version change

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 23:29:23 +08:00 · 2025-10-26 23:28:35 +08:00 · 2025-10-16 15:59:13 +08:00 · 2025-10-03 22:32:19 +08:00 · 2025-10-03 09:59:01 +08:00 · 2025-10-02 23:24:21 +08:00
63 changed files with 10541 additions and 130 deletions
@@ -1,13 +1,67 @@
-# LanPaint (Thinking mode Inpaint)
+<div align="center">

-Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior quality.  
-![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
+# LanPaint: Universal Inpainting Sampler with "Think Mode"
+[![arXiv](https://img.shields.io/badge/Arxiv-2502.03491-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2502.03491)
+[![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)
+</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 Exact and Fast Conditional Inference"](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).
+
+**🎬 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 Video Outpainting](#wan-22-video-outpainting)
+  - [Resource Consumption](#resource-consumption)
+- [Image Examples](#image-examples)
+  - [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 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-) 
+- [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 (**SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image, Wan2.2 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.  
@@ -16,17 +70,6 @@ This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpai

 **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).

-## 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.
-
-
 ## Quickstart

 1. **Install ComfyUI**: Follow the official [ComfyUI installation guide](https://docs.comfy.org/get_started) to set up ComfyUI on your system. Or ensure your ComfyUI version > 0.3.11.
@@ -43,41 +86,165 @@ 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)**
+
+## Video Examples (Beta)
+
+LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly inpaint masked regions across video frames while maintaining temporal consistency.
+
+**Note:** LanPaint supports video inpainting for longer sequences (e.g., 81 frames), but processing time increases significantly (please check the [Resource Consumption](#resource-consumption) section for details) and performance may become unstable. For optimal results and stability, we recommend limiting video inpainting to **40 frames or fewer**.
+
+### Wan 2.2 Video Inpainting 
+
+*Example: Wan2.2 t2v 14B, 480p video (11:6), 40 frames, LanPaint K Sampler, 2 steps of thinking*
+
+| Original Video | Mask (Add a white hat) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+| ![Original Video](https://github.com/scraed/LanPaint/blob/master/examples/Original_No_Mask_example17.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Example_17/Masked_Load_Me_in_Loader.png) | ![Inpainted Result](https://github.com/scraed/LanPaint/blob/master/examples/Inpainted_40frames_Drag_Me_to_ComfyUI_example17.gif) |
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Wan 2.2 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:** To further reduce VRAM requirements, we recommend 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 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).
+
+![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.

-## 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.

-## Examples

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

 ![Inpainting Result 14](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_14.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_11)

+
 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)
@@ -138,24 +305,6 @@ 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).  
-8. You also get an output from "masked blend" node, which copy the original image and paste onto the unmasked part of output. It is useful if you want unmasked region to match original picture pixel perfectly.
-
-Compare and explore the results from each method!
-
-![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG)  
-
-
 ## Usage

 **Workflow Setup**  
@@ -205,7 +354,7 @@ For challenging inpainting tasks:
 Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
  
 2️⃣ **Boost Speed**
-If you want better results but still need fewer steps, consider:
+Decrease **LanPaint_NumSteps** to accelerate generation! If you want better results but still need fewer steps, consider:
    - **Increasing LanPaint_StepSize** to speed up the thinking process.
    - **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster.
    
@@ -218,14 +367,45 @@ 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!
+
+## 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

@@ -0,0 +1,786 @@
+{
+  "id": "978d3a45-3d13-43c6-8ef9-89dc3e74d6ba",
+  "revision": 0,
+  "last_node_id": 82,
+  "last_link_id": 220,
+  "nodes": [
+    {
+      "id": 66,
+      "type": "SetLatentNoiseMask",
+      "pos": [
+        514.1915893554688,
+        781.9396362304688
+      ],
+      "size": [
+        264.5999755859375,
+        46
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 216
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 189
+        }
+      ],
+      "outputs": [
+        {
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+      "widgets_values": [
+        0,
+        "fixed",
+        30,
+        5,
+        "euler",
+        "karras",
+        1,
+        5,
+        "Image First",
+        "LanPaint KSampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!"
+      ]
+    },
+    {
+      "id": 74,
+      "type": "LanPaint_MaskBlend",
+      "pos": [
+        1512.2730712890625,
+        1176.3270263671875
+      ],
+      "size": [
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+        98
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image1",
+          "type": "IMAGE",
+          "link": 190
+        },
+        {
+          "name": "image2",
+          "type": "IMAGE",
+          "link": 189
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 191
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            188
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "LanPaint",
+        "ver": "4d3d5d17f0105b673df92da5b084cce567c9c712",
+        "Node name for S&R": "LanPaint_MaskBlend"
+      },
+      "widgets_values": [
+        9
+      ]
+    },
+    {
+      "id": 66,
+      "type": "SetLatentNoiseMask",
+      "pos": [
+        480.30780029296875,
+        821.3880004882812
+      ],
+      "size": [
+        264.5999755859375,
+        46
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 192
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 155
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            186
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23",
+        "Node name for S&R": "SetLatentNoiseMask"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 65,
+      "type": "VAEEncode",
+      "pos": [
+        206.43878173828125,
+        818.9985961914062
+      ],
+      "size": [
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+        46
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 156
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 157
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            192
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.23",
+        "Node name for S&R": "VAEEncode"
+      },
+      "widgets_values": []
+    }
+  ],
+  "links": [
+    [
+      81,
+      29,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
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+      1,
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+      0,
+      "CLIP"
+    ],
+    [
+      84,
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+    [
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+      1,
+      "CONDITIONING"
+    ],
+    [
+      185,
+      7,
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+      73,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      186,
+      66,
+      0,
+      73,
+      3,
+      "LATENT"
+    ],
+    [
+      187,
+      73,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      188,
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+      0,
+      75,
+      0,
+      "IMAGE"
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+    [
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+      1,
+      "IMAGE"
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+    [
+      190,
+      20,
+      0,
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+      0,
+      "IMAGE"
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+    [
+      191,
+      20,
+      1,
+      74,
+      2,
+      "MASK"
+    ],
+    [
+      192,
+      65,
+      0,
+      66,
+      0,
+      "LATENT"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Mask image for inpainting.",
+      "bounding": [
+        36.04227828979492,
+        989.7313232421875,
+        278.89093017578125,
+        669.3414916992188
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "Convert Latents for LanPaint",
+      "bounding": [
+        286.0640563964844,
+        714.343505859375,
+        489.16796875,
+        197.81044006347656
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 3,
+      "title": "Load Model and Set Prompts",
+      "bounding": [
+        -78.9311294555664,
+        176.08712768554688,
+        1006.1721801757812,
+        514.258544921875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 4,
+      "title": "Inpaint with the LanPaint KSampler",
+      "bounding": [
+        960.8922729492188,
+        179.17588806152344,
+        474.8909606933594,
+        630.4742431640625
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 5,
+      "title": "LanPaint OutPut",
+      "bounding": [
+        1085.6029052734375,
+        994.0775756835938,
+        345.4561767578125,
+        669.4969482421875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 11,
+      "title": "LanPaint",
+      "bounding": [
+        -262.59381103515625,
+        140.46656799316406,
+        1737.328857421875,
+        797.4443359375
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "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 = "1.2.0"
+version = "1.4.2"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
  {name = "LanPaint", email = "czhengac@connect.ust.hk"}
@@ -25,6 +25,8 @@ class LanPaint():
        self.img_dim_size = len(x.shape)
        self.latent_image = latent_image
        self.noise = noise
+        if torch.mean(torch.abs(self.noise)) < 1e-8:
+            self.noise = torch.randn_like(self.noise)
        if n_steps is None:
            n_steps = self.n_steps
        return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
@@ -36,8 +38,11 @@ class LanPaint():
        step_size = self.add_none_dims(step_size)
        # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
        # This is the replace step
-        x = x * (1 - latent_mask) +  self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
-        
+        def scale_latent_inpaint(x, sigma, noise, latent_image):
+            return self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
+
+        x = x * (1 - latent_mask) +  scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
+
        if IS_FLUX or IS_FLOW:
            x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
        else:
@@ -62,7 +67,6 @@ 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
@@ -92,8 +96,8 @@ 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
@@ -13,17 +13,55 @@ from .utils import *
 from .lanpaint import LanPaint


-def reshape_mask(input_mask, output_shape):
+def reshape_mask(input_mask, output_shape,video_inpainting=False):
    dims = len(output_shape) - 2
-
+    print('output shape',output_shape)
    scale_mode = "nearest-exact"
-    mask = torch.nn.functional.interpolate(input_mask, size=output_shape[-2:], mode=scale_mode)
-    if mask.shape[1] < output_shape[1]:
-        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
-    mask = repeat_to_batch_size(mask, output_shape[0])
+    print('input mask',input_mask.shape,type(input_mask),torch.max(input_mask),torch.min(input_mask))
+    print('target output_shape',output_shape)
+    print('input_mask.ndim:', input_mask.ndim, 'output_shape len:', len(output_shape))
+    
+    # Handle 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)
+        input_mask = input_mask.permute(1, 0, 2, 3).unsqueeze(0)
+        print('Video case - input_mask after reshaping:', input_mask.shape)
+        # Ensure we have the correct 5D shape: (batch, channels, frames, height, width)
+        batch_size, channels, frames, height, width = input_mask.shape
+        print('Video case - dimensions: batch_size={}, channels={}, frames={}, height={}, width={}'.format(batch_size, channels, frames, height, width))
+        print('Video case - target size:', (target_frames, target_height, target_width))
+        
+        # 3D nearest-exact interpolation: (batch, channels, frames, height, width) -> (batch, channels, target_frames, target_height, target_width)
+        temp_mask = torch.nn.functional.interpolate(
+            input_mask, 
+            size=(target_frames, target_height, target_width), 
+            mode=scale_mode, 
+        )
+        
+        # temp_mask is already 5D: (batch, channels, target_frames, target_height, target_width)
+        mask = temp_mask
+        print('after mask',mask.shape)
+        # Handle channel dimension expansion if needed
+        if mask.shape[1] < output_shape[1]:
+            mask = mask.repeat(1, output_shape[1], 1, 1, 1)[:, :output_shape[1]]
+        # Handle batch dimension
+        mask = repeat_to_batch_size(mask, output_shape[0])
+    else:  # Original 2D image case
+        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('resize mask',mask.shape,type(mask),torch.max(mask),torch.min(mask))
    return mask
-def prepare_mask(noise_mask, shape, device):
-    return reshape_mask(noise_mask, shape).to(device)
+def prepare_mask(noise_mask, shape, device,video_inpainting=False):
+    return reshape_mask(noise_mask, shape,video_inpainting).to(device)
 def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, cond_scale_BIG, model_options={}, seed=None):
    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
        uncond_ = None
@@ -42,13 +80,14 @@ 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 denoise_mask is not None:
-            denoise_mask = prepare_mask(denoise_mask, noise.shape, device)
+            video_inpainting = self.model_options.get("video_inpainting", False)
+            denoise_mask = prepare_mask(denoise_mask, noise.shape, device, video_inpainting)

        noise = noise.to(device)
        latent_image = latent_image.to(device)
@@ -57,7 +96,7 @@ 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()

@@ -75,8 +114,8 @@ 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
@@ -86,7 +125,8 @@ 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 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
@@ -245,6 +285,7 @@ class LanPaint_KSampler():
                "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
                "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}),
                "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+                "Inpainting_mode": (["🖼️ Image Inpainting", "🎬 Video Inpainting"], {"default": "🖼️ Image Inpainting", "tooltip": "Choose Image mode for photos or Video mode for video frames with temporal consistency"}),
                  }
        }

@@ -255,9 +296,9 @@ class LanPaint_KSampler():
    CATEGORY = "sampling"
    DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."

-    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, LanPaint_NumSteps=5, LanPaint_PromptMode = "Image First", LanPaint_Info=""):
+    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, LanPaint_NumSteps=5, LanPaint_PromptMode="Image First",  LanPaint_Info="",Inpainting_mode="🖼️ Image Inpainting"):

-        model.LanPaint_StepSize = 0.15
+        model.LanPaint_StepSize = 0.2
        model.LanPaint_Lambda = 16.0
        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -267,6 +308,13 @@ class LanPaint_KSampler():
            model.LanPaint_cfg_BIG = cfg
        else:
            model.LanPaint_cfg_BIG = 0*cfg - 0.5
+        
+        # Convert inpainting_mode to boolean for video_inpainting
+        video_inpainting = (Inpainting_mode == "🎬 Video Inpainting")
+        if not hasattr(model, 'model_options') or model.model_options is None:
+            model.model_options = {}
+        model.model_options["video_inpainting"] = video_inpainting
+        
        with override_sample_function():
            return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
 class LanPaint_KSamplerAdvanced:
@@ -288,12 +336,13 @@ class LanPaint_KSamplerAdvanced:
                    "return_with_leftover_noise": (["disable", "enable"], ),
                "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
                "LanPaint_Lambda": ("FLOAT", {"default": 16., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
-                "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
+                "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
                "LanPaint_Beta": ("FLOAT", {"default": 1., "min": 0.0001, "max": 5, "step": 0.1, "round": 0.1, "tooltip": "The step size ratio between masked / unmasked regions. Lower value can compensate high values of LanPaint_Lambda."}),
                "LanPaint_Friction": ("FLOAT", {"default": 15, "min": 0., "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The friction parameter for fast langevin, lower values result in faster convergence but may be unstable."}),
                "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}),
                "LanPaint_EarlyStop": ("INT", {"default": 1, "min": 0, "max": 10000, "tooltip": "The number of steps to stop the LanPaint early, useful for preventing the image from irregular patterns."}),
                "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler Adv. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+                "Inpainting_mode": (["🖼️ Image Inpainting", "🎬 Video Inpainting"], {"default": "🖼️ Image Inpainting", "tooltip": "Choose Image mode for photos or Video mode for video frames with temporal consistency"}),
                     },
                }

@@ -302,7 +351,7 @@ 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
@@ -319,9 +368,15 @@ 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:
@@ -416,7 +471,7 @@ class LanPaint_SamplerCustom:
    CATEGORY = "sampling/custom_sampling"

    def sample(self, model, sampler, sigmas, add_noise, noise_seed, cfg, positive, negative, latent_image, LanPaint_NumSteps, LanPaint_PromptMode, LanPaint_Info=""):
-        model.LanPaint_StepSize = 0.15
+        model.LanPaint_StepSize = 0.2
        model.LanPaint_Lambda = 16.0
        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -461,16 +516,13 @@ class LanPaint_SamplerCustomAdvanced:
    def INPUT_TYPES(s):
        return {"required":
                    {"noise": ("NOISE",),
-                     "guider": ("GUIDER",),
-                     "sampler": ("SAMPLER",),
-                     "sigmas": ("SIGMAS",),
-                     "latent_image": ("LATENT",),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "return_with_leftover_noise": (["disable", "enable"], ),
+                    "guider": ("GUIDER", ),
+                    "sampler": ("SAMPLER", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),
                     "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
                     "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
-                     "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+                     "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
                     "LanPaint_Beta": ("FLOAT", {"default": 1.0, "min": 0.0001, "max": 5.0, "step": 0.1, "tooltip": "Step size ratio between masked/unmasked regions. Lower values balance high Lambda."}),
                     "LanPaint_Friction": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Friction parameter for fast Langevin. Lower values speed convergence but may be unstable."}),
                     "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
@@ -479,17 +531,14 @@ class LanPaint_SamplerCustomAdvanced:
                    }
               }

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

-    def sample(self, noise, guider, sampler, sigmas, latent_image, start_at_step, end_at_step, return_with_leftover_noise, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
-        force_full_denoise = True
-        if end_at_step <= start_at_step:
-            raise ValueError('end_at_step must be larger than start_at_step')
-        if return_with_leftover_noise == "enable":
-            force_full_denoise = False
+    def sample(self, noise, guider, sampler, sigmas, latent_image, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
        model = guider.model_patcher
        model.LanPaint_StepSize = LanPaint_StepSize
        model.LanPaint_Lambda = LanPaint_Lambda
@@ -502,47 +551,33 @@ class LanPaint_SamplerCustomAdvanced:
        else:
            model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
        with override_sample_function():
-            latent = latent_image.copy()
-            latent_image_samples = latent["samples"]
-            latent_image_samples = comfy.sample.fix_empty_latent_channels(model, latent_image_samples)
-            latent["samples"] = latent_image_samples
+            latent = latent_image
+            latent_image = latent["samples"]
+            latent = latent.copy()
+            latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+            latent["samples"] = latent_image

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

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

+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {
Author	SHA1	Message	Date
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