Bump version from 1.4.9 to 1.4.10

Merge pull request #71 from godnight10061/fix/issue69-nan-multivariatenormal
Avoid MultivariateNormal crash on non-finite dynamics
2026-01-13 22:17:42 +08:00 · 2026-01-13 22:16:34 +08:00 · 2026-01-13 22:13:30 +08:00 · 2026-01-13 21:46:56 +08:00 · 2026-01-13 21:38:49 +08:00 · 2026-01-13 21:31:15 +08:00
78 changed files with 11224 additions and 928 deletions
@@ -26,6 +26,7 @@ jobs:
        run: |
          python -m pip install --upgrade pip
          pip install .[dev]
+          pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
      - name: Run Linting
        run: |
          ruff check .
@@ -11,3 +11,5 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - uses: comfy-org/node-diff@main
+        with:
+          base_ref: ${{ github.event.repository.default_branch }}
@@ -98,3 +98,5 @@ cookiecutter-pypackage-env/
 # vscode settings
 .history/
 *.code-workspace
+.vscode/
+/.vscode
@@ -5,4 +5,8 @@
    "/PATH/TO/ComfyUI/",
    "/PATH/TO/ComfyUI/custom_nodes/"
  ],
+  "cursorpyright.analysis.extraPaths": [
+    "/PATH/TO/ComfyUI/",
+    "/PATH/TO/ComfyUI/custom_nodes/"
+  ],
 }
@@ -1,46 +1,96 @@
 <div align="center">

 # LanPaint: Universal Inpainting Sampler with "Think Mode"
-[![arXiv](https://img.shields.io/badge/Arxiv-2502.03491-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2502.03491)
+[![TMLR PDF](https://img.shields.io/badge/TMLR-PDF-8A2BE2?logo=openreview&logoColor=white)](https://openreview.net/pdf?id=JPC8JyOUSW)
 [![Python Benchmark](https://img.shields.io/badge/🐍-Python_Benchmark-3776AB?logo=python)](https://github.com/scraed/LanPaintBench)
 [![ComfyUI Extension](https://img.shields.io/badge/ComfyUI-Extension-7B5DFF)](https://github.com/comfyanonymous/ComfyUI)
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-yellow?logo=huggingface&logoColor=white)](https://huggingface.co/charrywhite/LanPaint)
 [![Blog](https://img.shields.io/badge/📝-Blog-9cf)](https://scraed.github.io/scraedBlog/)
 [![GitHub stars](https://img.shields.io/github/stars/scraed/LanPaint)](https://github.com/scraed/LanPaint/stargazers)
+[![Discord](https://img.shields.io/badge/Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/aCGZutBV)
 </div>

+
 Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.  

-This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+This is the official implementation of ["LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling"](https://arxiv.org/abs/2502.03491), accepted by TMLR. The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).

-![Qwen Result 2](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_03.jpg) 
-Check [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14). You need to follow the ComfyUI version of [Qwen Image Edit workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) to download and install the model.
+## Citation

-![Qwen Result 1](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_01.jpg) 
+```
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
+}
+```
+**🎉 NEW 2026: Join our discord!**

-Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_13) and [Qwen Outpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_12). You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
+[Join our Discord](https://discord.gg/aCGZutBV) to share experiences, discuss features, and explore future development.
+
+**🎬 NEW: LanPaint now supports inpainting and outpainting based on Z-Image!**
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+| ![Original Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Original_No_Mask.png) | ![Masked Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Masked_Load_Me_in_Loader.png) | ![Inpainted Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/InPainted_Drag_Me_to_ComfyUI.png) |
+
+
+**🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
+
+<div align="center">
+
+| Original Video | Mask (edit T-shirt text) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+| ![Original](https://github.com/scraed/LanPaint/blob/master/examples/Original_No_Mask-example18.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Example_18/Masked_Load_Me_in_Loader.png) | ![Result](https://github.com/scraed/LanPaint/blob/master/examples/Inpainted_81frames_Drag_Me_to_ComfyUI_example18.gif) |
+
+*Video Inpainting Example: 81 frames with temporal consistency*
+
+</div>
+
+Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and 
+[Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint) support.
+  

 ## Table of Contents
 - [Features](#features)
 - [Quickstart](#quickstart)
 - [How to Use Examples](#how-to-use-examples)
- [Examples](#examples)
+- [Video Examples (Beta)](#video-examples-beta)
+  - [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
+  - [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
+  - [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
+  - [Resource Consumption](#resource-consumption)
+- [Image Examples](#image-examples)
+  - [Flux.2.Dev](#example-flux2dev-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Z-image](#example-z-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Hunyuan T2I](#example-hunyuan-t2i-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Wan 2.2 T2I with reference](#example-wan22-partial-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Qwen Image Edit 2511 2509](#example-qwen-edit-2509-inpaint)
+  - [Qwen Image Edit 2508](#example-qwen-edit-2508-inpaint)
  - [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
  - [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
  - [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
  - [Flux](#example-flux-inpaintlanpaint-k-sampler-5-steps-of-thinking)
-  - [SDXL Examples](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
+  - [SDXL](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
 - [Usage](#usage)
  - [Basic Sampler](#basic-sampler)
  - [Advanced Sampler](#lanpaint-ksampler-advanced)
  - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
- [Community Showcase](#community-showcase-) 
+- [Community Showcase](#community-showcase-)
+- [FAQ](#faq)
 - [Updates](#updates)
 - [ToDo](#todo)
 - [Citation](#citation)

 ## Features

- **Universal Compatibility** – Works instantly with almost any model (**SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image or custom LoRAs**) and ControlNet.  
+- **Universal Compatibility** – Works instantly with almost any model (**Z-image, Hunyuan, Wan 2.2, Qwen Image/Edit, HiDream, SD 3.5, Flux-series, SDXL, SD 1.5 or custom LoRAs**) and ControlNet.  
 ![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
 - **No Training Needed** – Works out of the box with your existing model.  
 - **Easy to Use** – Same workflow as standard ComfyUI KSampler.  
@@ -76,10 +126,175 @@ Once installed, you'll find the LanPaint nodes under the "sampling" category in
   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)**
   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)**

-## Examples
+## Video Examples (Beta)
+
+LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly inpaint masked regions across video frames while maintaining temporal consistency.
+
+**Note:** LanPaint supports video inpainting for longer sequences (e.g., 81 frames), but processing time increases significantly (please check the [Resource Consumption](#resource-consumption) section for details) and performance may become unstable. For optimal results and stability, we recommend limiting video inpainting to **40 frames or fewer**.
+
+### Wan 2.2 Video Inpainting 
+
+*Example: Wan2.2 t2v 14B, 480p video (11:6), 40 frames, LanPaint K Sampler, 2 steps of thinking*
+
+| Original Video | Mask (Add a white hat) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+| ![Original Video](https://github.com/scraed/LanPaint/blob/master/examples/Original_No_Mask_example17.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Example_17/Masked_Load_Me_in_Loader.png) | ![Inpainted Result](https://github.com/scraed/LanPaint/blob/master/examples/Inpainted_40frames_Drag_Me_to_ComfyUI_example17.gif) |
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Wan 2.2 5B Video Inpainting 
+
+Similar to Wan 2.2 14B with slightly different workflow. [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
+### Wan 2.2 Video Outpainting
+
+Extend your videos beyond their original boundaries with LanPaint's video outpainting capability based on Wan 2.2. This feature allows you to expand the canvas of your videos while maintaining coherent motion and context.
+
+*Example: Wan2.2 t2v 14B, 480p video (1:1 outpaint to 11:6), 40 frames, LanPaint K Sampler, 2 steps of thinking*
+
+| Original Video | Mask (Expand to 880x480) | Outpainted Result |
+|:--------------:|:----:|:-----------------:|
+| ![Original Video](https://github.com/scraed/LanPaint/blob/master/examples/Original_Load_Me_in_Loader_example19.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Mask_Example19_.png) | ![Outpainted Result](https://github.com/scraed/LanPaint/blob/master/examples/Outpainted_40frames_Drag_Me_to_ComfyUI_example19.gif) |
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_19)
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Resource Consumption
+
+
+<table>
+<thead>
+<tr>
+<th align="left">Processing Mode</th>
+<th align="left">Resolution</th>
+<th align="left">Frames Processed</th>
+<th align="left">VRAM Required</th>
+<th align="left">Total Runtime (20 steps)</th>
+</tr>
+</thead>
+<tbody>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Inpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>40 frames</td>
+<td>39.8 GB</td>
+<td><strong>05:37 min</strong></td>
+</tr>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Inpainting</strong></td>
+<td>480×480 (1:1)</td>
+<td>40 frames</td>
+<td>38.0 GB</td>
+<td><strong>05:35 min</strong></td>
+</tr>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Outpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>40 frames</td>
+<td>40.2 GB</td>
+<td><strong>05:36 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Inpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>81 frames</td>
+<td>43.3 GB</td>
+<td><strong>16:23 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Inpainting</strong></td>
+<td>480×480 (1:1)</td>
+<td>81 frames</td>
+<td>39.8 GB</td>
+<td><strong>14:25 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Outpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>81 frames</td>
+<td>42.6 GB</td>
+<td><strong>13:46 min</strong></td>
+</tr>
+</tbody>
+</table>
+
+<sub>**Test Platform**: All tests were conducted on an NVIDIA RTX Pro 6000.<br>
+**Model Used**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors`.<br>
+**Processing Steps**:  20 sampling steps x 2 (LanPaint steps of thinking).</sub>
+
+**Note:** Vram is required by the model, not LanPaint. To further reduce VRAM requirements, we recommend generating less frames and loading CLIP on CPU.
+
+## Image Examples
+
+### Example Hunyuan T2I: InPaint(LanPaint K Sampler, 5 steps of thinking)
+We are excited to announce that LanPaint now supports inpainting with Hunyuan text to image generation.
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_20)
+
+
+You need to follow the ComfyUI version of [Hunyuan workflow](https://docs.comfy.org/tutorials/video/hunyuan-video#hunyuan-text-to-video-workflow) to download and install the model.
+
+### Example Wan2.2: InPaint(LanPaint K Sampler, 5 steps of thinking)
+We are excited to announce that LanPaint now supports Wan2.2 text to image generation with Wan2.2 T2V model.
+
+![Inpainting Result 45](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_45.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_15)
+
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Example Z-image: InPaint(LanPaint K Sampler, 5 steps of thinking)
+LanPaint also supports inpainting with the Z-image text-to-image model.
+
+<details open>
+<summary>View Original / Masked / Inpainted Comparison</summary>
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+| ![Original Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Original_No_Mask.png) | ![Masked Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Masked_Load_Me_in_Loader.png) | ![Inpainted Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/InPainted_Drag_Me_to_ComfyUI.png) |
+
+</details>
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_21)
+
+<details open>
+<summary>View Z-image Outpainting (Original / Masked / Outpainted)</summary>
+
+| Original | Masked | Outpainted |
+|:--------:|:------:|:----------:|
+| ![Original Z-image Outpaint](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/Original_No_Mask.png) | ![Masked Z-image Outpaint](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/Masked_Load_Me_in_Loader.png) | ![Outpainted Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/InPainted_Drag_Me_to_ComfyUI.png) |
+
+</details>
+
+[View Outpaint Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_22)
+
+You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
+
+### Example Wan2.2: Partial InPaint(LanPaint K Sampler, 5 steps of thinking)
+Sometimes we don't want to inpaint completely new content, but rather let the inpainted image reference the original image. One option to achieve this is to inpaint with an edit model like Qwen Image Edit. Another option is to perform a partial inpaint: allowing the diffusion process to start at some middle steps rather than from 0.
+
+![Inpainting Result 46](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_46.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_16)
+
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+
+### Example Qwen Edit 2509: InPaint
+Check our latest updated [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14) for Qwen Image Edit 2509. Download the model at [Qwen Image Edit 2509 Comfy](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models). This workflow also supports Qwen Image Edit 2511.
+
+![Qwen Result 3](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_04.jpg) 
+
+### Example Qwen Edit 2508: InPaint
+![Qwen Result 2](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_03.jpg) 
+Check [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14). You need to follow the ComfyUI version of [Qwen Image Edit workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) to download and install the model.
+
+

 ### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
-We are excited to announce that LanPaint now supports Qwen Image, providing powerful inpainting capabilities for image editing.

 ![Inpainting Result 14](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_14.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_11)
@@ -89,6 +304,9 @@ You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.com

 The following examples utilize a random seed of 0 to generate a batch of 4 images for variance demonstration and fair comparison. (Note: Generating 4 images may exceed your GPU memory; please adjust the batch size as necessary.)

+![Qwen Result 1](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_01.jpg) 
+Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_13) and [Qwen Outpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_12). You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
+
 ### Example HiDream: InPaint (LanPaint K Sampler, 5 steps of thinking)
 ![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_11.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
@@ -107,6 +325,23 @@ You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.

 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.

+### Example Flux.2.Dev: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+<details open>
+<summary>View Original / Masked / Inpainted Comparison</summary>
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+| ![Original Flux.2.Dev](https://github.com/scraed/LanPaint/blob/master/examples/Example_23/Original_No_Mask.png) | ![Masked Flux.2.Dev](https://github.com/scraed/LanPaint/blob/master/examples/Example_23/Masked_Load_Me_in_Loader.png) | ![Inpainted Flux.2.Dev](https://github.com/scraed/LanPaint/blob/master/examples/Example_23/InPainted_Drag_Me_to_ComfyUI.png) |
+
+</details>
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_23)
+
+[Model Used in This Example](https://huggingface.co/Comfy-Org/flux2-dev)
+
+(Note: Prompt First mode is disabled on Flux.2.Dev. As it does not use CFG guidance.)
+
 ### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
 ![Inpainting Result 7](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_10.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
@@ -198,7 +433,7 @@ For challenging inpainting tasks:
 Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
  
 2️⃣ **Boost Speed**
-If you want better results but still need fewer steps, consider:
+Decrease **LanPaint_NumSteps** to accelerate generation! If you want better results but still need fewer steps, consider:
    - **Increasing LanPaint_StepSize** to speed up the thinking process.
    - **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster.
    
@@ -223,6 +458,9 @@ Discover how the community is using LanPaint! Here are some user-created tutoria

 Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!

+## FAQ
+[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
+
 ## Updates
 - 2025/08/08
    - Add Qwen image support
@@ -251,17 +489,19 @@ Submit a PR to add your tutorial/video here, or open an [Issue](https://github.c
 - Try Implement Detailer
 - ~~Provide inference code on without GUI.~~ Check our local Python benchmark code [LanPaintBench](https://github.com/scraed/LanPaintBench).

+
 ## Citation

 ```
-@misc{zheng2025lanpainttrainingfreediffusioninpainting,
-      title={Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference}, 
-      author={Candi Zheng and Yuan Lan and Yang Wang},
-      year={2025},
-      eprint={2502.03491},
-      archivePrefix={arXiv},
-      primaryClass={eess.IV},
-      url={https://arxiv.org/abs/2502.03491}, 
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
 }
 ```

@@ -269,4 +509,3 @@ Submit a PR to add your tutorial/video here, or open an [Issue](https://github.c



-
@@ -10,7 +10,85 @@ __author__ = """LanPaint"""
 __email__ = "czhengac@connect.ust.hk"
 __version__ = "0.0.1"

-from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
-from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
+
+def _install_lightweight_runtime_stubs() -> None:
+    """Install lightweight stubs so tooling can import this package without ComfyUI.
+
+    This is used by CI tooling (e.g., comfy-org/node-diff) that imports NODE_CLASS_MAPPINGS
+    in an environment where ComfyUI isn't installed.
+    """
+    import sys
+    import types
+
+    # `src/LanPaint/nodes.py` uses `torch.Tensor` in type annotations.
+    try:
+        import torch  # noqa: F401
+    except ModuleNotFoundError:
+        torch_mod = types.ModuleType("torch")
+
+        class Tensor:  # noqa: N801 (match torch naming)
+            pass
+
+        torch_mod.Tensor = Tensor
+        torch_mod.nn = types.SimpleNamespace(functional=types.SimpleNamespace())
+        sys.modules["torch"] = torch_mod
+
+    if "comfyui_version" not in sys.modules:
+        comfyui_version_mod = types.ModuleType("comfyui_version")
+        comfyui_version_mod.__version__ = "0.0.0"
+        sys.modules["comfyui_version"] = comfyui_version_mod
+
+    sys.modules.setdefault("nodes", types.ModuleType("nodes"))
+    sys.modules.setdefault("latent_preview", types.ModuleType("latent_preview"))
+
+    if "comfy" not in sys.modules:
+        comfy_mod = types.ModuleType("comfy")
+        comfy_mod.__path__ = []
+
+        comfy_utils_mod = types.ModuleType("comfy.utils")
+
+        def repeat_to_batch_size(tensor, batch_size):  # type: ignore[no-untyped-def]
+            if getattr(tensor, "shape", ())[0] == batch_size:
+                return tensor
+            return tensor
+
+        comfy_utils_mod.repeat_to_batch_size = repeat_to_batch_size
+
+        comfy_samplers_mod = types.ModuleType("comfy.samplers")
+
+        class DummyKSAMPLER:  # noqa: N801 (match ComfyUI naming)
+            pass
+
+        comfy_samplers_mod.KSAMPLER = DummyKSAMPLER
+
+        comfy_model_base_mod = types.ModuleType("comfy.model_base")
+
+        class ModelType:  # noqa: N801 (match ComfyUI naming)
+            FLUX = "FLUX"
+            FLOW = "FLOW"
+
+        class WAN22:  # noqa: N801 (match ComfyUI naming)
+            pass
+
+        comfy_model_base_mod.ModelType = ModelType
+        comfy_model_base_mod.WAN22 = WAN22
+
+        comfy_mod.utils = comfy_utils_mod
+        comfy_mod.samplers = comfy_samplers_mod
+        comfy_mod.model_base = comfy_model_base_mod
+
+        sys.modules["comfy"] = comfy_mod
+        sys.modules["comfy.utils"] = comfy_utils_mod
+        sys.modules["comfy.samplers"] = comfy_samplers_mod
+        sys.modules["comfy.model_base"] = comfy_model_base_mod
+
+
+try:
+    from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
+    from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
+except ModuleNotFoundError:
+    _install_lightweight_runtime_stubs()
+    from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
+    from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS

 WEB_DIRECTORY = "./web"
@@ -0,0 +1,786 @@
+{
+  "id": "978d3a45-3d13-43c6-8ef9-89dc3e74d6ba",
+  "revision": 0,
+  "last_node_id": 82,
+  "last_link_id": 220,
+  "nodes": [
+    {
+      "id": 66,
+      "type": "SetLatentNoiseMask",
+      "pos": [
+        514.1915893554688,
+        781.9396362304688
+      ],
+      "size": [
+        264.5999755859375,
+        46
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 216
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 189
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            186
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23",
+        "Node name for S&R": "SetLatentNoiseMask"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 78,
+      "type": "CLIPTextEncode",
+      "pos": [
+        314.8565368652344,
+        255.63235473632812
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 194
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            195
+          ]
+        }
+      ],
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 80,
+      "type": "CLIPTextEncode",
+      "pos": [
+        362.7684020996094,
+        481.0662536621094
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 196
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            210
+          ]
+        }
+      ],
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 79,
+      "type": "FluxGuidance",
+      "pos": [
+        529.1380615234375,
+        156.20236206054688
+      ],
+      "size": [
+        211.60000610351562,
+        58
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "conditioning",
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "link": 195
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            207
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "FluxGuidance"
+      },
+      "widgets_values": [
+        3.5
+      ]
+    },
+    {
+      "id": 77,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -66.25785064697266,
+        484.081787109375
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "MODEL",
+          "name": "MODEL",
+          "type": "MODEL",
+          "slot_index": 0,
+          "links": [
+            197
+          ]
+        },
+        {
+          "label": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "slot_index": 1,
+          "links": [
+            194,
+            196
+          ]
+        },
+        {
+          "label": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "slot_index": 2,
+          "links": [
+            200,
+            203
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "flux1-dev-fp8.safetensors"
+      ]
+    },
+    {
+      "id": 73,
+      "type": "LanPaint_KSampler",
+      "pos": [
+        1005.0399780273438,
+        296.9553527832031
+      ],
+      "size": [
+        413.6495666503906,
+        596
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 197
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 207
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 210
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 186
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            187
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "LanPaint",
+        "ver": "56bd6c04e89124cd06682b304245d6ddf8b20522",
+        "Node name for S&R": "LanPaint_KSampler"
+      },
+      "widgets_values": [
+        0,
+        "fixed",
+        30,
+        1,
+        "euler",
+        "simple",
+        1,
+        5,
+        "Image First",
+        "LanPaint KSampler. Recommend steps 50, LanPaint NumSteps 1-20 depending on the difficulty of task. LanPaint_EndSigma = 3.0 for anime style, 0.6 for realistic style. For more information, visit https://github.com/scraed/LanPaint",
+        "🖼️ Image Inpainting"
+      ]
+    },
+    {
+      "id": 65,
+      "type": "VAEEncode",
+      "pos": [
+        200.1034698486328,
+        776.3561401367188
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 188
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 203
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            216
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23",
+        "Node name for S&R": "VAEEncode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 48,
+      "type": "SaveImage",
+      "pos": [
+        1075.49169921875,
+        1167.2703857421875
+      ],
+      "size": [
+        311.2532653808594,
+        484.7096252441406
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 103
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23"
+      },
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 81,
+      "type": "LanPaint_MaskBlend",
+      "pos": [
+        1773.0189208984375,
+        1222.9478759765625
+      ],
+      "size": [
+        210,
+        98
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image1",
+          "type": "IMAGE",
+          "link": 219
+        },
+        {
+          "name": "image2",
+          "type": "IMAGE",
+          "link": 218
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 220
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            217
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "LanPaint",
+        "ver": "4d3d5d17f0105b673df92da5b084cce567c9c712",
+        "Node name for S&R": "LanPaint_MaskBlend"
+      },
+      "widgets_values": [
+        9
+      ]
+    },
+    {
+      "id": 82,
+      "type": "SaveImage",
+      "pos": [
+        2106.45166015625,
+        1090.682861328125
+      ],
+      "size": [
+        311.2532653808594,
+        484.7096252441406
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 217
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23"
+      },
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1211.46484375,
+        1065.318359375
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 187
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 200
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            103,
+            218
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23",
+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 75,
+      "type": "LoadImage",
+      "pos": [
+        45.954593658447266,
+        1150.45556640625
+      ],
+      "size": [
+        266.13720703125,
+        487.1314697265625
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            188,
+            219
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            189,
+            220
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "clipspace/clipspace-mask-8865503.5.png [input]",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      103,
+      8,
+      0,
+      48,
+      0,
+      "IMAGE"
+    ],
+    [
+      186,
+      66,
+      0,
+      73,
+      3,
+      "LATENT"
+    ],
+    [
+      187,
+      73,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      188,
+      75,
+      0,
+      65,
+      0,
+      "IMAGE"
+    ],
+    [
+      189,
+      75,
+      1,
+      66,
+      1,
+      "MASK"
+    ],
+    [
+      194,
+      77,
+      1,
+      78,
+      0,
+      "CLIP"
+    ],
+    [
+      195,
+      78,
+      0,
+      79,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      196,
+      77,
+      1,
+      80,
+      0,
+      "CLIP"
+    ],
+    [
+      197,
+      77,
+      0,
+      73,
+      0,
+      "MODEL"
+    ],
+    [
+      200,
+      77,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      203,
+      77,
+      2,
+      65,
+      1,
+      "VAE"
+    ],
+    [
+      207,
+      79,
+      0,
+      73,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      210,
+      80,
+      0,
+      73,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      216,
+      65,
+      0,
+      66,
+      0,
+      "LATENT"
+    ],
+    [
+      217,
+      81,
+      0,
+      82,
+      0,
+      "IMAGE"
+    ],
+    [
+      218,
+      8,
+      0,
+      81,
+      1,
+      "IMAGE"
+    ],
+    [
+      219,
+      75,
+      0,
+      81,
+      0,
+      "IMAGE"
+    ],
+    [
+      220,
+      75,
+      1,
+      81,
+      2,
+      "MASK"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Mask image for inpainting.",
+      "bounding": [
+        36.04227828979492,
+        989.7313232421875,
+        278.89093017578125,
+        669.3414916992188
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "Convert Latents for LanPaint",
+      "bounding": [
+        286.0640563964844,
+        714.343505859375,
+        489.16796875,
+        197.81044006347656
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 3,
+      "title": "Load Model and Set Prompts",
+      "bounding": [
+        -78.9311294555664,
+        176.08712768554688,
+        1006.1721801757812,
+        514.258544921875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 4,
+      "title": "Inpaint with the LanPaint KSampler",
+      "bounding": [
+        960.8922729492188,
+        179.17588806152344,
+        474.8909606933594,
+        630.4742431640625
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 5,
+      "title": "LanPaint OutPut",
+      "bounding": [
+        1085.6029052734375,
+        994.0775756835938,
+        345.4561767578125,
+        669.4969482421875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 11,
+      "title": "LanPaint",
+      "bounding": [
+        -262.59381103515625,
+        140.46656799316406,
+        1737.328857421875,
+        797.4443359375
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.35049389948139237,
+      "offset": [
+        348.866804381099,
+        308.65057628971834
+      ]
+    },
+    "frontendVersion": "1.27.10",
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "LanPaint": "0f509469ed2cd60c6032f739e282aad5dfc06166"
+    }
+  },
+  "version": 0.4
+}
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "LanPaint"
-version = "1.3.1"
+version = "1.4.10"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
  {name = "LanPaint", email = "czhengac@connect.ust.hk"}
@@ -75,5 +75,8 @@ select = [
    # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
 ]

+[tool.ruff.lint.per-file-ignores]
+"src/LanPaint/nodes.py" = ["F403", "F405"]
+
 [tool.ruff.lint.flake8-quotes]
 inline-quotes = "double"
@@ -1,5 +1,5 @@
 import torch
-from .utils import *
+from .utils import StochasticHarmonicOscillator
 from functools import partial

 class LanPaint():
@@ -13,6 +13,7 @@ class LanPaint():
        self.friction = Friction
        self.chara_beta = Beta
        self.img_dim_size = None
+
    def add_none_dims(self, array):
        # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
        index = (slice(None),) + (None,) * (self.img_dim_size-1)
@@ -25,19 +26,23 @@ class LanPaint():
        self.img_dim_size = len(x.shape)
        self.latent_image = latent_image
        self.noise = noise
+        if torch.mean(torch.abs(self.noise)) < 1e-8:
+            self.noise = torch.randn_like(self.noise)
        if n_steps is None:
            n_steps = self.n_steps
        return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
    def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW):
        VE_Sigma, abt, Flow_t = current_times

-        
        step_size = self.step_size * (1 - abt)
        step_size = self.add_none_dims(step_size)
        # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
        # This is the replace step
-        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
-        
+        def scale_latent_inpaint(x, sigma, noise, latent_image):
+            return self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
+
+        x = x * (1 - latent_mask) +  scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
+
        if IS_FLUX or IS_FLOW:
            x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
        else:
@@ -60,7 +65,6 @@ class LanPaint():
        return out

    def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):
-        
        lamb = self.chara_lamb
        if self.IS_FLUX or self.IS_FLOW:
            # compute t for flow model, with a small epsilon compensating for numerical error.
@@ -111,24 +115,71 @@ class LanPaint():
            x_t = x_t.to(dtype)
            v = v.to(dtype)
            return x_t, v
-        if args is None:
-            #v = torch.zeros_like(x_t)
-            v = None
-            C = Coef_C(x_t)
-            #print(torch.squeeze(dtx), torch.squeeze(dty))
-            x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
-        else:
-            v, C = args

-            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+        def advance_time_overdamped(x_t, dt, A, C, D):
+            """
+            Overdamped (Gamma -> infinity) limit:
+                dx = -A x dt + C dt + D dW_t
+            with C treated as constant over this substep.
+            """
+            dtype = x_t.dtype
+            with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+                A_dt = A * dt
+                exp_neg = torch.exp(-A_dt)

-            C_new = Coef_C(x_t)
-            v = v + Gamma**0.5 * ( C_new - C) *dt
+                eps = 1e-8
+                abs_A = torch.abs(A)
+                # k  = (1 - exp(-A dt)) / A  -> dt when A -> 0
+                k = torch.where(abs_A < eps, dt, (-torch.expm1(-A_dt)) / A)
+                # k2 = (1 - exp(-2 A dt)) / (2 A) -> dt when A -> 0
+                k2 = torch.where(abs_A < eps, dt, (-torch.expm1(-2 * A_dt)) / (2 * A))

-            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+                mean = exp_neg * x_t + k * C
+                var = (D ** 2) * k2
+                noise = torch.randn_like(x_t) * torch.sqrt(torch.clamp(var, min=0.0))
+                x_t = mean + noise
+            return x_t.to(dtype)
+
+        def run_damped(x_t, args):
+            if args is None:
+                v = None
+                C = Coef_C(x_t)
+                x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
+            else:
+                v, C = args
+                x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+                C_new = Coef_C(x_t)
+                v = v + Gamma**0.5 * ( C_new - C) *dt
+                x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+                C = C_new
+            return x_t, (v, C)
+
+        def run_overdamped(x_t, args):
+            if args is None:
+                C = Coef_C(x_t)
+                x_t = advance_time_overdamped(x_t, dt, A, C, D)
+            else:
+                _, C = args
+                x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+                C_new = Coef_C(x_t)
+                x_t = x_t + (C_new - C) * dt
+                x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+                C = C_new
+            return x_t, (None, C)
+
+        try:
+            x_t_next, (v_next, C_next) = run_damped(x_t, args)
+
+            if torch.isnan(x_t_next).any() or torch.isnan(v_next).any():
+                raise ValueError("NaN detected")
+
+            x_t = x_t_next
+            v = v_next
+            C = C_next
+
+        except Exception:
+            x_t, (v, C) = run_overdamped(x_t, args)

-            C = C_new
-  
        return x_t, (v, C)

    def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
@@ -140,7 +191,7 @@ class LanPaint():
        # Compute time step (dtx, dty) for x and y branches.
        dtx = 2 * step_size * sigma_x
        dty = 2 * step_size * sigma_y
-        
+
        # -------------------------------------------------------------------------
        # Define friction parameter Gamma_hat for each branch.
        # Using dtx**0 provides a tensor of the proper device/dtype.
@@ -170,4 +221,4 @@ class LanPaint():

    def x0_evalutation(self, x_t, score, sigma, args):
        x0 = x_t + score(x_t)
-        return x0
+        return x0
@@ -1,29 +1,82 @@
 from contextlib import contextmanager
-from inspect import cleandoc
-import inspect
+import math
 # import nodes.py
 import comfy
 import nodes
 import latent_preview
-from functools import partial
+import torch
 from comfy.utils import repeat_to_batch_size
 from comfy.samplers import *
 from comfy.model_base import ModelType
-from .utils import *
 from .lanpaint import LanPaint
+from comfy.model_base import WAN22
+import comfyui_version 

-
-def reshape_mask(input_mask, output_shape):
+def reshape_mask(input_mask, output_shape,video_inpainting=False):
    dims = len(output_shape) - 2
-
+    print('output shape',output_shape)
    scale_mode = "nearest-exact"
-    mask = torch.nn.functional.interpolate(input_mask, size=output_shape[-2:], mode=scale_mode)
-    if mask.shape[1] < output_shape[1]:
-        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
-    mask = repeat_to_batch_size(mask, output_shape[0])
+    print('input mask',input_mask.shape,type(input_mask),torch.max(input_mask),torch.min(input_mask))
+    print('target output_shape',output_shape)
+    print('input_mask.ndim:', input_mask.ndim, 'output_shape len:', len(output_shape))
+
+    # Handle input mask dimensions
+    if input_mask.ndim == 2:
+        input_mask = input_mask.unsqueeze(0).unsqueeze(0)
+    elif input_mask.ndim == 3:
+        input_mask = input_mask.unsqueeze(1)
+
+    # Handle 5D output shape (B, C, F, H, W) by ensuring input is 5D
+    if len(output_shape) == 5 and input_mask.ndim == 4:
+        if comfyui_version.__version__ >= "0.6.0":
+            input_mask = input_mask.unsqueeze(2)  # (B, C, 1, H, W)
+
+    # Handle video case with temporal dimension
+    if video_inpainting:  # Video case: (batch, channels, frames, height, width)
+        target_frames = output_shape[2]
+        target_height, target_width = output_shape[-2:]
+
+        print('Video case - input_mask initial shape:', input_mask.shape)
+
+        # First reshape input_mask to have proper dimensions for video processing
+        # Assume input is (frames, channels, height, width) -> (1, channels, frames, height, width)
+        ## if comfy version < 0.6.0
+        if comfyui_version.__version__ < "0.6.0":
+            input_mask = input_mask.permute(1, 0, 2, 3).unsqueeze(0)
+        print('Video case - input_mask after reshaping:', input_mask.shape)
+        # Ensure we have the correct 5D shape: (batch, channels, frames, height, width)
+        batch_size, channels, frames, height, width = input_mask.shape
+        print('Video case - dimensions: batch_size={}, channels={}, frames={}, height={}, width={}'.format(batch_size, channels, frames, height, width))
+        print('Video case - target size:', (target_frames, target_height, target_width))
+
+        # 3D nearest-exact interpolation: (batch, channels, frames, height, width) -> (batch, channels, target_frames, target_height, target_width)
+        temp_mask = torch.nn.functional.interpolate(
+            input_mask, 
+            size=(target_frames, target_height, target_width), 
+            mode=scale_mode, 
+        )
+
+        # temp_mask is already 5D: (batch, channels, target_frames, target_height, target_width)
+        mask = temp_mask
+        print('after mask',mask.shape)
+        # Handle channel dimension expansion if needed
+        if mask.shape[1] < output_shape[1]:
+            mask = mask.repeat(1, output_shape[1], 1, 1, 1)[:, :output_shape[1]]
+        # Handle batch dimension
+        mask = repeat_to_batch_size(mask, output_shape[0])
+    else:  # Original 2D image case
+        if comfyui_version.__version__ < "0.6.0":
+            mask = torch.nn.functional.interpolate(input_mask, size=output_shape[-2:], mode=scale_mode)
+        else:
+            mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+        if mask.shape[1] < output_shape[1]:
+            mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+        mask = repeat_to_batch_size(mask, output_shape[0])
+
+
    return mask
-def prepare_mask(noise_mask, shape, device):
-    return reshape_mask(noise_mask, shape).to(device)
+def prepare_mask(noise_mask, shape, device,video_inpainting=False):
+    return reshape_mask(noise_mask, shape,video_inpainting).to(device)
 def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, cond_scale_BIG, model_options={}, seed=None):
    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
        uncond_ = None
@@ -42,13 +95,18 @@ def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, con


 class CFGGuider_LanPaint:
-    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs):
        print("CFGGuider outer_sample")
        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
        device = self.model_patcher.load_device

+        if isinstance(self.inner_model, WAN22):
+            print("WAN22 detected")
+            self.inner_model.extra_conds = super(WAN22, self.inner_model).extra_conds
+
        if denoise_mask is not None:
-            denoise_mask = prepare_mask(denoise_mask, noise.shape, device)
+            video_inpainting = self.model_options.get("video_inpainting", False)
+            denoise_mask = prepare_mask(denoise_mask, noise.shape, device, video_inpainting)

        noise = noise.to(device)
        latent_image = latent_image.to(device)
@@ -57,7 +115,7 @@ class CFGGuider_LanPaint:

        try:
            self.model_patcher.pre_run()
-            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs)
        finally:
            self.model_patcher.cleanup()

@@ -86,14 +144,16 @@ class KSamplerX0Inpaint:

        IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX
        IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW
-
+        #print("model class", type(self.inner_model.inner_model))
+        #print("model type", self.inner_model.inner_model.model_type, "IS_FLUX", IS_FLUX, "IS_FLOW", IS_FLOW)
+        #print("sigma", torch.mean(sigma).item(), torch.min(sigma).item(), torch.max(sigma).item())
        # unify the notations into variance exploding diffusion model
        if IS_FLUX or IS_FLOW:
            Flow_t = sigma
            abt = (1 - Flow_t)**2 / ((1 - Flow_t)**2 + Flow_t**2 )
            VE_Sigma = Flow_t / (1 - Flow_t)
            #print("t", torch.mean( sigma ).item(), "VE_Sigma", torch.mean( VE_Sigma ).item())
-            
+

        else:
            VE_Sigma = sigma 
@@ -118,7 +178,7 @@ class KSamplerX0Inpaint:
                out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed)
        else:
            out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
-        
+
        # Add TAESD preview support - directly use the latent_preview module
        current_step = model_options.get("i", kwargs.get("i", 0))
        total_steps = model_options.get("total_steps", 0)
@@ -129,7 +189,7 @@ class KSamplerX0Inpaint:
            callback = model_options.get("callback", None)
            if callback is not None:
                callback({"i": current_step, "denoised": out, "x": x})
-    
+
        return out

 # Custom sampler class extending ComfyUI's KSAMPLER for LanPaint
@@ -245,6 +305,7 @@ class LanPaint_KSampler():
                "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."}),
                "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}),
                "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}),
+                "Inpainting_mode": (["🖼️ Image Inpainting", "🎬 Video Inpainting"], {"default": "🖼️ Image Inpainting", "tooltip": "Choose Image mode for photos or Video mode for video frames with temporal consistency"}),
                  }
        }

@@ -255,9 +316,9 @@ class LanPaint_KSampler():
    CATEGORY = "sampling"
    DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."

-    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=""):
+    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="",Inpainting_mode="🖼️ Image Inpainting"):

-        model.LanPaint_StepSize = 0.15
+        model.LanPaint_StepSize = 0.2
        model.LanPaint_Lambda = 16.0
        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -267,6 +328,13 @@ class LanPaint_KSampler():
            model.LanPaint_cfg_BIG = cfg
        else:
            model.LanPaint_cfg_BIG = 0*cfg - 0.5
+
+        # Convert inpainting_mode to boolean for video_inpainting
+        video_inpainting = (Inpainting_mode == "🎬 Video Inpainting")
+        if not hasattr(model, 'model_options') or model.model_options is None:
+            model.model_options = {}
+        model.model_options["video_inpainting"] = video_inpainting
+
        with override_sample_function():
            return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
 class LanPaint_KSamplerAdvanced:
@@ -288,12 +356,13 @@ class LanPaint_KSamplerAdvanced:
                    "return_with_leftover_noise": (["disable", "enable"], ),
                "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."}),
                "LanPaint_Lambda": ("FLOAT", {"default": 16., "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."}),
-                "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."}),
+                "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "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."}),
                "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."}),
                "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."}),
                "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}),
                "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."}),
                "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}),
+                "Inpainting_mode": (["🖼️ Image Inpainting", "🎬 Video Inpainting"], {"default": "🖼️ Image Inpainting", "tooltip": "Choose Image mode for photos or Video mode for video frames with temporal consistency"}),
                     },
                }

@@ -302,7 +371,7 @@ class LanPaint_KSamplerAdvanced:

    CATEGORY = "sampling"

-    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=""):
+    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, LanPaint_NumSteps=5, LanPaint_Lambda=16.0, LanPaint_StepSize=0.2, LanPaint_Beta=1.0, LanPaint_Friction=15.0, LanPaint_PromptMode="Image First", LanPaint_EarlyStop=1, LanPaint_Info="", Inpainting_mode="🖼️ Image Inpainting"):
        force_full_denoise = True
        if return_with_leftover_noise == "enable":
            force_full_denoise = False
@@ -320,8 +389,14 @@ class LanPaint_KSamplerAdvanced:
        else:
            model.LanPaint_cfg_BIG = 0*cfg - 0.5

+        # Convert inpainting_mode to boolean for video_inpainting
+        video_inpainting = (Inpainting_mode == "🎬 Video Inpainting")
+        if not hasattr(model, 'model_options') or model.model_options is None:
+            model.model_options = {}
+        model.model_options["video_inpainting"] = video_inpainting
+
        with override_sample_function():
-            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)
+            return nodes.common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)


 class MaskBlend:
@@ -349,15 +424,23 @@ class MaskBlend:
        # for each mask pixel, find out the nearest 1 pixel, and set the mask value to the distance between the two pixels
        # check the size of mask and image1, image2, if not the same, assert error
        if image1.shape[1] != image2.shape[1] or image1.shape[2] != image2.shape[2]:
-            raise ValueError("Make sure your image size is a multiple of 8. Otherwise the mask will not be aligned with the output image.")
-        
+            raise ValueError(
+                "Image size mismatch: Image1 and Image2 must have the same dimensions.\n"
+                "Additionally, ensure both images have width and height that are multiples of 8.\n"
+                "This is required because VAE decode always generates images with dimensions that are multiples of 8.\n"
+                "If your input images are not multiples of 8, a size mismatch will occur during the decoding process.\n"
+                "Please resize your images using an image resize node to ensure compatibility.\n"
+                "Current sizes - Image1: {}x{}, Image2: {}x{}".format(
+                    image1.shape[2], image1.shape[1], image2.shape[2], image2.shape[1]
+                )
+            )
        mask = mask.float()
        mask = torch.nn.functional.max_pool2d(mask, kernel_size=blend_overlap, stride=1, padding=blend_overlap//2)
        # apply Gaussian blur with kernel size blend_overlap
        kernel = self.gaussian_kernel(blend_overlap)
        kernel = kernel.to(image1.device)
        kernel = kernel[None, None, ...]
-        
+
        mask = torch.nn.functional.conv2d(mask[:,None,:,:], kernel, padding=blend_overlap//2)[:,0,:,:]


@@ -416,7 +499,7 @@ class LanPaint_SamplerCustom:
    CATEGORY = "sampling/custom_sampling"

    def sample(self, model, sampler, sigmas, add_noise, noise_seed, cfg, positive, negative, latent_image, LanPaint_NumSteps, LanPaint_PromptMode, LanPaint_Info=""):
-        model.LanPaint_StepSize = 0.15
+        model.LanPaint_StepSize = 0.2
        model.LanPaint_Lambda = 16.0
        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -455,22 +538,19 @@ class LanPaint_SamplerCustom:
            else:
                out_denoised = out
            return (out, out_denoised)
-        
+
 class LanPaint_SamplerCustomAdvanced:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"noise": ("NOISE",),
-                     "guider": ("GUIDER",),
-                     "sampler": ("SAMPLER",),
-                     "sigmas": ("SIGMAS",),
-                     "latent_image": ("LATENT",),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "return_with_leftover_noise": (["disable", "enable"], ),
+                    "guider": ("GUIDER", ),
+                    "sampler": ("SAMPLER", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),
                     "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
                     "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
-                     "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+                     "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
                     "LanPaint_Beta": ("FLOAT", {"default": 1.0, "min": 0.0001, "max": 5.0, "step": 0.1, "tooltip": "Step size ratio between masked/unmasked regions. Lower values balance high Lambda."}),
                     "LanPaint_Friction": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Friction parameter for fast Langevin. Lower values speed convergence but may be unstable."}),
                     "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
@@ -479,17 +559,14 @@ class LanPaint_SamplerCustomAdvanced:
                    }
               }

-    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_TYPES = ("LATENT","LATENT")
    RETURN_NAMES = ("output", "denoised_output")
+
    FUNCTION = "sample"
+
    CATEGORY = "sampling/custom_sampling"

-    def sample(self, noise, guider, sampler, sigmas, latent_image, start_at_step, end_at_step, return_with_leftover_noise, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
-        force_full_denoise = True
-        if end_at_step <= start_at_step:
-            raise ValueError('end_at_step must be larger than start_at_step')
-        if return_with_leftover_noise == "enable":
-            force_full_denoise = False
+    def sample(self, noise, guider, sampler, sigmas, latent_image, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
        model = guider.model_patcher
        model.LanPaint_StepSize = LanPaint_StepSize
        model.LanPaint_Lambda = LanPaint_Lambda
@@ -502,47 +579,33 @@ class LanPaint_SamplerCustomAdvanced:
        else:
            model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
        with override_sample_function():
-            latent = latent_image.copy()
-            latent_image_samples = latent["samples"]
-            latent_image_samples = comfy.sample.fix_empty_latent_channels(model, latent_image_samples)
-            latent["samples"] = latent_image_samples
+            latent = latent_image
+            latent_image = latent["samples"]
+            latent = latent.copy()
+            latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+            latent["samples"] = latent_image

            noise_mask = None
            if "noise_mask" in latent:
                noise_mask = latent["noise_mask"]

-            # From base comfy samplers.py
-            if end_at_step is not None and end_at_step < (len(sigmas) - 1):
-                sigmas = sigmas[:end_at_step + 1]
-                if force_full_denoise:
-                    sigmas[-1] = 0
-
-            if start_at_step is not None:
-                if start_at_step < (len(sigmas) - 1):
-                    sigmas = sigmas[start_at_step:]
-                else:
-                    if latent_image is not None:
-                        return latent_image
-                    else:
-                        return torch.zeros_like(noise)
-
            x0_output = {}
-            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+            callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
            disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
-
-            samples = guider.sample( noise.generate_noise(latent), latent_image_samples, sampler, sigmas, denoise_mask=noise_mask, callback=callback,disable_pbar=disable_pbar, seed=noise.seed
-            )
-
+            samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
            samples = samples.to(comfy.model_management.intermediate_device())
+
            out = latent.copy()
            out["samples"] = samples
            if "x0" in x0_output:
                out_denoised = latent.copy()
-                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+                out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
            else:
                out_denoised = out
            return (out, out_denoised)

+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {
@@ -28,11 +28,11 @@ def expm1mxmhx2_x3(x):
 def exp_1mcosh_GD(gamma_t, delta):
    """
    Compute e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
-    
+
    Parameters:
    gamma_t: Γ*t term (could be a scalar or tensor)
    delta: Δ term (could be a scalar or tensor)
-    
+
    Returns:
    Result of the computation with numerical stability handling
    """
@@ -68,7 +68,6 @@ def exp_sinh_GsqrtD(gamma_t, delta):
    sqrt_abs_delta = torch.sqrt(torch.abs(delta))
    gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta
    numerator_pos =  (torch.exp(gamma_t * (sqrt_abs_delta - 1)) - torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2
-    denominator_pos = gamma_t_sqrt_delta
    result_pos = numerator_pos / gamma_t_sqrt_delta
    result_pos = torch.where(torch.isfinite(result_pos), result_pos, torch.zeros_like(result_pos))

@@ -117,15 +116,15 @@ def zeta1(gamma_t, delta):
    exp_cosh_term = exp_cosh(half_gamma_t, delta)
    exp_sinh_term = exp_sinh_sqrtD(half_gamma_t, delta)

-    
+
    # Main computation
    numerator = 1 - (exp_cosh_term + exp_sinh_term)
    denominator = gamma_t * (1 - delta) / 4
    result = 1 - numerator / denominator
-    
+
    # Handle numerical instability
    result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
-    
+
    # Taylor expansion for small x (similar to your epxm1Dx approach)
    mask = torch.abs(denominator) < 5e-3
    term1 = epxm1_x(-gamma_t)
@@ -133,17 +132,17 @@ def zeta1(gamma_t, delta):
    term3 = expm1mxmhx2_x3(-gamma_t)
    taylor = term1 + (1/2.+ term1-3*term2)*denominator + (-1/6. + term1/2 - 4 * term2 + 10 * term3) * denominator**2
    result = torch.where(mask, taylor, result)
-    
+
    return result

 def exp_cosh_minus_terms(gamma_t, delta):
    """
    Compute E^(-tΓ) * (Cosh[tΓ] - 1 - (Cosh[tΓ√Δ] - 1)/Δ) / (tΓ(1 - Δ))
-    
+
    Parameters:
    gamma_t: Γ*t term (could be a scalar or tensor)
    delta: Δ term (could be a scalar or tensor)
-    
+
    Returns:
    Result of the computation with numerical stability handling
    """
@@ -151,17 +150,17 @@ def exp_cosh_minus_terms(gamma_t, delta):
    # Compute individual terms
    exp_cosh_term = exp_cosh(gamma_t, gamma_t**0) - exp_term # E^(-tΓ) (Cosh[tΓ] - 1) term
    exp_cosh_delta_term = - gamma_t**2 * exp_1mcosh_GD(gamma_t, delta)  # E^(-tΓ) (Cosh[tΓ√Δ] - 1)/Δ term
-    
+
    #exp_1mcosh_GD e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
    # Main computation
    numerator = exp_cosh_term - exp_cosh_delta_term
    denominator = gamma_t * (1 - delta)
-    
+
    result = numerator / denominator
-    
+
    # Handle numerical instability
    result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
-    
+
    # Taylor expansion for small gamma_t and delta near 1
    mask = (torch.abs(denominator) < 1e-1)
    exp_1mcosh_GD_term = exp_1mcosh_GD(gamma_t, delta**0)
@@ -170,7 +169,7 @@ def exp_cosh_minus_terms(gamma_t, delta):
       - denominator / 4 * ( 0.5 * exp_cosh(gamma_t, delta**0) - 4 * exp_1mcosh_GD_term - 5 /2 * exp_sinh_GsqrtD(gamma_t, delta**0) )
    )
    result = torch.where(mask, taylor, result)
-    
+
    return result


@@ -185,7 +184,7 @@ def sig11(gamma_t, delta):
 def Zcoefs(gamma_t, delta):
    Zeta1 = zeta1(gamma_t, delta)
    Zeta2 = zeta2(gamma_t, delta)
-    
+
    sq_total = 1 - Zeta1 + gamma_t * (delta - 1) * (Zeta1 - 1)**2 / 8
    amplitude = torch.sqrt(sq_total)
    Zcoef1 = ( gamma_t**0.5 * Zeta2 / 2 **0.5 ) / amplitude
@@ -208,7 +207,7 @@ class StochasticHarmonicOscillator:
        dq(t) = -Γ A y(t) dt + Γ C dt + Γ D dw(t) - Γ q(t) dt

    Also define v(t) = q(t) / √Γ, which is numerically more stable.
-        
+
    Where:
        y(t) - Position variable
        q(t) - Velocity variable
@@ -239,7 +238,7 @@ class StochasticHarmonicOscillator:
        Returns:
            tuple: (y(t), v(t))
        """
-        
+
        dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0
        Delta = self.Delta + dummyzero
        Gamma_hat = self.Gamma * t + dummyzero
@@ -254,12 +253,12 @@ class StochasticHarmonicOscillator:
        if v0 is None:
            v0 = torch.randn_like(y0) * D / 2 ** 0.5
            #v0 = (C - A * y0)/Gamma**0.5
-        
+
        # Calculate mean position and velocity
        term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t
        y_mean = term1 + y0
        v_mean =  (1 - EE)*(C - A * y0) / (Gamma ** 0.5) + (EE - A * t * (1 - zeta_1)) * v0
-        
+
        cov_yy = D**2 * t * self.sig22(Gamma_hat, Delta)
        cov_vv = D**2 * self.sig11(Gamma_hat, Delta) / 2
        cov_yv = (zeta2(Gamma_hat, Delta) * Gamma_hat * D ) **2 / 2 / (Gamma ** 0.5)
@@ -274,7 +273,7 @@ class StochasticHarmonicOscillator:
        cov_matrix[..., 1, 1] = cov_vv


-        
+
        # Compute the Cholesky decomposition to get scale_tril
        #scale_tril = torch.linalg.cholesky(cov_matrix)
        scale_tril = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
@@ -298,4 +297,4 @@ class StochasticHarmonicOscillator:
            scale_tril=scale_tril
        ).sample()

-        return new_yv[...,0], new_yv[...,1]
+        return new_yv[...,0], new_yv[...,1]
@@ -1,4 +1,5 @@
 [pytest]
-testpaths = . # Run tests in the current directory
-python_files = test_*.py # Run tests in files that start with "test_"
-norecursedirs = .. # Don't run tests in the parent directory
+# Keep settings value-only; pytest does not treat inline `# ...` as comments.
+testpaths = .
+python_files = test_*.py
+norecursedirs = ..
@@ -1,21 +1,13 @@
-#!/usr/bin/env python
+"""Basic import tests for LanPaint.

-"""Tests for `LanPaint` package."""
+The ComfyUI runtime dependencies (e.g. `comfy`) are intentionally optional for unit tests.
+"""

-import pytest
-from src.LanPaint.nodes import Example

-@pytest.fixture
-def example_node():
-    """Fixture to create an Example node instance."""
-    return Example()
+def test_package_imports_without_comfy() -> None:
+    import LanPaint

-def test_example_node_initialization(example_node):
-    """Test that the node can be instantiated."""
-    assert isinstance(example_node, Example)
-
-def test_return_types():
-    """Test the node's metadata."""
-    assert Example.RETURN_TYPES == ("IMAGE",)
-    assert Example.FUNCTION == "test"
-    assert Example.CATEGORY == "Example"
+    assert isinstance(LanPaint.NODE_CLASS_MAPPINGS, dict)
+    assert isinstance(LanPaint.NODE_DISPLAY_NAME_MAPPINGS, dict)
+    assert "LanPaint_KSampler" in LanPaint.NODE_CLASS_MAPPINGS
+    assert LanPaint.WEB_DIRECTORY == "./web"
@@ -0,0 +1,74 @@
+import importlib
+import sys
+import types
+
+import pytest
+import torch
+
+
+def _repeat_to_batch_size(tensor: torch.Tensor, batch_size: int) -> torch.Tensor:
+    if tensor.shape[0] == batch_size:
+        return tensor
+    if tensor.shape[0] == 1:
+        return tensor.repeat((batch_size,) + (1,) * (tensor.ndim - 1))
+    repeats = (batch_size + tensor.shape[0] - 1) // tensor.shape[0]
+    return tensor.repeat((repeats,) + (1,) * (tensor.ndim - 1))[:batch_size]
+
+
+def _import_nodes(monkeypatch, comfyui_version: str):
+    comfy_mod = types.ModuleType("comfy")
+    comfy_mod.__path__ = []
+
+    comfy_utils_mod = types.ModuleType("comfy.utils")
+    comfy_utils_mod.repeat_to_batch_size = _repeat_to_batch_size
+
+    comfy_samplers_mod = types.ModuleType("comfy.samplers")
+    class DummyKSAMPLER: ...
+    comfy_samplers_mod.KSAMPLER = DummyKSAMPLER
+
+    comfy_model_base_mod = types.ModuleType("comfy.model_base")
+    class ModelType:
+        FLUX = "FLUX"
+        FLOW = "FLOW"
+
+    class WAN22: ...
+    comfy_model_base_mod.ModelType = ModelType
+    comfy_model_base_mod.WAN22 = WAN22
+
+    comfyui_version_mod = types.ModuleType("comfyui_version")
+    comfyui_version_mod.__version__ = comfyui_version
+
+    comfy_mod.utils = comfy_utils_mod
+    comfy_mod.samplers = comfy_samplers_mod
+    comfy_mod.model_base = comfy_model_base_mod
+
+    monkeypatch.setitem(sys.modules, "comfy", comfy_mod)
+    monkeypatch.setitem(sys.modules, "comfy.utils", comfy_utils_mod)
+    monkeypatch.setitem(sys.modules, "comfy.samplers", comfy_samplers_mod)
+    monkeypatch.setitem(sys.modules, "comfy.model_base", comfy_model_base_mod)
+    monkeypatch.setitem(sys.modules, "nodes", types.ModuleType("nodes"))
+    monkeypatch.setitem(sys.modules, "latent_preview", types.ModuleType("latent_preview"))
+    monkeypatch.setitem(sys.modules, "comfyui_version", comfyui_version_mod)
+
+    sys.modules.pop("src.LanPaint.nodes", None)
+    return importlib.import_module("src.LanPaint.nodes")
+
+
+@pytest.mark.parametrize("comfyui_version", ["0.5.0", "0.6.0"])
+def test_reshape_mask_accepts_bhw_and_5d_output_shape(monkeypatch, comfyui_version: str) -> None:
+    lanpaint_nodes = _import_nodes(monkeypatch, comfyui_version)
+    input_mask = torch.zeros((1, 4, 4))
+    output_shape = (1, 16, 1, 8, 8)
+
+    out = lanpaint_nodes.reshape_mask(input_mask, output_shape, video_inpainting=False)
+    assert tuple(out.shape) == output_shape
+
+
+def test_prepare_mask_accepts_hw_and_moves_device(monkeypatch) -> None:
+    lanpaint_nodes = _import_nodes(monkeypatch, "0.5.0")
+    input_mask = torch.zeros((4, 4))
+    output_shape = (2, 3, 8, 8)
+
+    out = lanpaint_nodes.prepare_mask(input_mask, output_shape, device=torch.device("cpu"), video_inpainting=False)
+    assert tuple(out.shape) == output_shape
+    assert out.device.type == "cpu"
@@ -0,0 +1,51 @@
+import torch
+from unittest.mock import MagicMock, patch
+from src.LanPaint.lanpaint import LanPaint
+def test_langevin_dynamics_fallback_on_nan() -> None:
+    """Test that langevin_dynamics falls back to overdamped dynamics if damped dynamics produces NaNs."""
+    torch.manual_seed(0)
+    # Setup minimal LanPaint instance
+    lp = LanPaint(Model=MagicMock(), NSteps=10, Friction=1.0, Lambda=1.0, Beta=1.0, StepSize=0.1)
+    # Dummy inputs
+    # Shape: (Batch, Channel, Height, Width)
+    x_t = torch.randn(1, 4, 8, 8)
+    lp.img_dim_size = 4
+    mask = torch.zeros_like(x_t)
+    # Simple score function
+    def score(x):
+        return torch.zeros_like(x)
+    step_size = torch.tensor([0.1])
+    # (sigma, abt, flow_t)
+    current_times = (torch.tensor([0.5]), torch.tensor([0.5]), torch.tensor([0.5]))
+    # Mock StochasticHarmonicOscillator to return NaNs
+    # We patch it where it is used (imported) in lanpaint.py
+    with patch("src.LanPaint.lanpaint.StochasticHarmonicOscillator") as MockSHO:
+        mock_instance = MockSHO.return_value
+        # Configure dynamics to return NaNs
+        nan_tensor = torch.full_like(x_t, float('nan'))
+        mock_instance.dynamics.return_value = (nan_tensor, nan_tensor)
+        # Execute langevin_dynamics
+        # This should try run_damped -> get NaNs -> raise ValueError -> catch -> run_overdamped
+        x_out, (v_out, C_out) = lp.langevin_dynamics(x_t, score, mask, step_size, current_times, sigma_y=1.0)
+        # Verify that SHO was initialized and dynamics called
+        MockSHO.assert_called()
+        mock_instance.dynamics.assert_called()
+        # Verify result is finite (indicating fallback to overdamped logic was successful)
+        assert torch.isfinite(x_out).all(), "Output contains NaNs, fallback failed"
+        assert v_out is None or torch.isfinite(v_out).all()
+def test_langevin_dynamics_fallback_on_exception() -> None:
+    """Test that langevin_dynamics falls back to overdamped dynamics if damped dynamics raises Exception."""
+    torch.manual_seed(0)
+    lp = LanPaint(Model=MagicMock(), NSteps=10, Friction=1.0, Lambda=1.0, Beta=1.0, StepSize=0.1)
+    x_t = torch.randn(1, 4, 8, 8)
+    lp.img_dim_size = 4
+    mask = torch.zeros_like(x_t)
+    score = lambda x: torch.zeros_like(x)
+    step_size = torch.tensor([0.1])
+    current_times = (torch.tensor([0.5]), torch.tensor([0.5]), torch.tensor([0.5]))
+    with patch("src.LanPaint.lanpaint.StochasticHarmonicOscillator") as MockSHO:
+        mock_instance = MockSHO.return_value
+        # Configure dynamics to raise Exception
+        mock_instance.dynamics.side_effect = RuntimeError("Simulation exploded")
+        x_out, (v_out, C_out) = lp.langevin_dynamics(x_t, score, mask, step_size, current_times, sigma_y=1.0)
+        assert torch.isfinite(x_out).all()
Author	SHA1	Message	Date
scraed	1393a46d67	Bump version from 1.4.9 to 1.4.10	2026-01-13 22:17:42 +08:00
scraed	27421363bf	Merge pull request #71 from godnight10061/fix/issue69-nan-multivariatenormal Avoid MultivariateNormal crash on non-finite dynamics	2026-01-13 22:16:34 +08:00
scraed	01400a541d	Update test_sho_regression.py	2026-01-13 22:13:30 +08:00
scraed	c981354387	Update test_sho_regression.py	2026-01-13 21:46:56 +08:00
scraed	c92c8bfb1f	Update test_sho_regression.py	2026-01-13 21:38:49 +08:00
scraed	9334025801	Update test_sho_regression.py	2026-01-13 21:31:15 +08:00
scraed	144893ecd2	fall back to overdamped update if nan appears	2026-01-13 18:41:27 +08:00
scraed	bce8505ce2	Revert "Avoid MultivariateNormal crash on non-finite dynamics" This reverts commit `9432a34c38`.	2026-01-13 18:29:06 +08:00
godnight10061	0992657e54	Fix CI workflows for torch and default branch	2026-01-13 09:25:41 +08:00
godnight10061	2fa036992b	Add unit regression for NaN oscillator mean	2026-01-13 08:56:46 +08:00
godnight10061	9432a34c38	Avoid MultivariateNormal crash on non-finite dynamics	2026-01-13 01:39:48 +08:00
scraed	d4e8ee28fb	Merge pull request #70 from godnight10061/fix/reshape-mask-3d Thanks for the contribution! Tested and merged.	2026-01-11 18:22:14 +08:00
godnight10061	34b8e83831	Make CI tooling importable	2026-01-11 11:07:41 +08:00
godnight10061	e29d480a1a	Fix reshape_mask for 3D masks	2026-01-10 14:46:14 +08:00
charrywhite	9479106c1d	Fix Flux2 Dev Inpaint workflow size bug	2026-01-10 12:43:40 +08:00
charrywhite	db4bbbe9f2	Update Flux.2.Dev workflow Preview pic	2026-01-10 11:47:29 +08:00
charrywhite	1a3e15964c	Merge branch 'master' of https://github.com/scraed/LanPaint	2026-01-10 11:41:58 +08:00
charrywhite	4cc30f8a1d	Fix Flux2.Dev Inpainting Workflow MaskBlend Bug	2026-01-10 11:41:53 +08:00
scraed	d1e609192f	Bump version from 1.4.8 to 1.4.9	2026-01-06 23:13:59 +08:00
charrywhite	bf46831196	Update README.md	2026-01-06 23:01:04 +08:00
charrywhite	c9d9a79b18	Update README.md	2026-01-06 22:59:21 +08:00
charrywhite	175156af86	Add Flux.2 Dev support	2026-01-06 22:57:42 +08:00
charrywhite	3281946b32	Merge branch 'master' of https://github.com/scraed/LanPaint	2026-01-06 22:42:16 +08:00
charrywhite	83f557ff4c	Support Flux.2 Dev Inpainting	2026-01-06 22:42:07 +08:00
charrywhite	5e7fe4d5e4	Enhance README with new Discord info and features Updated README to highlight new Discord channel and features.	2026-01-06 21:06:25 +08:00
scraed	69f2aded4d	Update README with Discord link and new features Added Discord link for community engagement and announced new inpainting and outpainting features.	2026-01-06 20:02:15 +08:00
scraed	5f1b6d8989	add discord link	2026-01-06 20:01:41 +08:00
scraed	7b0d144db9	Update Qwen Image Edit 2511 support Updated references for Qwen Image Edit to include version 2511 alongside 2509.	2025-12-28 18:40:41 +08:00
scraed	27ebb6e7af	fix qwen edit shape error	2025-12-25 23:18:51 +08:00
charrywhite	f148e4b631	Update paper title in README.md	2025-12-17 22:33:46 +08:00
charrywhite	cc9ec8873a	Revise citation and add TMLR link Updated citation format and added TMLR link.	2025-12-17 22:30:06 +08:00
scraed	565087e8f8	fix z image resize error for inpaint	2025-12-15 18:15:15 +08:00
scraed	ebdc5dcc02	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-12-06 10:26:06 +08:00
scraed	9336523576	more clear bug report	2025-12-06 10:26:01 +08:00
scraed	21520b38fb	Update README.md	2025-12-05 09:39:13 +08:00
scraed	868bbaeefe	fix detail display	2025-12-04 15:01:50 +08:00
scraed	3581d6c217	outpaint example	2025-12-04 14:58:16 +08:00
scraed	d4da280a92	update version	2025-12-04 14:37:10 +08:00
scraed	d0bcd459cc	update version	2025-12-04 14:20:58 +08:00
scraed	469012d183	Z image update	2025-12-04 14:17:29 +08:00
scraed	3f4a5cdb5a	update pyproject.yaml	2025-11-23 01:11:08 +08:00
scraed	e0ce52b1be	fix blur issue in Wan 2.2 14B	2025-11-23 00:46:25 +08:00
scraed	2c8f4baec0	add wan 2.2 5B	2025-11-17 16:09:45 +08:00
scraed	daf1bdf4be	update version	2025-11-17 16:05:42 +08:00
scraed	f4d4dfdd61	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-11-17 16:04:51 +08:00
scraed	6d624e7e1e	support Wan 2.2 5B	2025-11-17 16:04:48 +08:00
scraed	e0940059e6	Update README.md	2025-11-10 13:37:49 +08:00
scraed	76ce20b51f	version change	2025-10-26 23:29:23 +08:00
scraed	29ed0b3da5	Add kwargs to CFGGuider_LanPaint sampling methods Updated the outer_sample and inner_sample method calls in CFGGuider_LanPaint to accept and forward arbitrary keyword arguments (kwargs). This allows for greater flexibility and compatibility with future changes or additional parameters.	2025-10-26 23:28:35 +08:00
scraed	3d5aa3a3fa	Add Hunyuan supports Added new example workflows for Hunyuan, including images and JSON workflow files. Updated README with Hunyuan T2I inpainting instructions and links. Increased default LanPaint step size to 0.2 in nodes and advanced sampler, and refactored scale_latent_inpaint logic in lanpaint.py for improved compatibility.	2025-10-16 15:59:13 +08:00
charrywhite	6109df6591	Upload a 41frames outpaint workflows	2025-10-03 22:32:19 +08:00
scraed	536716a7b9	typo fix	2025-10-03 09:59:01 +08:00
scraed	ece228ab10	Bump version to 1.4.0 in pyproject.toml Updated the project version from 1.3.3 to 1.4.0 to prepare for a new release.	2025-10-02 23:24:21 +08:00
charrywhite	c048230250	Update Original Video (Fix to 40 frames, fps=12)	2025-10-02 21:48:44 +08:00
charrywhite	aa885d9fa6	Update README.md	2025-10-02 21:33:37 +08:00
charrywhite	80d55a47b0	Add 81 frames example	2025-10-02 21:31:19 +08:00
charrywhite	79db1765e3	Add video inpainting (Beta)	2025-10-02 21:13:43 +08:00
charrywhite	929328d08d	Update README.md	2025-09-27 00:42:43 +08:00
scraed	c12897040b	Update README with inpainting result image Replaced the workflow & masks link with an inpainting result image to better showcase the output of the project.	2025-09-24 18:49:34 +08:00
scraed	48b9a59ca3	Update README with new model support and examples Added references to Wan2.2 and Qwen Image Edit 2509 support, and included a link to workflow and mask examples for improved documentation.	2025-09-24 18:48:46 +08:00
scraed	d55f8c61d0	Add Qwen Image Edit 2509 example to README Introduced a new section for Qwen Image Edit 2509 inpainting example, including workflow and model download links, and an example image. Updated the table of contents to reference the new example.	2025-09-24 18:43:41 +08:00
scraed	a097b9c9b2	Update LanPaintQwen_04.jpg example image Replaces the existing LanPaintQwen_04.jpg in the examples directory with a new version. This may reflect updated content or improved image quality.	2025-09-24 18:40:25 +08:00
scraed	70cc94cc6a	update readme	2025-09-24 18:30:04 +08:00
scraed	e653f45103	Support 2509	2025-09-24 18:27:11 +08:00
scraed	c9c237c2de	Add WAN 2.2 partial inpaint workflow template	2025-09-18 16:55:48 +08:00
scraed	c5aa0116d5	Add Example 16 for partial inpainting with reference Added a new example (Example 16) demonstrating partial inpainting using reference images. Updated README with instructions and links for the new example, and added related images to the examples directory.	2025-09-18 15:40:22 +08:00
scraed	1820d88594	fix link	2025-09-17 15:59:35 +08:00
scraed	c265bcc989	Add Wan2.2 inpainting example and bump version Added a new example for Wan2.2 inpainting with image and workflow links in the README. Updated project version to 1.3.2 in pyproject.toml.	2025-09-17 15:55:28 +08:00
scraed	b8973c570b	Support Wan 2.2 Txt 2 Img	2025-09-17 15:46:11 +08:00
scraed	1b50a90424	qwen edit resize fix	2025-09-07 13:07:17 +08:00
charrywhite	dab41249da	Update README.md	2025-08-30 11:15:11 +08:00
charrywhite	b2b2b21bf1	Update README.md	2025-08-30 11:14:53 +08:00
scraed	099d4137a9	Clarify speed improvement suggestions Reworded instructions for boosting speed in README.	2025-08-26 12:10:11 +08:00