecycle hooks still receive prompt_id directly. Include type name in canonicalized primitives so that int 7 and str "7" produce distinct hashes. Also canonicalize dict keys properly instead of str() coercion. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: address review feedback on cache provider API - Hold references to pending store tasks to prevent "Task was destroyed but it is still pending" warnings (bigcat88) - Parallel cache lookups with asyncio.gather instead of sequential awaits for better performance (bigcat88) - Delegate Caching.register/unregister_provider to existing functions in cache_provider.py instead of reimplementing (bigcat88) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
Get Started
Desktop Application
- The easiest way to get started.
- Available on Windows & macOS.
Windows Portable Package
- Get the latest commits and completely portable.
- Available on Windows.
Manual Install
Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
Examples
See what ComfyUI can do with the example workflows.
Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Image Models
- SD1.x, SD2.x (unCLIP)
- SDXL, SDXL Turbo
- Stable Cascade
- SD3 and SD3.5
- Pixart Alpha and Sigma
- AuraFlow
- HunyuanDiT
- Flux
- Lumina Image 2.0
- HiDream
- Qwen Image
- Hunyuan Image 2.1
- Flux 2
- Z Image
- Image Editing Models
- Video Models
- Audio Models
- 3D Models
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
- Works even if you don't have a GPU with:
--cpu(slow) - Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
- Safe loading of ckpt, pt, pth, etc.. files.
- Embeddings/Textual inversion
- Loras (regular, locon and loha)
- Hypernetworks
- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for Hires fix or much more advanced ones.
- Area Composition
- Inpainting with both regular and inpainting models.
- ControlNet and T2I-Adapter
- Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)
- GLIGEN
- Model Merging
- LCM models and Loras
- Latent previews with TAESD
- Works fully offline: core will never download anything unless you want to.
- Optional API nodes to use paid models from external providers through the online Comfy API disable with:
--disable-api-nodes - Config file to set the search paths for models.
Workflow examples can be found on the Examples page
Release Process
ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
-
- Releases a new stable version (e.g., v0.7.0) roughly every week.
- Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
- Minor versions will be used for releases off the master branch.
- Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
- Commits outside of the stable release tags may be very unstable and break many custom nodes.
- Serves as the foundation for the desktop release
-
- Builds a new release using the latest stable core version
-
- Weekly frontend updates are merged into the core repository
- Features are frozen for the upcoming core release
- Development continues for the next release cycle
Shortcuts
| Keybind | Explanation |
|---|---|
Ctrl + Enter |
Queue up current graph for generation |
Ctrl + Shift + Enter |
Queue up current graph as first for generation |
Ctrl + Alt + Enter |
Cancel current generation |
Ctrl + Z/Ctrl + Y |
Undo/Redo |
Ctrl + S |
Save workflow |
Ctrl + O |
Load workflow |
Ctrl + A |
Select all nodes |
Alt + C |
Collapse/uncollapse selected nodes |
Ctrl + M |
Mute/unmute selected nodes |
Ctrl + B |
Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
Delete/Backspace |
Delete selected nodes |
Ctrl + Backspace |
Delete the current graph |
Space |
Move the canvas around when held and moving the cursor |
Ctrl/Shift + Click |
Add clicked node to selection |
Ctrl + C/Ctrl + V |
Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
Ctrl + C/Ctrl + Shift + V |
Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
Shift + Drag |
Move multiple selected nodes at the same time |
Ctrl + D |
Load default graph |
Alt + + |
Canvas Zoom in |
Alt + - |
Canvas Zoom out |
Ctrl + Shift + LMB + Vertical drag |
Canvas Zoom in/out |
P |
Pin/Unpin selected nodes |
Ctrl + G |
Group selected nodes |
Q |
Toggle visibility of the queue |
H |
Toggle visibility of history |
R |
Refresh graph |
F |
Show/Hide menu |
. |
Fit view to selection (Whole graph when nothing is selected) |
| Double-Click LMB | Open node quick search palette |
Shift + Drag |
Move multiple wires at once |
Ctrl + Alt + LMB |
Disconnect all wires from clicked slot |
Ctrl can also be replaced with Cmd instead for macOS users
Installing
Windows Portable
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the releases page.
Direct link to download
Simply download, extract with 7-Zip 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
The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
Alternative Downloads:
Experimental portable for AMD GPUs
Portable with pytorch cuda 12.6 and python 3.12 (Supports Nvidia 10 series and older GPUs).
How do I share models between another UI and ComfyUI?
See the Config file 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.
comfy-cli
You can install and start ComfyUI using comfy-cli:
pip install comfy-cli
comfy install
Manual Install (Windows, Linux)
Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.
Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
Instructions:
Git clone this repo.
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
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/rocm7.1
This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2
AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
RDNA 3 (RX 7000 series):
pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/
RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/
RDNA 4 (RX 9000 series):
pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/
Intel GPUs (Windows and Linux)
Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found here
- To install PyTorch xpu, use the following command:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu
This is the command to install the Pytorch xpu nightly which might have some performance improvements:
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
NVIDIA
Nvidia users should install stable pytorch using this command:
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/cu130
Troubleshooting
If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
pip uninstall torch
And install it again with the command above.
Dependencies
Install the dependencies by opening your terminal inside the ComfyUI folder and:
pip install -r requirements.txt
After this you should have everything installed and can proceed to running ComfyUI.
Others:
Apple Mac silicon
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
- Install pytorch nightly. For instructions, read the Accelerated PyTorch training on Mac Apple Developer guide (make sure to install the latest pytorch nightly).
- Follow the ComfyUI manual installation instructions for Windows and Linux.
- Install the ComfyUI dependencies. If you have another Stable Diffusion UI you might be able to reuse the dependencies.
- Launch ComfyUI by running
python main.py
Note
: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in ComfyUI manual installation.
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 page. Here's a step-by-step guide tailored to your platform and installation method:
- Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
- Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
- Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the Installation page.
- Finally, adhere to the ComfyUI manual installation guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
Cambricon MLUs
For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
- Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the Installation
- Next, install the PyTorch(torch_mlu) following the instructions on the Installation
- Launch ComfyUI by running
python main.py
Iluvatar Corex
For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
- Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the Installation
- Launch ComfyUI by running
python main.py
ComfyUI-Manager
ComfyUI-Manager is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
Setup
-
Install the manager dependencies:
pip install -r manager_requirements.txt -
Enable the manager with the
--enable-managerflag when running ComfyUI:python main.py --enable-manager
Command Line Options
| Flag | Description |
|---|---|
--enable-manager |
Enable ComfyUI-Manager |
--enable-manager-legacy-ui |
Use the legacy manager UI instead of the new UI (requires --enable-manager) |
--disable-manager-ui |
Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires --enable-manager) |
Running
python main.py
For AMD cards not officially supported by ROCm
Try running it with this command if you have issues:
For 6700, 6600 and maybe other RDNA2 or older: HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py
For AMD 7600 and maybe other RDNA3 cards: HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py
AMD ROCm Tips
You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention
You can also try setting this env variable PYTORCH_TUNABLEOP_ENABLED=1 which might speed things up at the cost of a very slow initial run.
Notes
Only parts of the graph that have an output with all the correct inputs will be executed.
Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \( or \).
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \{ or \}.
Dynamic prompts also support C-style comments, like // comment or /* comment */.
To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
embedding:embedding_filename.pt
How to show high-quality previews?
Use --preview-method auto to enable previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with TAESD, download the taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth and place them in the models/vae_approx folder. Once they're installed, restart ComfyUI and launch it with --preview-method taesd to enable high-quality previews.
How to use TLS/SSL?
Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"
Use --tls-keyfile key.pem --tls-certfile cert.pem to enable TLS/SSL, the app will now be accessible with https://... instead of http://....
Note: Windows users can use alexisrolland/docker-openssl or one of the 3rd party binary distributions to run the command example above.
If you use a container, note that the volume mount-vcan be a relative path so... -v ".\:/openssl-certs" ...would create the key & cert files in the current directory of your command prompt or powershell terminal.
Support and dev channel
Discord: Try the #help or #feedback channels.
Matrix space: #comfyui_space:matrix.org (it's like discord but open source).
See also: https://www.comfy.org/
Frontend Development
As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: ComfyUI Frontend. This repository now hosts the compiled JS (from TS/Vue) under the web/ directory.
Reporting Issues and Requesting Features
For any bugs, issues, or feature requests related to the frontend, please use the ComfyUI Frontend repository. This will help us manage and address frontend-specific concerns more efficiently.
Using the Latest Frontend
The new frontend is now the default for ComfyUI. However, please note:
- The frontend in the main ComfyUI repository is updated fortnightly.
- Daily releases are available in the separate frontend repository.
To use the most up-to-date frontend version:
-
For the latest daily release, launch ComfyUI with this command line argument:
--front-end-version Comfy-Org/ComfyUI_frontend@latest -
For a specific version, replace
latestwith the desired version number:--front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
Accessing the Legacy Frontend
If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
This will use a snapshot of the legacy frontend preserved in the ComfyUI Legacy Frontend repository.