├── .editorconfig ├── .github ├── ISSUE_TEMPLATE.md └── workflows │ ├── build-pipeline.yml │ ├── publish_action.yml │ ├── publish_node.yml │ └── validate.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .vscode └── settings.json ├── Example.JPG ├── LICENSE ├── MANIFEST.in ├── Nodes.JPG ├── README.md ├── __init__.py ├── examples ├── Example_1 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_2 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_3 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_4 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_5 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_6 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_7 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_8 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── Example_9 │ ├── InPainted_Drag_Me_to_ComfyUI.png │ ├── Masked_Load_Me_in_Loader.png │ └── Original_No_Mask.png ├── InpaintChara_04.jpg ├── InpaintChara_05.jpg ├── InpaintChara_06.jpg ├── InpaintChara_07.jpg ├── InpaintChara_08.jpg ├── InpaintChara_09.jpg ├── InpaintChara_10.jpg ├── InpaintChara_11.jpg ├── InpaintChara_12.jpg └── InpaintChara_13.jpg ├── pyproject.toml ├── src └── LanPaint │ ├── __init__.py │ ├── lanpaint.py │ ├── nodes.py │ └── utils.py ├── tests ├── __init__.py ├── conftest.py ├── pytest.ini └── test_LanPaint.py └── web └── js └── example.js /.editorconfig: -------------------------------------------------------------------------------- 1 | # http://editorconfig.org 2 | 3 | root = true 4 | 5 | [*] 6 | indent_style = space 7 | indent_size = 4 8 | trim_trailing_whitespace = true 9 | insert_final_newline = true 10 | charset = utf-8 11 | end_of_line = lf 12 | 13 | [*.bat] 14 | indent_style = tab 15 | end_of_line = crlf 16 | 17 | [LICENSE] 18 | insert_final_newline = false 19 | 20 | [Makefile] 21 | indent_style = tab 22 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | * LanPaint version: 2 | * Python version: 3 | * Operating System: 4 | 5 | ### Description 6 | 7 | Describe what you were trying to get done. 8 | Tell us what happened, what went wrong, and what you expected to happen. 9 | 10 | ### What I Did 11 | 12 | ``` 13 | Paste the command(s) you ran and the output. 14 | If there was a crash, please include the traceback here. 15 | ``` 16 | -------------------------------------------------------------------------------- /.github/workflows/build-pipeline.yml: -------------------------------------------------------------------------------- 1 | # GitHub CI build pipeline 2 | name: LanPaint CI build 3 | 4 | on: 5 | pull_request: 6 | branches: 7 | - master 8 | - main 9 | jobs: 10 | build: 11 | runs-on: ${{ matrix.os }} 12 | env: 13 | PYTHONIOENCODING: "utf8" 14 | strategy: 15 | matrix: 16 | os: [ubuntu-latest] 17 | python-version: ["3.12"] 18 | 19 | steps: 20 | - uses: actions/checkout@v4 21 | - name: Set up Python 22 | uses: actions/setup-python@v5 23 | with: 24 | python-version: ${{ matrix.python-version }} 25 | - name: Install dependencies 26 | run: | 27 | python -m pip install --upgrade pip 28 | pip install .[dev] 29 | - name: Run Linting 30 | run: | 31 | ruff check . 32 | - name: Run Tests 33 | run: | 34 | pytest tests/ 35 | -------------------------------------------------------------------------------- /.github/workflows/publish_action.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | paths: 8 | - "pyproject.toml" 9 | 10 | permissions: 11 | issues: write 12 | 13 | jobs: 14 | publish-node: 15 | name: Publish Custom Node to registry 16 | runs-on: ubuntu-latest 17 | if: ${{ github.repository_owner == 'scraed' }} 18 | steps: 19 | - name: Check out code 20 | uses: actions/checkout@v4 21 | - name: Publish Custom Node 22 | uses: Comfy-Org/publish-node-action@v1 23 | with: 24 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github Repository secrets and reference it here. -------------------------------------------------------------------------------- /.github/workflows/publish_node.yml: -------------------------------------------------------------------------------- 1 | name: 📦 Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | tags: 6 | - '*' 7 | 8 | permissions: 9 | issues: write 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | steps: 16 | - name: ♻️ Check out code 17 | uses: actions/checkout@v4 18 | - name: 📦 Publish Custom Node 19 | uses: Comfy-Org/publish-node-action@main 20 | with: 21 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 22 | -------------------------------------------------------------------------------- /.github/workflows/validate.yml: -------------------------------------------------------------------------------- 1 | name: Validate backwards compatibility 2 | 3 | on: 4 | pull_request: 5 | branches: 6 | - master 7 | - main 8 | 9 | jobs: 10 | validate: 11 | runs-on: ubuntu-latest 12 | steps: 13 | - uses: comfy-org/node-diff@main 14 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # OSX useful to ignore 7 | *.DS_Store 8 | .AppleDouble 9 | .LSOverride 10 | 11 | # Thumbnails 12 | ._* 13 | 14 | # Files that might appear in the root of a volume 15 | .DocumentRevisions-V100 16 | .fseventsd 17 | .Spotlight-V100 18 | .TemporaryItems 19 | .Trashes 20 | .VolumeIcon.icns 21 | .com.apple.timemachine.donotpresent 22 | 23 | # Directories potentially created on remote AFP share 24 | .AppleDB 25 | .AppleDesktop 26 | Network Trash Folder 27 | Temporary Items 28 | .apdisk 29 | 30 | # C extensions 31 | *.so 32 | 33 | # Distribution / packaging 34 | .Python 35 | env/ 36 | venv/ 37 | build/ 38 | develop-eggs/ 39 | dist/ 40 | downloads/ 41 | eggs/ 42 | .eggs/ 43 | lib/ 44 | lib64/ 45 | parts/ 46 | sdist/ 47 | var/ 48 | *.egg-info/ 49 | .installed.cfg 50 | *.egg 51 | 52 | # PyInstaller 53 | # Usually these files are written by a python script from a template 54 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 55 | *.manifest 56 | *.spec 57 | 58 | # Installer logs 59 | pip-log.txt 60 | pip-delete-this-directory.txt 61 | 62 | # Unit test / coverage reports 63 | htmlcov/ 64 | .tox/ 65 | .coverage 66 | .coverage.* 67 | .cache 68 | nosetests.xml 69 | coverage.xml 70 | *,cover 71 | .hypothesis/ 72 | .pytest_cache/ 73 | 74 | # Translations 75 | *.mo 76 | *.pot 77 | 78 | # Django stuff: 79 | *.log 80 | 81 | # Sphinx documentation 82 | docs/_build/ 83 | 84 | # IntelliJ Idea 85 | .idea 86 | *.iml 87 | *.ipr 88 | *.iws 89 | 90 | # PyBuilder 91 | target/ 92 | 93 | # Cookiecutter 94 | output/ 95 | python_boilerplate/ 96 | cookiecutter-pypackage-env/ 97 | 98 | # vscode settings 99 | .history/ 100 | *.code-workspace 101 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/astral-sh/ruff-pre-commit 3 | # Ruff version. 4 | rev: v0.4.9 5 | hooks: 6 | # Run the linter. 7 | - id: ruff 8 | args: [ --fix ] 9 | # Run the formatter. 10 | - id: ruff-format 11 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | // Required - change /PATH/TO to the absolute path to ComfyUI. Windows e.g.: D:/My Folder/ComfyUI/ 3 | // This pulls in ComfyUI Python types for the extension. 4 | "python.analysis.extraPaths": [ 5 | "/PATH/TO/ComfyUI/", 6 | "/PATH/TO/ComfyUI/custom_nodes/" 7 | ], 8 | } 9 | -------------------------------------------------------------------------------- /Example.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scraed/LanPaint/62870f060ad42db64c28125d6b88aa02271fb2c6/Example.JPG -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include LICENSE 2 | include README.md 3 | 4 | recursive-exclude * __pycache__ 5 | recursive-exclude * *.py[co] 6 | 7 | recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif 8 | 9 | graft src/LanPaint/web 10 | -------------------------------------------------------------------------------- /Nodes.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scraed/LanPaint/62870f060ad42db64c28125d6b88aa02271fb2c6/Nodes.JPG -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LanPaint (Thinking mode Inpaint) 2 | 3 | Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior quality. 4 | ![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 5 | 6 | This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). 7 | 8 | ## Features 9 | 10 | - **Universal Compatibility** – Works instantly with almost any model (SD 1.5, XL, 3.5, Flux, HiDream, or custom LoRAs) and ControlNet. 11 | - **No Training Needed** – Works out of the box with your existing model. 12 | - **Easy to Use** – Same workflow as standard ComfyUI KSampler. 13 | - **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting. 14 | - **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising. 15 | - **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters. 16 | 17 | ## How It Works 18 | LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results. 19 | 20 | LanPaint introduces "BIG score" that creates a **two-way alignment** between masked and unmasked areas. It continuously evaluates: 21 | - *"Does the new content make sense with the existing elements?"* 22 | - *"Do the existing elements support the new creation?"* 23 | Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions. 24 | 25 | LanPaint also implements an accurate, robust, and fast Langevin dynamics solver. 26 | 27 | 28 | ## Quickstart 29 | 30 | 1. **Install ComfyUI**: Follow the official [ComfyUI installation guide](https://docs.comfy.org/get_started) to set up ComfyUI on your system. Or ensure your ComfyUI version > 0.3.11. 31 | 2. **Install ComfyUI-Manager**: Add the [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) for easy extension management. 32 | 3. **Install LanPaint Nodes**: 33 | - **Via ComfyUI-Manager**: Search for "[LanPaint](https://registry.comfy.org/publishers/scraed/nodes/LanPaint)" in the manager and install it directly. 34 | - **Manually**: Click "Install via Git URL" in ComfyUI-Manager and input the GitHub repository link: 35 | ``` 36 | https://github.com/scraed/LanPaint.git 37 | ``` 38 | Alternatively, clone this repository into the `ComfyUI/custom_nodes` folder. 39 | 4. **Restart ComfyUI**: Restart ComfyUI to load the LanPaint nodes. 40 | 41 | Once installed, you'll find the LanPaint nodes under the "sampling" category in ComfyUI. Use them just like the default KSampler for high-quality inpainting! 42 | 43 | 44 | 45 | ## Updates 46 | - 2025/06/04 47 | - Add more sampler support. 48 | - Add early stopping to advanced sampler. 49 | - 2025/05/28 50 | - Major update on the Langevin solver. It is now much faster and more stable. 51 | - Greatly simplified the parameters for advanced sampler. 52 | - Fix performance issue on Flux and SD 3.5 53 | - 2025/04/16 54 | - Added Primary HiDream support 55 | - 2025/03/22 56 | - Added Primary Flux support 57 | - Added Tease Mode 58 | - 2025/03/10 59 | - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability. 60 | 61 | ## Examples 62 | All examples use a random seed 0 to generate batch of 4 images for fair comparison. (Warning: Generating 4 images may exceed your GPU memory; adjust batch size as needed.) 63 | 64 | ### Example HiDream: InPaint(LanPaint K Sampler, 5 steps of thinking) 65 | ![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_11.jpg) 66 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8) 67 | 68 | You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model. 69 | 70 | ### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking) 71 | ![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_12.jpg) 72 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9) 73 | 74 | You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki.com/en/tutorial/advanced/stable-diffusion-3-5-comfyui-workflow) to download and install the model. 75 | 76 | ### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking) 77 | ![Inpainting Result 7](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_10.jpg) 78 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7) 79 | [Model Used in This Example](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors) 80 | (Note: Prompt First mode is disabled on Flux. As it does not use CFG guidance.) 81 | 82 | ### Example SDXL 0: Character Consistency (Side View Generation) (LanPaint K Sampler, 5 steps of thinking) 83 | ![Inpainting Result 6](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_09.jpg) 84 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_6) 85 | [Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658) 86 | 87 | (Tricks 1: You can emphasize the character by copy it's image multiple times with Photoshop. Here I have made one extra copy.) 88 | 89 | (Tricks 2: Use prompts like multiple views, multiple angles, clone, turnaround. Use LanPaint's Prompt first mode (does not support Flux)) 90 | 91 | (Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!) 92 | 93 | 94 | ### Example SDXL 1: Basket to Basket Ball (LanPaint K Sampler, 2 steps of thinking). 95 | ![Inpainting Result 1](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_04.jpg) 96 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_1) 97 | [Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658) 98 | ### Example SDXL 2: White Shirt to Blue Shirt (LanPaint K Sampler, 5 steps of thinking) 99 | ![Inpainting Result 2](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_05.jpg) 100 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_2) 101 | [Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658) 102 | ### Example SDXL 3: Smile to Sad (LanPaint K Sampler, 5 steps of thinking) 103 | ![Inpainting Result 3](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_06.jpg) 104 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_3) 105 | [Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl) 106 | ### Example SDXL 4: Damage Restoration (LanPaint K Sampler, 5 steps of thinking) 107 | ![Inpainting Result 4](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_07.jpg) 108 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_4) 109 | [Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl) 110 | ### Example SDXL 5: Huge Damage Restoration (LanPaint K Sampler, 20 steps of thinking) 111 | ![Inpainting Result 5](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_08.jpg) 112 | [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_5) 113 | [Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl) 114 | 115 | Check more for use cases like inpaint on [fine tuned models](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938662021) and [face swapping](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938723501), thanks to [Amazon90](https://github.com/Amazon90). 116 | 117 | 118 | ## **How to Use These Examples:** 119 | 1. Navigate to the **example** folder (i.e example_1) by clicking **View Workflow & Masks**, download all pictures. 120 | 2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow. 121 | 3. Download the required model from Civitai by clicking **Model Used in This Example**. 122 | 4. Load the model into the **"Load Checkpoint"** node. 123 | 5. Upload **Original_No_Mask.png** to the **"Load image"** node in the **"Original Image"** group (far left). 124 | 6. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left). 125 | 7. Queue the task, you will get inpainted results from three methods: 126 | - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)** (middle), 127 | - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)** (second from right), 128 | - **LanPaint** (far right). 129 | 8. You also get an output from "masked blend" node, which copy the original image and paste onto the unmasked part of output. It is useful if you want unmasked region to match original picture pixel perfectly. 130 | 131 | Compare and explore the results from each method! 132 | 133 | ![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG) 134 | 135 | 136 | ## Usage 137 | 138 | **Workflow Setup** 139 | Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes. The inpainting workflow is the same as the [SetLatentNoiseMask](https://comfyui-wiki.com/zh/comfyui-nodes/latent/inpaint/set-latent-noise-mask) inpainting workflow. 140 | 141 | **Note** 142 | - LanPaint requires binary masks (values of 0 or 1) without opacity or smoothing. To ensure compatibility, set the mask's **opacity and hardness to maximum** in your mask editor. During inpainting, any mask with smoothing or gradients will automatically be converted to a binary mask. 143 | - LanPaint relies heavily on your text prompts to guide inpainting - explicitly describe the content you want generated in the masked area. If results show artifacts or mismatched elements, counteract them with targeted negative prompts. 144 | 145 | ## Basic Sampler 146 | ![Samplers](https://github.com/scraed/LanPaint/blob/master/Nodes.JPG) 147 | 148 | - LanPaint KSampler: The most basic and easy to use sampler for inpainting. 149 | - LanPaint KSampler (Advanced): Full control of all parameters. 150 | 151 | ### LanPaint KSampler 152 | Simplified interface with recommended defaults: 153 | 154 | - Steps: 20 - 50. More steps will give more "thinking" and better results. 155 | - LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks ( which means 5 times slower than sampling without thinking). Use 10 for more challenging tasks. 156 | - LanPaint Prompt mode: Image First mode and Prompt First mode. Image First mode focuses on the image, inpaint based on image context (maybe ignore prompt), while Prompt First mode focuses more on the prompt. Use Prompt First mode for tasks like character consistency. (Technically, it Prompt First mode change CFG scale to negative value in the BIG score to emphasis prompt, which will costs image quality.) 157 | 158 | ### LanPaint KSampler (Advanced) 159 | Full parameter control: 160 | **Key Parameters** 161 | 162 | | Parameter | Range | Description | 163 | |-----------|-------|-------------| 164 | | `Steps` | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 20-50. | 165 | | `LanPaint_NumSteps` | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 2-5. Hard task: 5-10 | 166 | | `LanPaint_Lambda` | 0.1-50 | Content alignment strength (higher = stricter). Recommend 4.0 - 10.0 | 167 | | `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. | 168 | | `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 | 169 | | `LanPaint_Friction` | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0| 170 | | `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5| 171 | | `LanPaint_PromptMode` | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. | 172 | 173 | For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information. 174 | 175 | ### LanPaint Mask Blend 176 | This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly. 177 | 178 | ## LanPaint KSampler (Advanced) Tuning Guide 179 | For challenging inpainting tasks: 180 | 181 | 1️⃣ **Boost Quality** 182 | Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations. 183 | 184 | 2️⃣ **Boost Speed** 185 | If you want better results but still need fewer steps, consider: 186 | - **Increasing LanPaint_StepSize** to speed up the thinking process. 187 | - **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster. 188 | 189 | 3️⃣ **Fix Unstability**: 190 | If you find the results have wired texture, try 191 | - Reduce **LanPaint_Friction** to make the Langevin dynamics more stable. 192 | - Reduce **LanPaint_StepSize** to use smaller step size. 193 | - Reduce **LanPaint_Beta** if you are using a high lambda value. 194 | 195 | ⚠️ **Notes**: 196 | - For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting. Better to do it with a batche of images to avoid overfitting on a single image. 197 | 198 | ## ToDo 199 | - Try Implement Detailer 200 | - Provide inference code on without GUI. 201 | 202 | ## Contribute 203 | 204 | - 2025/03/06: Bug Fix for str not callable error and unpack error. Big thanks to [jamesWalker55](https://github.com/jamesWalker55) and [EricBCoding](https://github.com/EricBCoding). 205 | 206 | 207 | ## Citation 208 | 209 | ``` 210 | @misc{zheng2025lanpainttrainingfreediffusioninpainting, 211 | title={Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference}, 212 | author={Candi Zheng and Yuan Lan and Yang Wang}, 213 | year={2025}, 214 | eprint={2502.03491}, 215 | archivePrefix={arXiv}, 216 | primaryClass={eess.IV}, 217 | url={https://arxiv.org/abs/2502.03491}, 218 | } 219 | ``` 220 | 221 | 222 | 223 | 224 | 225 | 226 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | """Top-level package for LanPaint.""" 2 | 3 | __all__ = [ 4 | "NODE_CLASS_MAPPINGS", 5 | "NODE_DISPLAY_NAME_MAPPINGS", 6 | "WEB_DIRECTORY", 7 | ] 8 | 9 | __author__ = """LanPaint""" 10 | __email__ = "czhengac@connect.ust.hk" 11 | __version__ = "0.0.1" 12 | 13 | from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS 14 | from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS 15 | 16 | WEB_DIRECTORY = "./web" 17 | -------------------------------------------------------------------------------- /examples/Example_1/InPainted_Drag_Me_to_ComfyUI.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scraed/LanPaint/62870f060ad42db64c28125d6b88aa02271fb2c6/examples/Example_1/InPainted_Drag_Me_to_ComfyUI.png -------------------------------------------------------------------------------- /examples/Example_1/Masked_Load_Me_in_Loader.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scraed/LanPaint/62870f060ad42db64c28125d6b88aa02271fb2c6/examples/Example_1/Masked_Load_Me_in_Loader.png -------------------------------------------------------------------------------- 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[project.optional-dependencies] 20 | dev = [ 21 | "bump-my-version", 22 | "coverage", # testing 23 | "mypy", # linting 24 | "pre-commit", # runs linting on commit 25 | "pytest", # testing 26 | "ruff", # linting 27 | ] 28 | 29 | [project.urls] 30 | bugs = "https://github.com/scraed/LanPaint/issues" 31 | homepage = "https://github.com/scraed/LanPaint" 32 | Repository = "https://github.com/scraed/LanPaint" 33 | 34 | [tool.comfy] 35 | PublisherId = "scraed" 36 | DisplayName = "LanPaint" 37 | Icon = "" 38 | 39 | [tool.setuptools.package-data] 40 | "*" = ["*.*"] 41 | 42 | [tool.pytest.ini_options] 43 | minversion = "8.0" 44 | testpaths = [ 45 | "tests", 46 | ] 47 | 48 | [tool.mypy] 49 | files = "." 50 | 51 | # Use strict defaults 52 | strict = true 53 | warn_unreachable = true 54 | warn_no_return = true 55 | 56 | [[tool.mypy.overrides]] 57 | # Don't require test functions to include types 58 | module = "tests.*" 59 | allow_untyped_defs = true 60 | disable_error_code = "attr-defined" 61 | 62 | [tool.ruff] 63 | # extend-exclude = ["static", "ci/templates"] 64 | line-length = 140 65 | src = ["src", "tests"] 66 | target-version = "py39" 67 | 68 | # Add rules to ban exec/eval 69 | [tool.ruff.lint] 70 | select = [ 71 | "S102", # exec-builtin 72 | "S307", # eval-used 73 | "W293", 74 | "F", # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. 75 | # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f 76 | ] 77 | 78 | [tool.ruff.lint.flake8-quotes] 79 | inline-quotes = "double" 80 | -------------------------------------------------------------------------------- /src/LanPaint/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scraed/LanPaint/62870f060ad42db64c28125d6b88aa02271fb2c6/src/LanPaint/__init__.py -------------------------------------------------------------------------------- /src/LanPaint/lanpaint.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from .utils import * 3 | from functools import partial 4 | class LanPaint(): 5 | def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False): 6 | self.n_steps = NSteps 7 | self.chara_lamb = Lambda 8 | self.IS_FLUX = IS_FLUX 9 | self.IS_FLOW = IS_FLOW 10 | self.step_size = StepSize 11 | self.inner_model = Model 12 | self.friction = Friction 13 | self.chara_beta = Beta 14 | 15 | def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None): 16 | self.latent_image = latent_image 17 | self.noise = noise 18 | if n_steps is None: 19 | n_steps = self.n_steps 20 | return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW) 21 | def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW): 22 | VE_Sigma, abt, Flow_t = current_times 23 | 24 | 25 | step_size = self.step_size * (1 - abt) 26 | step_size = step_size[:, None, None, None] 27 | # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values 28 | # This is the replace step 29 | x = x * (1 - latent_mask) + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask 30 | 31 | if IS_FLUX or IS_FLOW: 32 | x_t = x * ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 ) 33 | else: 34 | x_t = x / ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values 35 | 36 | ############ LanPaint Iterations Start ############### 37 | # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region. 38 | args = None 39 | for i in range(n_steps): 40 | score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = abt[:, None,None,None], sigma = VE_Sigma[:, None,None,None], tflow = Flow_t[:, None,None,None], model_options = model_options, seed = seed ) 41 | x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.sigma_x(abt)[:, None,None,None], sigma_y = self.sigma_y(abt)[:, None,None,None], args = args) 42 | if IS_FLUX or IS_FLOW: 43 | x = x_t / ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 ) 44 | else: 45 | x = x_t * ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values 46 | ############ LanPaint Iterations End ############### 47 | # out is x_0 48 | out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed) 49 | out = out * (1-latent_mask) + self.latent_image * latent_mask 50 | return out 51 | 52 | def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed): 53 | 54 | lamb = self.chara_lamb 55 | 56 | if self.IS_FLUX or self.IS_FLOW: 57 | # compute t for flow model, with a small epsilon compensating for numerical error. 58 | x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching 59 | x_0, x_0_BIG = self.inner_model(x, tflow[:, 0,0,0], model_options=model_options, seed=seed) 60 | else: 61 | x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding 62 | x_0, x_0_BIG = self.inner_model(x, sigma[:, 0,0,0], model_options=model_options, seed=seed) 63 | 64 | score_x = -(x_t - x_0) 65 | score_y = - (1 + lamb) * ( x_t - y ) + lamb * (x_t - x_0_BIG) 66 | return score_x * (1 - mask) + score_y * mask 67 | def sigma_x(self, abt): 68 | # the time scale for the x_t update 69 | return abt**0 70 | def sigma_y(self, abt): 71 | beta = self.chara_beta * abt ** 0 72 | return beta 73 | 74 | def langevin_dynamics(self, x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None): 75 | # prepare the step size and time parameters 76 | with torch.autocast(device_type=x_t.device.type, dtype=torch.float32): 77 | step_sizes = self.prepare_step_size(current_times, step_size, sigma_x, sigma_y) 78 | sigma, abt, dtx, dty, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y = step_sizes 79 | # print('mask',mask.device) 80 | if torch.mean(dtx) <= 0.: 81 | return x_t, args 82 | # ------------------------------------------------------------------------- 83 | # Compute the Langevin dynamics update in variance perserving notation 84 | # ------------------------------------------------------------------------- 85 | x0 = self.x0_evalutation(x_t, score, sigma, args) 86 | C = abt**0.5 * x0 / (1-abt) 87 | A = A_x * (1-mask) + A_y * mask 88 | D = D_x * (1-mask) + D_y * mask 89 | dt = dtx * (1-mask) + dty * mask 90 | Gamma = Gamma_x * (1-mask) + Gamma_y * mask 91 | 92 | 93 | 94 | if args is None: 95 | #v = torch.zeros_like(x_t) 96 | v = None 97 | else: 98 | v, = args 99 | 100 | with torch.autocast(device_type=x_t.device.type, dtype=torch.float32): 101 | osc = StochasticHarmonicOscillator(Gamma, A, C, D ) 102 | x_t, v = osc.dynamics(x_t, v, dt ) 103 | 104 | return x_t, (v,) 105 | 106 | def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y): 107 | # ------------------------------------------------------------------------- 108 | # Unpack current times parameters (sigma and abt) 109 | sigma, abt, flow_t = current_times 110 | sigma = sigma[:, None,None,None] 111 | abt = abt[:, None,None,None] 112 | # Compute time step (dtx, dty) for x and y branches. 113 | dtx = 2 * step_size * sigma_x 114 | dty = 2 * step_size * sigma_y 115 | 116 | # ------------------------------------------------------------------------- 117 | # Define friction parameter Gamma_hat for each branch. 118 | # Using dtx**0 provides a tensor of the proper device/dtype. 119 | 120 | Gamma_hat_x = self.friction **2 * self.step_size * sigma_x / 0.1 * sigma**0 121 | Gamma_hat_y = self.friction **2 * self.step_size * sigma_y / 0.1 * sigma**0 122 | #print("Gamma_hat_x", torch.mean(Gamma_hat_x).item(), "Gamma_hat_y", torch.mean(Gamma_hat_y).item()) 123 | # adjust dt to match denoise-addnoise steps sizes 124 | Gamma_hat_x /= 2. 125 | Gamma_hat_y /= 2. 126 | 127 | A_t_x = (1) / ( 1 - abt ) * dtx / 2 128 | A_t_y = (1) / ( 1 - abt ) * dty / 2 129 | 130 | A_x = A_t_x / (dtx/2) 131 | A_y = A_t_y / (dty/2) 132 | Gamma_x = Gamma_hat_x / (dtx/2) 133 | Gamma_y = Gamma_hat_y / (dty/2) 134 | 135 | #D_x = (2 * (1 + sigma**2) )**0.5 136 | #D_y = (2 * (1 + sigma**2) )**0.5 137 | D_x = (2 * abt**0 )**0.5 138 | D_y = (2 * abt**0 )**0.5 139 | return sigma, abt, dtx/2, dty/2, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y 140 | 141 | 142 | 143 | def x0_evalutation(self, x_t, score, sigma, args): 144 | x0 = x_t + score(x_t) 145 | return x0 -------------------------------------------------------------------------------- /src/LanPaint/nodes.py: -------------------------------------------------------------------------------- 1 | from contextlib import contextmanager 2 | from inspect import cleandoc 3 | import inspect 4 | # import nodes.py 5 | import comfy 6 | import nodes 7 | import latent_preview 8 | from functools import partial 9 | from comfy.utils import repeat_to_batch_size 10 | from comfy.samplers import * 11 | from comfy.model_base import ModelType 12 | from .utils import * 13 | from .lanpaint import LanPaint 14 | # Monkey patch comfy.samplers module by importing with absolute package path 15 | #exec(inspect.getsource(comfy.samplers).replace("from .", "from comfy.")) 16 | 17 | def reshape_mask(input_mask, output_shape): 18 | dims = len(output_shape) - 2 19 | 20 | 21 | scale_mode = "nearest-exact" 22 | mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode) 23 | if mask.shape[1] < output_shape[1]: 24 | mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]] 25 | mask = repeat_to_batch_size(mask, output_shape[0]) 26 | return mask 27 | def prepare_mask(noise_mask, shape, device): 28 | return reshape_mask(noise_mask, shape).to(device) 29 | def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, cond_scale_BIG, model_options={}, seed=None): 30 | if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: 31 | uncond_ = None 32 | else: 33 | uncond_ = uncond 34 | 35 | conds = [cond, uncond_] 36 | out = calc_cond_batch(model, conds, x, timestep, model_options) 37 | 38 | for fn in model_options.get("sampler_pre_cfg_function", []): 39 | args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, 40 | "input": x, "sigma": timestep, "model": model, "model_options": model_options} 41 | out = fn(args) 42 | 43 | return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_), cfg_function(model, out[0], out[1], cond_scale_BIG, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) 44 | 45 | 46 | class CFGGuider_LanPaint: 47 | def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): 48 | print("CFGGuider outer_sample") 49 | self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) 50 | device = self.model_patcher.load_device 51 | 52 | if denoise_mask is not None: 53 | denoise_mask = prepare_mask(denoise_mask, noise.shape, device) 54 | 55 | noise = noise.to(device) 56 | latent_image = latent_image.to(device) 57 | sigmas = sigmas.to(device) 58 | cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) 59 | 60 | try: 61 | self.model_patcher.pre_run() 62 | output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) 63 | finally: 64 | self.model_patcher.cleanup() 65 | 66 | comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) 67 | del self.inner_model 68 | del self.loaded_models 69 | return output 70 | def predict_noise(self, x, timestep, model_options={}, seed=None): 71 | return sampling_function_LanPaint(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, self.cfg_BIG, model_options=model_options, seed=seed) 72 | 73 | #CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample 74 | #CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise 75 | 76 | class KSamplerX0Inpaint: 77 | def __init__(self, model, sigmas): 78 | self.inner_model = model 79 | self.sigmas = sigmas 80 | self.model_sigmas = torch.cat( (torch.tensor([0.], device = sigmas.device) , torch.tensor( self.inner_model.model_patcher.get_model_object("model_sampling").sigmas, device = sigmas.device) ) ) 81 | self.model_sigmas = torch.tensor( self.model_sigmas, dtype = self.sigmas.dtype ) 82 | def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None,**kwargs): 83 | ### For 1.5 and XL model 84 | # x is x_t in the notation of variance exploding diffusion model, x_t = x_0 + sigma * noise 85 | # sigma is the noise level 86 | ### For flux model 87 | # x is rectified flow x_t = sigma * noise + (1.0 - sigma) * x_0 88 | 89 | IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX 90 | IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW 91 | 92 | # unify the notations into variance exploding diffusion model 93 | if IS_FLUX or IS_FLOW: 94 | Flow_t = sigma 95 | abt = (1 - Flow_t)**2 / ((1 - Flow_t)**2 + Flow_t**2 ) 96 | VE_Sigma = Flow_t / (1 - Flow_t) 97 | #print("t", torch.mean( sigma ).item(), "VE_Sigma", torch.mean( VE_Sigma ).item()) 98 | 99 | 100 | else: 101 | VE_Sigma = sigma 102 | abt = 1/( 1+VE_Sigma**2 ) 103 | Flow_t = (1-abt)**0.5 / ( (1-abt)**0.5 + abt**0.5 ) 104 | 105 | if denoise_mask is not None: 106 | if "denoise_mask_function" in model_options: 107 | denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) 108 | 109 | denoise_mask = (denoise_mask > 0.5).float() 110 | 111 | latent_mask = 1 - denoise_mask 112 | current_times = (VE_Sigma, abt, Flow_t) 113 | 114 | current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) ) 115 | total_steps = len(self.sigmas)-1 116 | 117 | if total_steps - current_step <= self.LanPaint_early_stop: 118 | out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed, n_steps=0) 119 | else: 120 | out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed) 121 | else: 122 | out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed) 123 | 124 | # Add TAESD preview support - directly use the latent_preview module 125 | current_step = model_options.get("i", kwargs.get("i", 0)) 126 | total_steps = model_options.get("total_steps", 0) 127 | 128 | # Only show preview every few steps to improve performance 129 | if current_step % 2 == 0: 130 | # Directly call the preview callback if it exists 131 | callback = model_options.get("callback", None) 132 | if callback is not None: 133 | callback({"i": current_step, "denoised": out, "x": x}) 134 | 135 | return out 136 | 137 | # Custom sampler class extending ComfyUI's KSAMPLER for LanPaint 138 | class KSAMPLER(comfy.samplers.KSAMPLER): 139 | def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): 140 | #noise here is a randn noise from comfy.sample.prepare_noise 141 | #latent_image is the latent image as input of the KSampler node. For inpainting, it is the masked latent image. Otherwise it is zero tensor. 142 | extra_args["denoise_mask"] = denoise_mask 143 | model_k = KSamplerX0Inpaint(model_wrap, sigmas) 144 | model_k.latent_image = latent_image 145 | if self.inpaint_options.get("random", False): #TODO: Should this be the default? 146 | generator = torch.manual_seed(extra_args.get("seed", 41) + 1) 147 | model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) 148 | else: 149 | model_k.noise = noise 150 | 151 | IS_FLUX = model_wrap.inner_model.model_type == ModelType.FLUX 152 | IS_FLOW = model_wrap.inner_model.model_type == ModelType.FLOW 153 | # unify the notations into variance exploding diffusion model 154 | if IS_FLUX: 155 | model_wrap.cfg_BIG = 1.0 156 | else: 157 | model_wrap.cfg_BIG = model_wrap.model_patcher.LanPaint_cfg_BIG 158 | noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) 159 | 160 | model_k.PaintMethod = LanPaint(model_k.inner_model, 161 | model_wrap.model_patcher.LanPaint_NumSteps, 162 | model_wrap.model_patcher.LanPaint_Friction, 163 | model_wrap.model_patcher.LanPaint_Lambda, 164 | model_wrap.model_patcher.LanPaint_Beta, 165 | model_wrap.model_patcher.LanPaint_StepSize, 166 | IS_FLUX = IS_FLUX, 167 | IS_FLOW = IS_FLOW) 168 | model_k.LanPaint_early_stop = model_wrap.model_patcher.LanPaint_EarlyStop 169 | #if not inpainting, after noise_scaling, noise = noise * sigma, which is the noise added to the clean latent image in the variance exploding diffusion model notation. 170 | #if inpainting, after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region. 171 | k_callback = None 172 | total_steps = len(sigmas) - 1 173 | if callback is not None: 174 | k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) 175 | #print("LanPaint KSampler call sampler_function", self.sampler_function) 176 | # The main loop! 177 | #print("##########") 178 | #print("Sampling with ", self.sampler_function) 179 | #print("##########") 180 | samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) 181 | #print("LanPaint KSampler end sampler_function") 182 | samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) 183 | return samples 184 | 185 | @contextmanager 186 | def override_sample_function(): 187 | original_outer_sample = comfy.samplers.CFGGuider.outer_sample 188 | comfy.samplers.CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample 189 | 190 | original_predict_noise = comfy.samplers.CFGGuider.predict_noise 191 | comfy.samplers.CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise 192 | 193 | original_sample = comfy.samplers.KSAMPLER.sample 194 | comfy.samplers.KSAMPLER.sample = KSAMPLER.sample 195 | 196 | try: 197 | yield 198 | finally: 199 | comfy.samplers.KSAMPLER.sample = original_sample 200 | comfy.samplers.CFGGuider.predict_noise = original_predict_noise 201 | comfy.samplers.CFGGuider.outer_sample = original_outer_sample 202 | 203 | 204 | class LanPaint_UpSale_LatentNoiseMask: 205 | @classmethod 206 | def INPUT_TYPES(s): 207 | return {"required": { "samples": ("LATENT",), 208 | "scale": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1}), 209 | }} 210 | RETURN_TYPES = ("LATENT",) 211 | FUNCTION = "set_mask" 212 | 213 | 214 | CATEGORY = "latent/inpaint" 215 | 216 | def set_mask(self, samples, scale): 217 | s = samples.copy() 218 | samples = s['samples'] 219 | # generate a mask with every scaleth pixel set to 1 220 | mask = torch.zeros(samples.shape[0], 1, samples.shape[2], samples.shape[3], device=samples.device) + 1 221 | mask[:, :, ::scale, ::scale] = 0 222 | s["noise_mask"] = mask 223 | return (s,) 224 | 225 | #KSAMPLER_NAMES = ["euler", "dpmpp_2m", "uni_pc"] 226 | KSAMPLER_NAMES = ["euler","euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", 227 | "dpm_fast", "dpmpp_sde", "dpmpp_sde_gpu", 228 | "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", 229 | "deis", "res_multistep", "res_multistep_ancestral", 230 | "gradient_estimation", "er_sde", "seeds_2", "seeds_3"] 231 | 232 | class LanPaint_KSampler(): 233 | @classmethod 234 | def INPUT_TYPES(s): 235 | return { 236 | "required": { 237 | "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), 238 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}), 239 | "steps": ("INT", {"default": 30, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), 240 | "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), 241 | "sampler_name": (KSAMPLER_NAMES, {"tooltip": "Recommended: euler."}), 242 | "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras", "tooltip": "The scheduler controls how noise is gradually removed to form the image."}), 243 | "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}), 244 | "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}), 245 | "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}), 246 | "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}), 247 | "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}), 248 | "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}), 249 | "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}), 250 | } 251 | } 252 | 253 | RETURN_TYPES = ("LATENT",) 254 | OUTPUT_TOOLTIPS = ("The denoised latent.",) 255 | FUNCTION = "sample" 256 | 257 | CATEGORY = "sampling" 258 | DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image." 259 | 260 | def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, LanPaint_NumSteps=5, LanPaint_PromptMode = "Image First", LanPaint_Info=""): 261 | 262 | model.LanPaint_StepSize = 0.15 263 | model.LanPaint_Lambda = 8.0 264 | model.LanPaint_Beta = 1.0 265 | model.LanPaint_NumSteps = LanPaint_NumSteps 266 | model.LanPaint_Friction = 15. 267 | model.LanPaint_EarlyStop = 1 268 | if LanPaint_PromptMode == "Image First": 269 | model.LanPaint_cfg_BIG = cfg 270 | else: 271 | model.LanPaint_cfg_BIG = 0*cfg - 0.5 272 | with override_sample_function(): 273 | return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) 274 | class LanPaint_KSamplerAdvanced: 275 | @classmethod 276 | def INPUT_TYPES(s): 277 | return {"required": 278 | {"model": ("MODEL",), 279 | "add_noise": (["enable", "disable"], ), 280 | "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), 281 | "steps": ("INT", {"default": 30, "min": 1, "max": 10000}), 282 | "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), 283 | "sampler_name": (KSAMPLER_NAMES, ), 284 | "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), 285 | "positive": ("CONDITIONING", ), 286 | "negative": ("CONDITIONING", ), 287 | "latent_image": ("LATENT", ), 288 | "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), 289 | "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), 290 | "return_with_leftover_noise": (["disable", "enable"], ), 291 | "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}), 292 | "LanPaint_Lambda": ("FLOAT", {"default": 8., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}), 293 | "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}), 294 | "LanPaint_Beta": ("FLOAT", {"default": 1., "min": 0.0001, "max": 5, "step": 0.1, "round": 0.1, "tooltip": "The step size ratio between masked / unmasked regions. Lower value can compensate high values of LanPaint_Lambda."}), 295 | "LanPaint_Friction": ("FLOAT", {"default": 15, "min": 0., "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The friction parameter for fast langevin, lower values result in faster convergence but may be unstable."}), 296 | "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}), 297 | "LanPaint_EarlyStop": ("INT", {"default": 1, "min": 0, "max": 10000, "tooltip": "The number of steps to stop the LanPaint early, useful for preventing the image from irregular patterns."}), 298 | "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler Adv. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}), 299 | }, 300 | } 301 | 302 | RETURN_TYPES = ("LATENT",) 303 | FUNCTION = "sample" 304 | 305 | CATEGORY = "sampling" 306 | 307 | def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, LanPaint_StepSize=0.05, LanPaint_Lambda=5, LanPaint_Beta=1, LanPaint_NumSteps=5, LanPaint_Friction=5, LanPaint_PromptMode = "Image First", LanPaint_EarlyStop = 1, LanPaint_Info=""): 308 | force_full_denoise = True 309 | if return_with_leftover_noise == "enable": 310 | force_full_denoise = False 311 | disable_noise = False 312 | if add_noise == "disable": 313 | disable_noise = True 314 | model.LanPaint_StepSize = LanPaint_StepSize 315 | model.LanPaint_Lambda = LanPaint_Lambda 316 | model.LanPaint_Beta = LanPaint_Beta 317 | model.LanPaint_NumSteps = LanPaint_NumSteps 318 | model.LanPaint_Friction = LanPaint_Friction 319 | model.LanPaint_EarlyStop = LanPaint_EarlyStop 320 | if LanPaint_PromptMode == "Image First": 321 | model.LanPaint_cfg_BIG = cfg 322 | else: 323 | model.LanPaint_cfg_BIG = 0*cfg - 0.5 324 | 325 | with override_sample_function(): 326 | return nodes.common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) 327 | 328 | 329 | class MaskBlend: 330 | def __init__(self): 331 | pass 332 | 333 | @classmethod 334 | def INPUT_TYPES(s): 335 | return { 336 | "required": { 337 | "image1": ("IMAGE",), 338 | "image2": ("IMAGE",), 339 | "mask": ("MASK",), 340 | "blend_overlap": ("INT", {"default": 1, "min": 1, "max": 51, "step": 2, "tooltip": "The number of pixels to blend between the two images."}) 341 | }, 342 | } 343 | 344 | RETURN_TYPES = ("IMAGE",) 345 | FUNCTION = "blend_images" 346 | 347 | CATEGORY = "image/postprocessing" 348 | 349 | def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, mask: torch.Tensor, blend_overlap: int): 350 | # smooth the binary 01 mask, keep 1 still 1, but smooth the transition from 1 to 0 351 | # for each mask pixel, find out the nearest 1 pixel, and set the mask value to the distance between the two pixels 352 | mask = mask.float() 353 | mask = torch.nn.functional.max_pool2d(mask, kernel_size=blend_overlap, stride=1, padding=blend_overlap//2) 354 | # apply Gaussian blur with kernel size blend_overlap 355 | kernel = self.gaussian_kernel(blend_overlap) 356 | kernel = kernel.to(image1.device) 357 | kernel = kernel[None, None, ...] 358 | 359 | mask = torch.nn.functional.conv2d(mask[:,None,:,:], kernel, padding=blend_overlap//2)[:,0,:,:] 360 | 361 | 362 | blended_image = image1 * (1 - mask[...,None]) + image2 * mask[...,None] 363 | return (blended_image,) 364 | def gaussian_kernel(self,kernel_size): 365 | """ 366 | Creates a 2D Gaussian kernel with the given size and standard deviation (sigma). 367 | """ 368 | sigma = (kernel_size - 1)/4 369 | # Create a grid of (x, y) coordinates 370 | x = torch.arange(kernel_size).float() - kernel_size // 2 371 | y = torch.arange(kernel_size).float() - kernel_size // 2 372 | x_grid, y_grid = torch.meshgrid(x, y, indexing='ij') 373 | 374 | # Compute the Gaussian function 375 | kernel = torch.exp(-(x_grid ** 2 + y_grid ** 2) / (2 * sigma ** 2)) 376 | kernel = kernel / kernel.sum() # Normalize the kernel 377 | 378 | return kernel 379 | 380 | 381 | # A dictionary that contains all nodes you want to export with their names 382 | # NOTE: names should be globally unique 383 | NODE_CLASS_MAPPINGS = { 384 | "LanPaint_KSampler": LanPaint_KSampler, 385 | "LanPaint_KSamplerAdvanced": LanPaint_KSamplerAdvanced, 386 | "LanPaint_MaskBlend": MaskBlend, 387 | # "LanPaint_UpSale_LatentNoiseMask": LanPaint_UpSale_LatentNoiseMask, 388 | } 389 | 390 | # A dictionary that contains the friendly/humanly readable titles for the nodes 391 | NODE_DISPLAY_NAME_MAPPINGS = { 392 | "LanPaint_KSampler": "LanPaint KSampler", 393 | "LanPaint_KSamplerAdvanced": "LanPaint KSampler (Advanced)", 394 | "LanPaint_MaskBlend": "LanPaint Mask Blend", 395 | # "LanPaint_UpSale_LatentNoiseMask": "LanPaint UpSale Latent Noise Mask" 396 | } 397 | -------------------------------------------------------------------------------- /src/LanPaint/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | def epxm1_x(x): 3 | # Compute the (exp(x) - 1) / x term with a small value to avoid division by zero. 4 | result = torch.special.expm1(x) / x 5 | # replace NaN or inf values with 0 6 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 7 | mask = torch.abs(x) < 1e-2 8 | result = torch.where(mask, 1 + x/2. + x**2 / 6., result) 9 | return result 10 | def epxm1mx_x2(x): 11 | # Compute the (exp(x) - 1 - x) / x**2 term with a small value to avoid division by zero. 12 | result = (torch.special.expm1(x) - x) / x**2 13 | # replace NaN or inf values with 0 14 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 15 | mask = torch.abs(x**2) < 1e-2 16 | result = torch.where(mask, 1/2. + x/6 + x**2 / 24 + x**3 / 120, result) 17 | return result 18 | 19 | def expm1mxmhx2_x3(x): 20 | # Compute the (exp(x) - 1 - x - x**2 / 2) / x**3 term with a small value to avoid division by zero. 21 | result = (torch.special.expm1(x) - x - x**2 / 2) / x**3 22 | # replace NaN or inf values with 0 23 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 24 | mask = torch.abs(x**3) < 1e-2 25 | result = torch.where(mask, 1/6 + x/24 + x**2 / 120 + x**3 / 720 + x**4 / 5040, result) 26 | return result 27 | 28 | def exp_1mcosh_GD(gamma_t, delta): 29 | """ 30 | Compute e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ ) 31 | 32 | Parameters: 33 | gamma_t: Γ*t term (could be a scalar or tensor) 34 | delta: Δ term (could be a scalar or tensor) 35 | 36 | Returns: 37 | Result of the computation with numerical stability handling 38 | """ 39 | # Main computation 40 | is_positive = delta > 0 41 | sqrt_abs_delta = torch.sqrt(torch.abs(delta)) 42 | gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta 43 | numerator_pos = torch.exp(-gamma_t) - (torch.exp(gamma_t * (sqrt_abs_delta - 1)) + torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2 44 | numerator_neg = torch.exp(-gamma_t) * ( 1 - torch.cos(gamma_t * sqrt_abs_delta ) ) 45 | numerator = torch.where(is_positive, numerator_pos, numerator_neg) 46 | result = numerator / (delta * gamma_t**2 ) 47 | # Handle NaN/inf cases 48 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 49 | # Handle numerical instability for small delta 50 | mask = torch.abs(gamma_t_sqrt_delta**2) < 5e-2 51 | taylor = ( -0.5 - gamma_t**2 / 24 * delta - gamma_t**4 / 720 * delta**2 ) * torch.exp(-gamma_t) 52 | result = torch.where(mask, taylor, result) 53 | return result 54 | 55 | def exp_sinh_GsqrtD(gamma_t, delta): 56 | """ 57 | Compute e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ) 58 | 59 | Parameters: 60 | gamma_t: Γ*t term (could be a scalar or tensor) 61 | delta: Δ term (could be a scalar or tensor) 62 | 63 | Returns: 64 | Result of the computation with numerical stability handling 65 | """ 66 | # Main computation 67 | is_positive = delta > 0 68 | sqrt_abs_delta = torch.sqrt(torch.abs(delta)) 69 | gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta 70 | numerator_pos = (torch.exp(gamma_t * (sqrt_abs_delta - 1)) - torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2 71 | denominator_pos = gamma_t_sqrt_delta 72 | result_pos = numerator_pos / gamma_t_sqrt_delta 73 | result_pos = torch.where(torch.isfinite(result_pos), result_pos, torch.zeros_like(result_pos)) 74 | 75 | # Taylor expansion for small gamma_t_sqrt_delta 76 | mask = torch.abs(gamma_t_sqrt_delta) < 1e-2 77 | taylor = ( 1 + gamma_t**2 / 6 * delta + gamma_t**4 / 120 * delta**2 ) * torch.exp(-gamma_t) 78 | result_pos = torch.where(mask, taylor, result_pos) 79 | 80 | # Handle negative delta 81 | result_neg = torch.exp(-gamma_t) * torch.special.sinc(gamma_t_sqrt_delta/torch.pi) 82 | result = torch.where(is_positive, result_pos, result_neg) 83 | return result 84 | 85 | def exp_cosh(gamma_t, delta): 86 | """ 87 | Compute e^(-Γt) * cosh(Γt√Δ) 88 | 89 | Parameters: 90 | gamma_t: Γ*t term (could be a scalar or tensor) 91 | delta: Δ term (could be a scalar or tensor) 92 | 93 | Returns: 94 | Result of the computation with numerical stability handling 95 | """ 96 | exp_1mcosh_GD_result = exp_1mcosh_GD(gamma_t, delta) # e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ ) 97 | result = torch.exp(-gamma_t) - gamma_t**2 * delta * exp_1mcosh_GD_result 98 | return result 99 | def exp_sinh_sqrtD(gamma_t, delta): 100 | """ 101 | Compute e^(-Γt) * sinh(Γt√Δ) / √Δ 102 | Parameters: 103 | gamma_t: Γ*t term (could be a scalar or tensor) 104 | delta: Δ term (could be a scalar or tensor) 105 | Returns: 106 | Result of the computation with numerical stability handling 107 | """ 108 | exp_sinh_GsqrtD_result = exp_sinh_GsqrtD(gamma_t, delta) # e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ) 109 | result = gamma_t * exp_sinh_GsqrtD_result 110 | return result 111 | 112 | 113 | 114 | def zeta1(gamma_t, delta): 115 | # Compute hyperbolic terms and exponential 116 | half_gamma_t = gamma_t / 2 117 | exp_cosh_term = exp_cosh(half_gamma_t, delta) 118 | exp_sinh_term = exp_sinh_sqrtD(half_gamma_t, delta) 119 | 120 | 121 | # Main computation 122 | numerator = 1 - (exp_cosh_term + exp_sinh_term) 123 | denominator = gamma_t * (1 - delta) / 4 124 | result = 1 - numerator / denominator 125 | 126 | # Handle numerical instability 127 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 128 | 129 | # Taylor expansion for small x (similar to your epxm1Dx approach) 130 | mask = torch.abs(denominator) < 5e-3 131 | term1 = epxm1_x(-gamma_t) 132 | term2 = epxm1mx_x2(-gamma_t) 133 | term3 = expm1mxmhx2_x3(-gamma_t) 134 | taylor = term1 + (1/2.+ term1-3*term2)*denominator + (-1/6. + term1/2 - 4 * term2 + 10 * term3) * denominator**2 135 | result = torch.where(mask, taylor, result) 136 | 137 | return result 138 | 139 | def exp_cosh_minus_terms(gamma_t, delta): 140 | """ 141 | Compute E^(-tΓ) * (Cosh[tΓ] - 1 - (Cosh[tΓ√Δ] - 1)/Δ) / (tΓ(1 - Δ)) 142 | 143 | Parameters: 144 | gamma_t: Γ*t term (could be a scalar or tensor) 145 | delta: Δ term (could be a scalar or tensor) 146 | 147 | Returns: 148 | Result of the computation with numerical stability handling 149 | """ 150 | exp_term = torch.exp(-gamma_t) 151 | # Compute individual terms 152 | exp_cosh_term = exp_cosh(gamma_t, gamma_t**0) - exp_term # E^(-tΓ) (Cosh[tΓ] - 1) term 153 | exp_cosh_delta_term = - gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) # E^(-tΓ) (Cosh[tΓ√Δ] - 1)/Δ term 154 | 155 | #exp_1mcosh_GD e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ ) 156 | # Main computation 157 | numerator = exp_cosh_term - exp_cosh_delta_term 158 | denominator = gamma_t * (1 - delta) 159 | 160 | result = numerator / denominator 161 | 162 | # Handle numerical instability 163 | result = torch.where(torch.isfinite(result), result, torch.zeros_like(result)) 164 | 165 | # Taylor expansion for small gamma_t and delta near 1 166 | mask = (torch.abs(denominator) < 1e-1) 167 | exp_1mcosh_GD_term = exp_1mcosh_GD(gamma_t, delta**0) 168 | taylor = ( 169 | gamma_t*exp_1mcosh_GD_term + 0.5 * gamma_t * exp_sinh_GsqrtD(gamma_t, delta**0) 170 | - denominator / 4 * ( 0.5 * exp_cosh(gamma_t, delta**0) - 4 * exp_1mcosh_GD_term - 5 /2 * exp_sinh_GsqrtD(gamma_t, delta**0) ) 171 | ) 172 | result = torch.where(mask, taylor, result) 173 | 174 | return result 175 | 176 | 177 | def zeta2(gamma_t, delta): 178 | half_gamma_t = gamma_t / 2 179 | return exp_sinh_GsqrtD(half_gamma_t, delta) 180 | 181 | def sig11(gamma_t, delta): 182 | return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta) 183 | 184 | 185 | def Zcoefs(gamma_t, delta): 186 | Zeta1 = zeta1(gamma_t, delta) 187 | Zeta2 = zeta2(gamma_t, delta) 188 | 189 | sq_total = 1 - Zeta1 + gamma_t * (delta - 1) * (Zeta1 - 1)**2 / 8 190 | amplitude = torch.sqrt(sq_total) 191 | Zcoef1 = ( gamma_t**0.5 * Zeta2 / 2 **0.5 ) / amplitude 192 | Zcoef2 = Zcoef1 * gamma_t *( - 2 * exp_1mcosh_GD(gamma_t, delta) / sig11(gamma_t, delta) ) ** 0.5 193 | #cterm = exp_cosh_minus_terms(gamma_t, delta) 194 | #sterm = exp_sinh_sqrtD(gamma_t, delta**0) + exp_sinh_sqrtD(gamma_t, delta) 195 | #Zcoef3 = 2 * torch.sqrt( cterm / ( gamma_t * (1 - delta) * cterm + sterm ) ) 196 | Zcoef3 = torch.sqrt( torch.maximum(1 - Zcoef1**2 - Zcoef2**2, sq_total.new_zeros(sq_total.shape)) ) 197 | 198 | return Zcoef1 * amplitude, Zcoef2 * amplitude, Zcoef3 * amplitude, amplitude 199 | 200 | def Zcoefs_asymp(gamma_t, delta): 201 | A_t = (gamma_t * (1 - delta) )/4 202 | return epxm1_x(- 2 * A_t) 203 | 204 | class StochasticHarmonicOscillator: 205 | """ 206 | Simulates a stochastic harmonic oscillator governed by the equations: 207 | dy(t) = q(t) dt 208 | dq(t) = -Γ A y(t) dt + Γ C dt + Γ D dw(t) - Γ q(t) dt 209 | 210 | Also define v(t) = q(t) / √Γ, which is numerically more stable. 211 | 212 | Where: 213 | y(t) - Position variable 214 | q(t) - Velocity variable 215 | Γ - Damping coefficient 216 | A - Harmonic potential strength 217 | C - Constant force term 218 | D - Noise amplitude 219 | dw(t) - Wiener process (Brownian motion) 220 | """ 221 | def __init__(self, Gamma, A, C, D): 222 | self.Gamma = Gamma 223 | self.A = A 224 | self.C = C 225 | self.D = D 226 | self.Delta = 1 - 4 * A / Gamma 227 | def sig11(self, gamma_t, delta): 228 | return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta) 229 | def sig22(self, gamma_t, delta): 230 | return 1- zeta1(2*gamma_t, delta) + 2 * gamma_t * exp_1mcosh_GD(gamma_t, delta) 231 | def dynamics(self, y0, v0, t): 232 | """ 233 | Calculates the position and velocity variables at time t. 234 | 235 | Parameters: 236 | y0 (float): Initial position 237 | v0 (float): Initial velocity v(0) = q(0) / √Γ 238 | t (float): Time at which to evaluate the dynamics 239 | Returns: 240 | tuple: (y(t), v(t)) 241 | """ 242 | 243 | 244 | dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0 245 | Delta = self.Delta + dummyzero 246 | Gamma_hat = self.Gamma * t + dummyzero 247 | A = self.A + dummyzero 248 | C = self.C + dummyzero 249 | D = self.D + dummyzero 250 | Gamma = self.Gamma + dummyzero 251 | zeta_1 = zeta1( Gamma_hat, Delta) 252 | zeta_2 = zeta2( Gamma_hat, Delta) 253 | EE = 1 - Gamma_hat * zeta_2 254 | 255 | if v0 is None: 256 | #v0 = torch.randn_like(y0) * D / 2 ** 0.5 257 | v0 = (C - A * y0)/Gamma**0.5 258 | 259 | # Calculate mean position and velocity 260 | term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t 261 | y_mean = term1 + y0 262 | v_mean = (1 - EE)*(C - A * y0) / (Gamma ** 0.5) + (EE - A * t * (1 - zeta_1)) * v0 263 | 264 | cov_yy = D**2 * t * self.sig22(Gamma_hat, Delta) 265 | cov_vv = D**2 * self.sig11(Gamma_hat, Delta) / 2 266 | cov_yv = (zeta2(Gamma_hat, Delta) * Gamma_hat * D ) **2 / 2 / (Gamma ** 0.5) 267 | 268 | # sample new position and velocity with multivariate normal distribution 269 | 270 | batch_shape = y0.shape 271 | cov_matrix = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype) 272 | cov_matrix[..., 0, 0] = cov_yy 273 | cov_matrix[..., 0, 1] = cov_yv 274 | cov_matrix[..., 1, 0] = cov_yv # symmetric 275 | cov_matrix[..., 1, 1] = cov_vv 276 | 277 | # Sample correlated noise from multivariate normal 278 | mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype) 279 | mean[..., 0] = y_mean 280 | mean[..., 1] = v_mean 281 | new_yv = torch.distributions.MultivariateNormal( 282 | loc=mean, 283 | covariance_matrix=cov_matrix 284 | ).sample() 285 | 286 | return new_yv[...,0], new_yv[...,1] 287 | 288 | 289 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | """Unit test package for LanPaint.""" 2 | -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | # Add the project root directory to Python path 5 | # This allows the tests to import the project 6 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 7 | -------------------------------------------------------------------------------- /tests/pytest.ini: -------------------------------------------------------------------------------- 1 | [pytest] 2 | testpaths = . # Run tests in the current directory 3 | python_files = test_*.py # Run tests in files that start with "test_" 4 | norecursedirs = .. # Don't run tests in the parent directory 5 | -------------------------------------------------------------------------------- /tests/test_LanPaint.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | """Tests for `LanPaint` package.""" 4 | 5 | import pytest 6 | from src.LanPaint.nodes import Example 7 | 8 | @pytest.fixture 9 | def example_node(): 10 | """Fixture to create an Example node instance.""" 11 | return Example() 12 | 13 | def test_example_node_initialization(example_node): 14 | """Test that the node can be instantiated.""" 15 | assert isinstance(example_node, Example) 16 | 17 | def test_return_types(): 18 | """Test the node's metadata.""" 19 | assert Example.RETURN_TYPES == ("IMAGE",) 20 | assert Example.FUNCTION == "test" 21 | assert Example.CATEGORY == "Example" 22 | -------------------------------------------------------------------------------- /web/js/example.js: -------------------------------------------------------------------------------- 1 | console.log(app); 2 | --------------------------------------------------------------------------------