0ee322ec5f
* Added hook_patches to ModelPatcher for weights (model) * Initial changes to calc_cond_batch to eventually support hook_patches * Added current_patcher property to BaseModel * Consolidated add_hook_patches_as_diffs into add_hook_patches func, fixed fp8 support for model-as-lora feature * Added call to initialize_timesteps on hooks in process_conds func, and added call prepare current keyframe on hooks in calc_cond_batch * Added default_conds support in calc_cond_batch func * Added initial set of hook-related nodes, added code to register hooks for loras/model-as-loras, small renaming/refactoring * Made CLIP work with hook patches * Added initial hook scheduling nodes, small renaming/refactoring * Fixed MaxSpeed and default conds implementations * Added support for adding weight hooks that aren't registered on the ModelPatcher at sampling time * Made Set Clip Hooks node work with hooks from Create Hook nodes, began work on better Create Hook Model As LoRA node * Initial work on adding 'model_as_lora' lora type to calculate_weight * Continued work on simpler Create Hook Model As LoRA node, started to implement ModelPatcher callbacks, attachments, and additional_models * Fix incorrect ref to create_hook_patches_clone after moving function * Added injections support to ModelPatcher + necessary bookkeeping, added additional_models support in ModelPatcher, conds, and hooks * Added wrappers to ModelPatcher to facilitate standardized function wrapping * Started scaffolding for other hook types, refactored get_hooks_from_cond to organize hooks by type * Fix skip_until_exit logic bug breaking injection after first run of model * Updated clone_has_same_weights function to account for new ModelPatcher properties, improved AutoPatcherEjector usage in partially_load * Added WrapperExecutor for non-classbound functions, added calc_cond_batch wrappers * Refactored callbacks+wrappers to allow storing lists by id * Added forward_timestep_embed_patch type, added helper functions on ModelPatcher for emb_patch and forward_timestep_embed_patch, added helper functions for removing callbacks/wrappers/additional_models by key, added custom_should_register prop to hooks * Added get_attachment func on ModelPatcher * Implement basic MemoryCounter system for determing with cached weights due to hooks should be offloaded in hooks_backup * Modified ControlNet/T2IAdapter get_control function to receive transformer_options as additional parameter, made the model_options stored in extra_args in inner_sample be a clone of the original model_options instead of same ref * Added create_model_options_clone func, modified type annotations to use __future__ so that I can use the better type annotations * Refactored WrapperExecutor code to remove need for WrapperClassExecutor (now gone), added sampler.sample wrapper (pending review, will likely keep but will see what hacks this could currently let me get rid of in ACN/ADE) * Added Combine versions of Cond/Cond Pair Set Props nodes, renamed Pair Cond to Cond Pair, fixed default conds never applying hooks (due to hooks key typo) * Renamed Create Hook Model As LoRA nodes to make the test node the main one (more changes pending) * Added uuid to conds in CFGGuider and uuids to transformer_options to allow uniquely identifying conds in batches during sampling * Fixed models not being unloaded properly due to current_patcher reference; the current ComfyUI model cleanup code requires that nothing else has a reference to the ModelPatcher instances * Fixed default conds not respecting hook keyframes, made keyframes not reset cache when strength is unchanged, fixed Cond Set Default Combine throwing error, fixed model-as-lora throwing error during calculate_weight after a recent ComfyUI update, small refactoring/scaffolding changes for hooks * Changed CreateHookModelAsLoraTest to be the new CreateHookModelAsLora, rename old ones as 'direct' and will be removed prior to merge * Added initial support within CLIP Text Encode (Prompt) node for scheduling weight hook CLIP strength via clip_start_percent/clip_end_percent on conds, added schedule_clip toggle to Set CLIP Hooks node, small cleanup/fixes * Fix range check in get_hooks_for_clip_schedule so that proper keyframes get assigned to corresponding ranges * Optimized CLIP hook scheduling to treat same strength as same keyframe * Less fragile memory management. * Make encode_from_tokens_scheduled call cleaner, rollback change in model_patcher.py for hook_patches_backup dict * Fix issue. * Remove useless function. * Prevent and detect some types of memory leaks. * Run garbage collector when switching workflow if needed. * Moved WrappersMP/CallbacksMP/WrapperExecutor to patcher_extension.py * Refactored code to store wrappers and callbacks in transformer_options, added apply_model and diffusion_model.forward wrappers * Fix issue. * Refactored hooks in calc_cond_batch to be part of get_area_and_mult tuple, added extra_hooks to ControlBase to allow custom controlnets w/ hooks, small cleanup and renaming * Fixed inconsistency of results when schedule_clip is set to False, small renaming/typo fixing, added initial support for ControlNet extra_hooks to work in tandem with normal cond hooks, initial work on calc_cond_batch merging all subdicts in returned transformer_options * Modified callbacks and wrappers so that unregistered types can be used, allowing custom_nodes to have their own unique callbacks/wrappers if desired * Updated different hook types to reflect actual progress of implementation, initial scaffolding for working WrapperHook functionality * Fixed existing weight hook_patches (pre-registered) not working properly for CLIP * Removed Register/Direct hook nodes since they were present only for testing, removed diff-related weight hook calculation as improved_memory removes unload_model_clones and using sample time registered hooks is less hacky * Added clip scheduling support to all other native ComfyUI text encoding nodes (sdxl, flux, hunyuan, sd3) * Made WrapperHook functional, added another wrapper/callback getter, added ON_DETACH callback to ModelPatcher * Made opt_hooks append by default instead of replace, renamed comfy.hooks set functions to be more accurate * Added apply_to_conds to Set CLIP Hooks, modified relevant code to allow text encoding to automatically apply hooks to output conds when apply_to_conds is set to True * Fix cached_hook_patches not respecting target_device/memory_counter results * Fixed issue with setting weights from hooks instead of copying them, added additional memory_counter check when caching hook patches * Remove unnecessary torch.no_grad calls for hook patches * Increased MemoryCounter minimum memory to leave free by *2 until a better way to get inference memory estimate of currently loaded models exists * For encode_from_tokens_scheduled, allow start_percent and end_percent in add_dict to limit which scheduled conds get encoded for optimization purposes * Removed a .to call on results of calculate_weight in patch_hook_weight_to_device that was screwing up the intermediate results for fp8 prior to being passed into stochastic_rounding call * Made encode_from_tokens_scheduled work when no hooks are set on patcher * Small cleanup of comments * Turn off hook patch caching when only 1 hook present in sampling, replace some current_hook = None with calls to self.patch_hooks(None) instead to avoid a potential edge case * On Cond/Cond Pair nodes, removed opt_ prefix from optional inputs * Allow both FLOATS and FLOAT for floats_strength input * Revert change, does not work * Made patch_hook_weight_to_device respect set_func and convert_func * Make discard_model_sampling True by default * Add changes manually from 'master' so merge conflict resolution goes more smoothly * Cleaned up text encode nodes with just a single clip.encode_from_tokens_scheduled call * Make sure encode_from_tokens_scheduled will respect use_clip_schedule on clip * Made nodes in nodes_hooks be marked as experimental (beta) * Add get_nested_additional_models for cases where additional_models could have their own additional_models, and add robustness for circular additional_models references * Made finalize_default_conds area math consistent with other sampling code * Changed 'opt_hooks' input of Cond/Cond Pair Set Default Combine nodes to 'hooks' * Remove a couple old TODO's and a no longer necessary workaround |
||
|---|---|---|
| .ci | ||
| .github | ||
| api_server | ||
| app | ||
| comfy | ||
| comfy_execution | ||
| comfy_extras | ||
| custom_nodes | ||
| input | ||
| models | ||
| notebooks | ||
| output | ||
| script_examples | ||
| tests | ||
| tests-unit | ||
| utils | ||
| web | ||
| .gitattributes | ||
| .gitignore | ||
| .pylintrc | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cuda_malloc.py | ||
| execution.py | ||
| extra_model_paths.yaml.example | ||
| fix_torch.py | ||
| folder_paths.py | ||
| latent_preview.py | ||
| LICENSE | ||
| main.py | ||
| new_updater.py | ||
| node_helpers.py | ||
| nodes.py | ||
| pytest.ini | ||
| README.md | ||
| requirements.txt | ||
| server.py | ||
ComfyUI
The most powerful and modular diffusion model GUI and backend.
This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
ComfyUI Examples
Installing ComfyUI
Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Fully supports SD1.x, SD2.x, SDXL, Stable Video Diffusion, Stable Cascade, SD3 and Stable Audio
- LTX-Video
- Flux
- Mochi
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
- Works even if you don't have a GPU with:
--cpu(slow) - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
- 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...)
- unCLIP Models
- GLIGEN
- Model Merging
- LCM models and Loras
- SDXL Turbo
- AuraFlow
- HunyuanDiT
- Latent previews with TAESD
- Starts up very fast.
- Works fully offline: will never download anything.
- Config file to set the search paths for models.
Workflow examples can be found on the Examples page
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 |
| 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
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 and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
If you have trouble extracting it, right click the file -> properties -> unblock
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.
Jupyter Notebook
To run it on services like paperspace, kaggle or colab you can use my Jupyter Notebook
Manual Install (Windows, Linux)
Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
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 only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2
This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2
NVIDIA
Nvidia users should install stable pytorch using this command:
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124
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/cu124
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:
Intel GPUs
Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the Installation page. Choose your platform and method of install and follow the instructions. The steps are as follows:
- Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
- Follow the instructions to install Intel's oneAPI Basekit for your platform.
- Install the packages for IPEX using the instructions provided in the Installation page for your platform.
- Follow the ComfyUI manual installation instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
Additional discussion and help can be found here.
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.
DirectML (AMD Cards on Windows)
pip install torch-directml Then you can launch ComfyUI with: python main.py --directml
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 pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention
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
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 weekly.
- 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 weekly 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.