666 version code

Fix Flux2 Dev Inpaint workflow size bug
Update Flux.2.Dev workflow Preview pic
2026-02-02 21:36:01 +08:00 · 2026-01-10 12:43:40 +08:00 · 2026-01-10 11:47:29 +08:00 · 2026-01-10 11:41:58 +08:00 · 2026-01-10 11:41:53 +08:00 · 2026-01-06 23:13:59 +08:00
92 changed files with 14466 additions and 127 deletions
@@ -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/"
+  ],
 }
@@ -1,29 +1,104 @@
-# LanPaint (Thinking mode Inpaint)
+<div align="center">

-Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, aiming for seamless and accurate results. 
-![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
+# LanPaint: Universal Inpainting Sampler with "Think Mode"
+[![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>

-This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). 
+
+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 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).
+
+## Citation
+
+```
+@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!**
+
+[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)
+- [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](#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-)
+- [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, 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.  
 - **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.  
 - **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.  
 - **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters. 

-## How It Works  
-LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results.
-
-LanPaint introduces "BIG score" that creates a **two-way alignment** between masked and unmasked areas. It continuously evaluates:  
- *"Does the new content make sense with the existing elements?"*  
- *"Do the existing elements support the new creation?"*  
-Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions.  
-  
-LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.
-
+**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).

 ## Quickstart

@@ -41,38 +116,232 @@ LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.
 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!


+## **How to Use Examples:**  
+1. Navigate to the **example** folder (i.e example_1), download all pictures.  
+2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
+3. Download the required model (i.e clicking **Model Used in This Example**).  
+4. Load the model in ComfyUI.
+5. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left), or the **Prepare Image** node.  
+7. Queue the task, you will get inpainted results from LanPaint. Some example also gives you inpainted results from the following methods for comparison:
+   - **[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)**

-## Updates
- 2025/06/04
-    - Add more sampler support.
-    - Add early stopping to advanced sampler.
- 2025/05/28
-    - Major update on the Langevin solver. It is now much faster and more stable.
-    - Greatly simplified the parameters for advanced sampler.
-    - Fix performance issue on Flux and SD 3.5
- 2025/04/16
-    - Added Primary HiDream support
- 2025/03/22
-    - Added Primary Flux support
-    - Added Tease Mode
- 2025/03/10
-    - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
+## Video Examples (Beta)

-## Examples
-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.)
+LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly inpaint masked regions across video frames while maintaining temporal consistency.

-### Example HiDream: InPaint(LanPaint K Sampler, 5 steps of thinking)
+**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)
+
+![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)
+
+
+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.
+
+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)

 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.

+### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
+![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13(1).jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
+
+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. Thanks [Amazon90](https://github.com/Amazon90) for providing this example.
+
 ### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
 ![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_12.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)

 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)
@@ -115,23 +384,6 @@ You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki
 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).


-## **How to Use These Examples:**  
-1. Navigate to the **example** folder (i.e example_1) by clicking **View Workflow & Masks**, download all pictures.  
-2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
-3. Download the required model from Civitai by clicking **Model Used in This Example**.  
-4. Load the model into the **"Load Checkpoint"** node.  
-5. Upload **Original_No_Mask.png** to the **"Load image"** node in the **"Original Image"** group (far left).  
-6. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left).  
-7. Queue the task, you will get inpainted results from three methods:  
-   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)** (middle),  
-   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)** (second from right),  
-   - **LanPaint** (far right).  
-
-Compare and explore the results from each method!
-
-![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG)  
-
-
 ## Usage

 **Workflow Setup**  
@@ -166,20 +418,22 @@ Full parameter control:
 | `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. |
 | `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 |
 | `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|
+| `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5|
 | `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. |

 For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.

-
+### LanPaint Mask Blend
+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.

 ## LanPaint KSampler (Advanced) Tuning Guide
 For challenging inpainting tasks:  

 1️⃣ **Boost Quality**
-Increase total number of sampling steps, **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
+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.
    
@@ -192,26 +446,62 @@ If you find the results have wired texture, try
 ⚠️ **Notes**:  
 - 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.

+## Community Showcase [](#community-showcase-)
+
+Discover how the community is using LanPaint! Here are some user-created tutorials:
+
+- [Ai绘画进阶148-三大王炸！庆祝高允贞出道6周年！T8即将直播？当AI绘画学会深度思考？！万能修复神器LanPaint，万物皆可修！-T8 Comfyui教程](https://www.youtube.com/watch?v=Z4DSTv3UPJo)
+- [Ai绘画进阶151-真相了！T8竟是个AI？！LanPaint进阶（二），人物一致性，多视角实验性测试，新参数讲解，工作流分享-T8 Comfyui教程](https://www.youtube.com/watch?v=landiRhvF3k)
+- [重绘和三视图角色一致性解决新方案！LanPaint节点尝试](https://www.youtube.com/watch?v=X0WbXdm6FA0)
+- [ComfyUI: HiDream with Perturbation Upscale, LanPaint Inpainting (Workflow Tutorial)](https://www.youtube.com/watch?v=2-mGe4QVIIw&t=2785s)
+- [ComfyUI必备LanPaint插件超详细使用教程](https://plugin.aix.ink/archives/lanpaint)
+
+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
+- 2025/06/21
+    - Update the algorithm with enhanced stability and outpaint performance.
+    - Add outpaint example
+    - Supports Sampler Custom (Thanks to [MINENEMA](https://github.com/MINENEMA))
+- 2025/06/04
+    - Add more sampler support.
+    - Add early stopping to advanced sampler.
+- 2025/05/28
+    - Major update on the Langevin solver. It is now much faster and more stable.
+    - Greatly simplified the parameters for advanced sampler.
+    - Fix performance issue on Flux and SD 3.5
+- 2025/04/16
+    - Added Primary HiDream support
+- 2025/03/22
+    - Added Primary Flux support
+    - Added Tease Mode
+- 2025/03/10
+    - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
+- 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).
+
 ## ToDo
 - Try Implement Detailer
- Provide inference code on without GUI.
-
-## Contribute
-
- 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).
+- ~~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={}
 }
 ```

@@ -219,4 +509,3 @@ If you find the results have wired texture, try



-
@@ -0,0 +1,786 @@
+{
+  "id": "978d3a45-3d13-43c6-8ef9-89dc3e74d6ba",
+  "revision": 0,
+  "last_node_id": 82,
+  "last_link_id": 220,
+  "nodes": [
+    {
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+      "type": "SetLatentNoiseMask",
+      "pos": [
+        514.1915893554688,
+        781.9396362304688
+      ],
+      "size": [
+        264.5999755859375,
+        46
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
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+          "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": []
+    },
+    {
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+      "type": "CLIPTextEncode",
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+        255.63235473632812
+      ],
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+        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"
+    },
+    {
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+      "type": "CLIPTextEncode",
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+      },
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+        {
+          "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",
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+        {
+          "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": [
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+      "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"
+      ]
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+    {
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+      "type": "LanPaint_KSampler",
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+        {
+          "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,
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+        "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"
+      ]
+    },
+    {
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+      "type": "VAEEncode",
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+        {
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+          "type": "VAE",
+          "link": 203
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+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
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+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23"
+      },
+      "widgets_values": [
+        "ComfyUI"
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+          "type": "IMAGE",
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+      "properties": {
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+          "type": "IMAGE",
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+        "Node name for S&R": "LoadImage"
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+        "image"
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+  ],
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+          "type": "CONDITIONING",
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+        "image"
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+        {
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+          "type": "LATENT",
+          "link": 186
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+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            187
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+        }
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+        1,
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+        "Image First",
+        "LanPaint KSampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!"
+      ]
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+          "link": 189
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+        {
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+          "type": "IMAGE",
+          "links": [
+            188
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+      "properties": {
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+        "ver": "4d3d5d17f0105b673df92da5b084cce567c9c712",
+        "Node name for S&R": "LanPaint_MaskBlend"
+      },
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+      "type": "SetLatentNoiseMask",
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+          "link": 192
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+        {
+          "name": "mask",
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+          "link": 155
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            186
+          ]
+        }
+      ],
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+        "ver": "0.3.23",
+        "Node name for S&R": "SetLatentNoiseMask"
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+        {
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+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
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+        }
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+    {
+      "id": 2,
+      "title": "Convert Latents for LanPaint",
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+    {
+      "id": 4,
+      "title": "Inpaint with the LanPaint KSampler",
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+    {
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+      "title": "LanPaint OutPut",
+      "bounding": [
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+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 13,
+      "title": "Paste back the original to preserve it exactly, if you want",
+      "bounding": [
+        1491.586181640625,
+        984.3547973632812,
+        749.66455078125,
+        637.360595703125
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.6209213230591556,
+      "offset": [
+        850.0803535605011,
+        86.6432096141053
+      ]
+    },
+    "frontendVersion": "1.25.10",
+    "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 = "1.0.2"
+version = "1.4.9"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
  {name = "LanPaint", email = "czhengac@connect.ust.hk"}
@@ -1,6 +1,7 @@
 import torch
 from .utils import *
 from functools import partial
+
 class LanPaint():
    def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False):
        self.n_steps = NSteps
@@ -11,10 +12,22 @@ class LanPaint():
        self.inner_model = Model
        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)
+        return array[index]
+    def remove_none_dims(self, array):
+        # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+        index = (slice(None),) + (0,) * (self.img_dim_size-1)
+        return array[index]
    def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None):
+        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)
@@ -23,26 +36,31 @@ class LanPaint():

        
        step_size = self.step_size * (1 - abt)
-        step_size = step_size[:, None, None, None]
+        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 * ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 )
+            x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
        else:
-            x_t = x / ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values
+            x_t = x / ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values

        ############ LanPaint Iterations Start ###############
        # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
        args = None
        for i in range(n_steps):
-            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 )
-            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)  
+            score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = self.add_none_dims(abt), sigma = self.add_none_dims(VE_Sigma), tflow = self.add_none_dims(Flow_t), model_options = model_options, seed = seed )
+            x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)  
        if IS_FLUX or IS_FLOW:
-            x = x_t / ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 )
+            x = x_t / ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
        else:
-            x = x_t * ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values
+            x = x_t * ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
        ############ LanPaint Iterations End ###############
        # out is x_0
        out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
@@ -52,14 +70,13 @@ class LanPaint():
    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.
            x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching
-            x_0, x_0_BIG = self.inner_model(x, tflow[:, 0,0,0], model_options=model_options, seed=seed)
+            x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
        else:
            x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding
-            x_0, x_0_BIG = self.inner_model(x, sigma[:, 0,0,0], model_options=model_options, seed=seed)
+            x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)

        score_x = -(x_t - x_0)
        score_y =  - (1 + lamb) * ( x_t - y )  + lamb * (x_t - x_0_BIG)  
@@ -82,33 +99,52 @@ class LanPaint():
        # -------------------------------------------------------------------------
        # Compute the Langevin dynamics update in variance perserving notation
        # -------------------------------------------------------------------------
-        x0 = self.x0_evalutation(x_t, score, sigma, args)
-        C = abt**0.5 * x0 / (1-abt)
+        #x0 = self.x0_evalutation(x_t, score, sigma, args)
+        #C = abt**0.5 * x0 / (1-abt)
        A = A_x * (1-mask) + A_y * mask
        D = D_x * (1-mask) + D_y * mask
        dt = dtx * (1-mask) + dty * mask
        Gamma = Gamma_x * (1-mask) + Gamma_y * mask


-
+        def Coef_C(x_t):
+            x0 = self.x0_evalutation(x_t, score, sigma, args)
+            C = (abt**0.5 * x0  - x_t )/ (1-abt) + A * x_t
+            return C
+        def advance_time(x_t, v, dt, Gamma, A, C, D):
+            dtype = x_t.dtype
+            with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+                osc = StochasticHarmonicOscillator(Gamma, A, C, D )
+                x_t, v = osc.dynamics(x_t, v, dt )
+            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, = args
+            v, C = args

-        with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
-            osc = StochasticHarmonicOscillator(Gamma, A, C, D )
-            x_t, v = osc.dynamics(x_t, v, dt )
+            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,)
+        return x_t, (v, C)

    def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
        # -------------------------------------------------------------------------
        # Unpack current times parameters (sigma and abt)
        sigma, abt, flow_t = current_times
-        sigma = sigma[:, None,None,None]
-        abt = abt[:, None,None,None]
+        sigma = self.add_none_dims(sigma)
+        abt = self.add_none_dims(abt)
        # Compute time step (dtx, dty) for x and y branches.
        dtx = 2 * step_size * sigma_x
        dty = 2 * step_size * sigma_y
@@ -123,9 +159,9 @@ class LanPaint():
        # adjust dt to match denoise-addnoise steps sizes
        Gamma_hat_x /= 2.
        Gamma_hat_y /= 2.
-
        A_t_x = (1) / ( 1 - abt ) * dtx / 2
-        A_t_y =  (1) / ( 1 - abt ) * dty / 2
+        A_t_y =  (1+self.chara_lamb) / ( 1 - abt ) * dty / 2
+

        A_x = A_t_x / (dtx/2)
        A_y = A_t_y / (dty/2)
@@ -11,21 +11,115 @@ from comfy.samplers import *
 from comfy.model_base import ModelType
 from .utils import *
 from .lanpaint import LanPaint
-# Monkey patch comfy.samplers module by importing with absolute package path
-#exec(inspect.getsource(comfy.samplers).replace("from .", "from comfy."))
+from comfy.model_base import WAN22
+import comfyui_version 
+import comfy.nested_tensor 

-def reshape_mask(input_mask, output_shape):
+def reshape_mask(input_mask, output_shape,video_inpainting=False):
+
+    import comfy.nested_tensor
+    
+    # 修改这里的判断条件，不能只用 hasattr("unbind")
+    if isinstance(input_mask, comfy.nested_tensor.NestedTensor):
+        masks = input_mask.unbind()
+        
+        # 如果 output_shape 也是嵌套的（通常 noise.shape 在 NestedTensor 下返回 tuple of shapes）
+        if isinstance(output_shape, (list, tuple)) and len(output_shape) > 0 and not isinstance(output_shape[0], int):
+            reshaped_parts = []
+            for i in range(len(masks)):
+                # 递归处理每一个子部分，并传入对应的子 shape
+                reshaped_parts.append(reshape_mask(masks[i], output_shape[i], video_inpainting))
+            return comfy.nested_tensor.NestedTensor(tuple(reshaped_parts))
+        else:
+            # 如果 output_shape 是单一形状（降级处理）
+            return comfy.nested_tensor.NestedTensor(tuple(reshape_mask(m, output_shape, video_inpainting) for m in masks))
+        
    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 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])
+    if video_inpainting:
+        # 如果是 3D Token 序列 (LTXV 压平后的情况)
+        if input_mask.ndim == 3 and len(output_shape) == 3:
+            mask = torch.nn.functional.interpolate(
+                input_mask, 
+                size=output_shape[2], 
+                mode=scale_mode
+            )
+            return mask
+            
+        # 只有在确认为 5D 视频张量时才执行原有逻辑
+        if input_mask.ndim == 5:
+            target_frames = output_shape[2]
+            target_height, target_width = output_shape[-2:]
+            
+            # (这里保留你原有的 permute 和 unsqueeze 逻辑，但要确保它是针对非 5D 输入的补救)
+            if input_mask.ndim < 5:
+                # 假设输入是 (F, C, H, W) -> (1, C, F, H, W)
+                if hasattr(comfyui_version, "__version__") and comfyui_version.__version__ < "0.6.0":
+                    input_mask = input_mask.permute(1, 0, 2, 3).unsqueeze(0)
+            
+            # 现在可以安全地解包 5D 形状了
+            batch_size, channels, frames, height, width = input_mask.shape
+            mask = torch.nn.functional.interpolate(
+                input_mask, 
+                size=(target_frames, target_height, target_width), 
+                mode=scale_mode, 
+            )
+            
+            if mask.shape[1] < output_shape[1]:
+                mask = mask.repeat(1, output_shape[1], 1, 1, 1)[:, :output_shape[1]]
+            mask = repeat_to_batch_size(mask, output_shape[0])
+            return mask
+    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
@@ -44,13 +138,18 @@ 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)
+            print('denoise_mask',denoise_mask.shape,type(denoise_mask))
+            denoise_mask = prepare_mask(denoise_mask, noise.shape, device, video_inpainting)

        noise = noise.to(device)
        latent_image = latent_image.to(device)
@@ -59,7 +158,7 @@ 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()

@@ -77,8 +176,8 @@ class KSamplerX0Inpaint:
    def __init__(self, model, sigmas):
        self.inner_model = model
        self.sigmas = sigmas
-        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) ) )
-        self.model_sigmas = torch.tensor( self.model_sigmas, dtype = self.sigmas.dtype )
+        #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) ) )
+        #self.model_sigmas = torch.tensor( self.model_sigmas, dtype = self.sigmas.dtype )
    def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None,**kwargs):
        ### For 1.5 and XL model
        # x is x_t in the notation of variance exploding diffusion model, x_t = x_0 + sigma * noise
@@ -88,15 +187,15 @@ 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 
            abt = 1/( 1+VE_Sigma**2 )
@@ -106,6 +205,31 @@ class KSamplerX0Inpaint:
            if "denoise_mask_function" in model_options:
                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})

+            if isinstance(denoise_mask, comfy.nested_tensor.NestedTensor):
+                masks = denoise_mask.unbind()
+                xs = x.unbind()
+                latent_imgs = self.latent_image.unbind()
+                noises = self.noise.unbind()
+                
+                outs = []
+                # 针对 LTXV，通常 i=0 是视频，i=1 是音频
+                for i in range(len(xs)):
+                    m = (masks[i] > 0.5).float()
+                    lm = 1 - m
+                    # 这里的 PaintMethod 通常只支持普通 Tensor，所以我们分块处理
+                    # 注意：如果音频部分不需要 Inpaint，可以增加判断
+                    current_times = (VE_Sigma, abt, Flow_t)
+                    
+                    # 只有视频部分 (i=0) 应用 LanPaint 逻辑，音频部分通常直接 pass 或原样返回
+                    if i == 0:
+                        out_part = self.PaintMethod(xs[i], latent_imgs[i], noises[i], sigma, lm, current_times, model_options, seed)
+                    else:
+                        # 音频部分如果没有对应的 Inpaint 逻辑，通常直接调用 inner_model
+                        out_part, _ = self.inner_model(xs[i], sigma, model_options=model_options, seed=seed)
+                    outs.append(out_part)
+                    
+                return comfy.nested_tensor.NestedTensor(tuple(outs))
+            
            denoise_mask = (denoise_mask > 0.5).float()

            latent_mask = 1 - denoise_mask
@@ -114,7 +238,7 @@ class KSamplerX0Inpaint:
            current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) )
            total_steps = len(self.sigmas)-1

-            if total_steps - current_step < self.LanPaint_early_stop:
+            if total_steps - current_step <= self.LanPaint_early_stop:
                out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed, n_steps=0)
            else:
                out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed)
@@ -140,6 +264,7 @@ class KSAMPLER(comfy.samplers.KSAMPLER):
        #noise here is a randn noise from comfy.sample.prepare_noise
        #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.
        extra_args["denoise_mask"] = denoise_mask
+        print("LanPaint KSampler start sampler_function",denoise_mask.shape if denoise_mask is not None else None)
        model_k = KSamplerX0Inpaint(model_wrap, sigmas)
        model_k.latent_image = latent_image
        if self.inpaint_options.get("random", False): #TODO: Should this be the default?
@@ -247,6 +372,7 @@ 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"}),
                  }
        }

@@ -257,11 +383,11 @@ 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_Lambda = 8.0
-        model.LanPaint_Beta = 1.0
+        model.LanPaint_StepSize = 0.2
+        model.LanPaint_Lambda = 16.0
+        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
        model.LanPaint_Friction = 15.
        model.LanPaint_EarlyStop = 1
@@ -269,6 +395,13 @@ 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:
@@ -289,13 +422,14 @@ class LanPaint_KSamplerAdvanced:
                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                    "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": 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."}),
-                "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_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": 0.2, "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"}),
                     },
                }

@@ -304,7 +438,7 @@ 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
@@ -321,9 +455,309 @@ class LanPaint_KSamplerAdvanced:
            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, 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:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE", {"tooltip": "Image before inpaint"}),
+                "image2": ("IMAGE", {"tooltip": "Image after inpaint"}),
+                "mask": ("MASK",),
+                "blend_overlap": ("INT", {"default": 1, "min": 1, "max": 51, "step": 2, "tooltip": "The number of pixels to blend between the two images."})
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "blend_images"
+
+    CATEGORY = "image/postprocessing"
+
+    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, mask: torch.Tensor, blend_overlap: int):
+        # smooth the binary 01 mask, keep 1 still 1, but smooth the transition from 1 to 0
+        # 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(
+                "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,:,:]
+
+
+        blended_image = image1 * (1 - mask[...,None]) + image2 * mask[...,None]
+        return (blended_image,)
+    def gaussian_kernel(self,kernel_size):
+        """
+        Creates a 2D Gaussian kernel with the given size and standard deviation (sigma).
+        """
+        sigma = (kernel_size - 1)/4
+        # Create a grid of (x, y) coordinates
+        x = torch.arange(kernel_size).float() - kernel_size // 2
+        y = torch.arange(kernel_size).float() - kernel_size // 2
+        x_grid, y_grid = torch.meshgrid(x, y, indexing='ij')
+
+        # Compute the Gaussian function
+        kernel = torch.exp(-(x_grid ** 2 + y_grid ** 2) / (2 * sigma ** 2))
+        kernel = kernel / kernel.sum()  # Normalize the kernel
+
+        return kernel
+
+class MaskBlendAlpha:
+    """
+    Create an RGBA image by writing the mask into the PNG alpha channel.
+
+    Requirement:
+    - inpaint region: alpha = 0 (transparent)
+    - other region:   alpha = 1 (opaque)
+
+    This node writes the mask into the PNG alpha channel.
+    Current default behavior matches the previous `invert_mask=True` behavior:
+    alpha = mask.
+    """
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", {"tooltip": "VAE-decoded image (RGB)."}),
+                "mask": ("MASK", {"tooltip": "Mask used as alpha channel (alpha = mask)."}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "to_rgba"
+    CATEGORY = "image/postprocessing"
+
+    def to_rgba(self, image: torch.Tensor, mask: torch.Tensor):
+        """
+        image: [B,H,W,3] float in [0,1]
+        mask:  [B,H,W] (or [H,W]) float in [0,1] used as alpha
+        returns RGBA image: [B,H,W,4] float in [0,1]
+        """
+        if image.ndim != 4 or image.shape[-1] != 3:
+            raise ValueError(f"Expected IMAGE tensor [B,H,W,3], got {tuple(image.shape)}")
+
+        # Normalize mask shape to [B,H,W]
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        elif mask.ndim == 3:
+            pass
+        else:
+            # Some pipelines may carry mask as [B,1,H,W]
+            if mask.ndim == 4 and mask.shape[1] == 1:
+                mask = mask[:, 0, :, :]
+            else:
+                raise ValueError(f"Expected MASK tensor [B,H,W] or [H,W], got {tuple(mask.shape)}")
+
+        b, h, w, _ = image.shape
+
+        # Batch align
+        if mask.shape[0] != b:
+            if mask.shape[0] == 1:
+                mask = mask.repeat(b, 1, 1)
+            else:
+                raise ValueError(f"Batch mismatch: image batch={b}, mask batch={mask.shape[0]}")
+
+        # Spatial align (resize mask to image resolution if needed)
+        if mask.shape[1] != h or mask.shape[2] != w:
+            mask_4d = mask.unsqueeze(1)  # [B,1,H,W]
+            mask_4d = torch.nn.functional.interpolate(mask_4d, size=(h, w), mode="nearest")
+            mask = mask_4d[:, 0, :, :]
+
+        mask = mask.float().clamp(0.0, 1.0)
+
+        # Default behavior (matches previous invert_mask=True path):
+        # alpha = mask
+        rgba = torch.cat([image, mask.unsqueeze(-1)], dim=-1)
+        return (rgba,)
+
+class Noise_EmptyNoise:
+    def generate_noise(self, latent):
+        return torch.zeros_like(latent["samples"])
+
+class Noise_RandomNoise:
+    def __init__(self, seed):
+        self.seed = seed
+    def generate_noise(self, latent):
+        torch.manual_seed(self.seed)
+        return torch.randn_like(latent["samples"])
+
+# Custom sampler implementation mimmicking base comfy nodes_custom_sampler.py
+class LanPaint_SamplerCustom:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "add_noise": ("BOOLEAN", {"default": True}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                     "positive": ("CONDITIONING",),
+                     "negative": ("CONDITIONING",),
+                     "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_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
+                     "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler. 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"}),
+                      }
+               }
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("output", "denoised_output")
+    FUNCTION = "sample"
+    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="",Inpainting_mode="🖼️ Image Inpainting"):
+        model.LanPaint_StepSize = 0.2
+        model.LanPaint_Lambda = 16.0
+        model.LanPaint_Beta = 1.
+        model.LanPaint_NumSteps = LanPaint_NumSteps
+        model.LanPaint_Friction = 15.
+        model.LanPaint_EarlyStop = 1
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = cfg
+        else:
+            model.LanPaint_cfg_BIG = 0 * cfg - 0.5
+        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():
+            latent = latent_image.copy()
+            latent_image = latent["samples"]
+            latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+            latent["samples"] = latent_image
+
+            if not add_noise:
+                noise = Noise_EmptyNoise().generate_noise(latent)
+            else:
+                noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+            noise_mask = None
+            if "noise_mask" in latent:
+                noise_mask = latent["noise_mask"]
+
+            x0_output = {}
+            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+            disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+            samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+            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())
+            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", ),
+                     "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": 0.2, "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."}),
+                     "LanPaint_EarlyStop": ("INT", {"default": 1, "min": 0, "max": 10000, "tooltip": "Steps to stop LanPaint early, preventing irregular patterns."}),
+                     "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler 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"}),
+                    }
+               }
+
+    RETURN_TYPES = ("LATENT","LATENT")
+    RETURN_NAMES = ("output", "denoised_output")
+
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling/custom_sampling"
+
+    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="",Inpainting_mode="🖼️ Image Inpainting"):
+        model = guider.model_patcher
+        model.LanPaint_StepSize = LanPaint_StepSize
+        model.LanPaint_Lambda = LanPaint_Lambda
+        model.LanPaint_Beta = LanPaint_Beta
+        model.LanPaint_NumSteps = LanPaint_NumSteps
+        model.LanPaint_Friction = LanPaint_Friction
+        model.LanPaint_EarlyStop = LanPaint_EarlyStop
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = guider.cfg
+        else:
+            model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
+        
+        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():
+            latent = latent_image
+            latent_image = latent["samples"]
+            print('before fix_empty_latent_channels latent_image shape',latent_image.shape)
+            latent = latent.copy()
+            latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+            latent["samples"] = latent_image
+            print('latent_image shape',latent_image.shape)
+            print('outside noise_mask',latent["noise_mask"].shape if "noise_mask" in latent else 'no noise_mask')
+            print('latent keys',latent.keys())
+            noise_mask = None
+            if "noise_mask" in latent:
+                noise_mask = latent["noise_mask"]
+                print('inside noise_mask shape',noise_mask.shape)
+
+            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, 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"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+            else:
+                out_denoised = out
+            # print('output',out.keys(),out["samples"].shape,out['noise_mask'].shape)
+            return (out, out_denoised)


 # A dictionary that contains all nodes you want to export with their names
@@ -331,6 +765,10 @@ class LanPaint_KSamplerAdvanced:
 NODE_CLASS_MAPPINGS = {
    "LanPaint_KSampler": LanPaint_KSampler,
    "LanPaint_KSamplerAdvanced": LanPaint_KSamplerAdvanced,
+    "LanPaint_SamplerCustom" : LanPaint_SamplerCustom,
+    "LanPaint_SamplerCustomAdvanced" : LanPaint_SamplerCustomAdvanced,
+    "LanPaint_MaskBlend": MaskBlend,
+    "LanPaint_MaskBlendAlpha": MaskBlendAlpha,
 #    "LanPaint_UpSale_LatentNoiseMask": LanPaint_UpSale_LatentNoiseMask,
 }

@@ -338,5 +776,9 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
    "LanPaint_KSampler": "LanPaint KSampler",
    "LanPaint_KSamplerAdvanced": "LanPaint KSampler (Advanced)",
+    "LanPaint_SamplerCustom" : "LanPaint Sampler Custom",
+    "LanPaint_SamplerCustomAdvanced" : "LanPaint Sampler Custom (Advanced)",
+    "LanPaint_MaskBlend": "LanPaint Mask Blend",
+    "LanPaint_MaskBlendAlpha": "MaskBlend (alpha)",
 #    "LanPaint_UpSale_LatentNoiseMask": "LanPaint UpSale Latent Noise Mask"
 }
@@ -240,7 +240,6 @@ class StochasticHarmonicOscillator:
            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
@@ -253,8 +252,8 @@ class StochasticHarmonicOscillator:
        EE = 1 - Gamma_hat * zeta_2

        if v0 is None:
-            #v0 = torch.randn_like(y0) * D / 2 ** 0.5
-            v0 = (C - A * y0)/Gamma**0.5
+            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
@@ -273,16 +272,30 @@ class StochasticHarmonicOscillator:
        cov_matrix[..., 0, 1] = cov_yv
        cov_matrix[..., 1, 0] = cov_yv  # symmetric
        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)
+        tol = 1e-8
+        cov_yy = torch.clamp( cov_yy, min = tol )
+        sd_yy = torch.sqrt( cov_yy )
+        inv_sd_yy = 1/(sd_yy)
+
+        scale_tril[..., 0, 0] = sd_yy
+        scale_tril[..., 0, 1] = 0.
+        scale_tril[..., 1, 0] = cov_yv * inv_sd_yy
+        scale_tril[..., 1, 1] = torch.clamp( cov_vv - cov_yv**2 / cov_yy, min = tol ) ** 0.5
+        # check if it matches torch.linalg.
+        #assert torch.allclose(torch.linalg.cholesky(cov_matrix), scale_tril, atol = 1e-4, rtol = 1e-4 )
        # Sample correlated noise from multivariate normal
        mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype)
        mean[..., 0] = y_mean
        mean[..., 1] = v_mean
        new_yv = torch.distributions.MultivariateNormal(
            loc=mean,
-            covariance_matrix=cov_matrix
+            scale_tril=scale_tril
        ).sample()

-        return new_yv[...,0], new_yv[...,1]
-
-
+        return new_yv[...,0], new_yv[...,1]
Author	SHA1	Message	Date
charrywhite	111f9fdffb	666 version code	2026-02-02 21:36:01 +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
scraed	2f7967a584	update version	2025-08-25 11:37:54 +08:00
scraed	e28e115297	add example workflow jsons	2025-08-25 11:37:31 +08:00
scraed	f2cc70fed3	Update inpainted example image Replaces the InPainted_Drag_Me_to_ComfyUI.png file in Example_14 with a new version. This may reflect updated visual content or corrections to the example image.	2025-08-25 11:11:09 +08:00
scraed	b8e140f7aa	Comment out unused model_sigmas initialization The initialization of model_sigmas in KSamplerX0Inpaint was commented out to remove warning. Also remove group nodes from example workflow	2025-08-22 10:34:51 +08:00
scraed	e6f61e806c	Bump version to 1.3.0 in pyproject.toml Updated the project version from 1.2.0 to 1.3.0 to reflect new changes or improvements in the LanPaint package.	2025-08-22 00:26:46 +08:00
scraed	ba506f2654	Add new Qwen edit example and update README Added Example_14 images and LanPaintQwen_03.jpg to showcase the masked Qwen edit workflow. Updated README with new example references and instructions for using the ComfyUI Qwen Image Edit workflow.	2025-08-22 00:17:40 +08:00
scraed	986dee6bd9	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-08-21 18:12:18 +08:00
scraed	e514553120	fix performance issue	2025-08-21 18:12:11 +08:00
scraed	40d7f0854e	Update README.md	2025-08-18 23:07:02 +08:00
charrywhite	a5e3af84f9	Update README.md	2025-08-18 18:18:21 +08:00
charrywhite	598ca776eb	Update README.md	2025-08-18 18:10:50 +08:00
charrywhite	b36584b2a7	Update README.md	2025-08-18 18:07:49 +08:00
charrywhite	082c549b24	Update README.md	2025-08-18 18:05:21 +08:00
charrywhite	453b6c090e	Update README.md	2025-08-18 17:45:27 +08:00
charrywhite	07229ecbcd	Update README.md reorganize readme	2025-08-18 17:36:02 +08:00
charrywhite	76cf0ff5fe	Update README.md	2025-08-18 17:25:21 +08:00
scraed	e1f84469f1	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-08-18 17:20:03 +08:00
charrywhite	1fa77b0d1e	Update README.md	2025-08-18 17:19:20 +08:00
scraed	cd62e71467	Update LanPaintQwen_01.jpg example image Replaces the existing LanPaintQwen_01.jpg in the examples directory with a new version.	2025-08-18 17:19:02 +08:00
charrywhite	deaf2cec2a	Update README.md	2025-08-18 17:18:01 +08:00
scraed	587a941607	Update README.md	2025-08-18 15:44:21 +08:00
scraed	7bdbab75e6	Update LanPaintQwen_01.jpg	2025-08-18 15:35:01 +08:00
scraed	b77c25677f	Fix swapped links for Qwen workflows in README Corrected the example links for Qwen Inpaint and Outpaint workflows to point to their respective directories.	2025-08-18 15:30:29 +08:00
scraed	bf5b607238	update readme	2025-08-18 15:29:21 +08:00
scraed	ad77fb7836	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-08-18 15:27:19 +08:00
scraed	3f8eb2552f	Update README and add new Qwen inpainting examples Enhanced README with details and links for Qwen Image workflows and added new example images for Qwen inpainting and outpainting in Example_12 and Example_13 directories. Also included a sample result image for Qwen inpainting.	2025-08-18 15:27:12 +08:00
scraed	238e49e31f	Update README.md	2025-08-15 15:34:36 +08:00
scraed	7aeb6e535f	Add Qwen image support and new inpainting examples Updated the README to announce Qwen image support and provide usage instructions. Added new example images and workflow files for Qwen inpainting in the examples directory.	2025-08-08 14:09:53 +08:00
scraed	eda0f19944	Generalize dimension handling in LanPaint and fix mask reshape Refactored LanPaint to dynamically handle tensors with varying numbers of dimensions by introducing add_none_dims and remove_none_dims utility methods. Updated all relevant tensor broadcasting to use these methods, improving flexibility. Also fixed reshape_mask in nodes.py to use the last two dimensions for resizing, ensuring correct mask shape.	2025-08-08 13:42:24 +08:00
scraed	e20c8f20ce	update version	2025-06-21 14:13:09 +08:00
scraed	89b9010b35	Fix indentation error in sample method Corrected an indentation issue in the sample method of LanPaint_SamplerCustomAdvanced to ensure proper execution of the end_at_step check.	2025-06-21 14:09:11 +08:00
scraed	ee0c65656e	simplify custom sampler	2025-06-21 13:18:25 +08:00
scraed	48b1dd4be6	Merge branch 'master' into pr/33	2025-06-21 13:10:05 +08:00
scraed	9d304cd5a3	update readme	2025-06-21 13:03:30 +08:00
scraed	61c19ac31d	update algorithm with better outpaint	2025-06-21 12:56:36 +08:00
Bagier	1ca6e53090	adjust info names	2025-06-20 08:31:51 +07:00
Bagier	520932cee0	add invalid value check	2025-06-19 20:49:20 +07:00
Bagier	37612bb399	Logic fix	2025-06-19 20:12:50 +07:00
Bagier	0e6cb45081	re-add lanpaint info	2025-06-19 19:25:59 +07:00
Bagier	0a5e36d55e	add advanced custom sampler	2025-06-19 19:15:58 +07:00
Bagier	b319f772cb	fix parameter update error	2025-06-19 19:00:41 +07:00
Bagier	340376ad18	Add custom sampler variation	2025-06-19 16:14:26 +07:00
scraed	dbfc1585fc	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-06-16 20:03:31 +08:00
scraed	c3ae2c644d	update coefficients	2025-06-16 20:03:25 +08:00
scraed	c7017373c9	Update pyproject.toml	2025-06-08 17:59:04 +08:00
scraed	850f707eb6	Remove redundant comment	2025-06-08 17:58:40 +08:00
scraed	62870f060a	Update README.md	2025-06-06 13:05:50 +08:00
scraed	a91cefacf0	Update README.md	2025-06-06 13:05:16 +08:00
scraed	4265f71a85	Update README.md	2025-06-05 18:15:21 +08:00
scraed	4189ba80c2	add error message	2025-06-05 17:10:07 +08:00
scraed	23ad6e47fd	add new masked blend node	2025-06-05 16:21:46 +08:00
scraed	4d3d5d17f0	Update README.md	2025-06-05 01:22:09 +08:00