add wan 2.2 5B

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.

.gitignore

@@ -98,3 +98,5 @@ cookiecutter-pypackage-env/
 # vscode settings
 .history/
 *.code-workspace
+.vscode/
+/.vscode

.vscode/settings.json

@@ -5,4 +5,8 @@
     "/PATH/TO/ComfyUI/",
     "/PATH/TO/ComfyUI/custom_nodes/"
   ],
+  "cursorpyright.analysis.extraPaths": [
+    "/PATH/TO/ComfyUI/",
+    "/PATH/TO/ComfyUI/custom_nodes/"
+  ],
 }

README.md

@@ -12,38 +12,57 @@
 
 Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.  
 
-This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with 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).
 
-![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.
+**🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
 
-![Qwen Result 1](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_01.jpg) 
+<div align="center">
 
-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.
+| Original Video | Mask (edit T-shirt text) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+| ![Original](https://github.com/scraed/LanPaint/blob/master/examples/Original_No_Mask-example18.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Example_18/Masked_Load_Me_in_Loader.png) | ![Result](https://github.com/scraed/LanPaint/blob/master/examples/Inpainted_81frames_Drag_Me_to_ComfyUI_example18.gif) |
+
+*Video Inpainting Example: 81 frames with temporal consistency*
+
+</div>
+
+Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and 
+[Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint) support.
+  
 
 ## Table of Contents
 - [Features](#features)
 - [Quickstart](#quickstart)
 - [How to Use Examples](#how-to-use-examples)
-- [Examples](#examples)
+- [Video Examples (Beta)](#video-examples-beta)
+  - [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
+  - [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
+  - [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
+  - [Resource Consumption](#resource-consumption)
+- [Image Examples](#image-examples)
+  - [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 Examples](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
+  - [SDXL](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
 - [Usage](#usage)
   - [Basic Sampler](#basic-sampler)
   - [Advanced Sampler](#lanpaint-ksampler-advanced)
   - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
-- [Community Showcase](#community-showcase-) 
+- [Community Showcase](#community-showcase-)
+- [FAQ](#faq)
 - [Updates](#updates)
 - [ToDo](#todo)
 - [Citation](#citation)
 
 ## Features
 
-- **Universal Compatibility** – Works instantly with almost any model (**SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image or custom LoRAs**) and ControlNet.  
+- **Universal Compatibility** – Works instantly with almost any model (**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.  
@@ -79,7 +98,116 @@ Once installed, you'll find the LanPaint nodes under the "sampling" category in
    - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)**
    - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)**
 
-## Examples
+## Video Examples (Beta)
+
+LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly inpaint masked regions across video frames while maintaining temporal consistency.
+
+**Note:** LanPaint supports video inpainting for longer sequences (e.g., 81 frames), but processing time increases significantly (please check the [Resource Consumption](#resource-consumption) section for details) and performance may become unstable. For optimal results and stability, we recommend limiting video inpainting to **40 frames or fewer**.
+
+### Wan 2.2 Video Inpainting 
+
+*Example: Wan2.2 t2v 14B, 480p video (11:6), 40 frames, LanPaint K Sampler, 2 steps of thinking*
+
+| Original Video | Mask (Add a white hat) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+| ![Original Video](https://github.com/scraed/LanPaint/blob/master/examples/Original_No_Mask_example17.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Example_17/Masked_Load_Me_in_Loader.png) | ![Inpainted Result](https://github.com/scraed/LanPaint/blob/master/examples/Inpainted_40frames_Drag_Me_to_ComfyUI_example17.gif) |
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Wan 2.2 5B Video Inpainting 
+
+Similar to Wan 2.2 14B with slightly different workflow. [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
+### Wan 2.2 Video Outpainting
+
+Extend your videos beyond their original boundaries with LanPaint's video outpainting capability based on Wan 2.2. This feature allows you to expand the canvas of your videos while maintaining coherent motion and context.
+
+*Example: Wan2.2 t2v 14B, 480p video (1:1 outpaint to 11:6), 40 frames, LanPaint K Sampler, 2 steps of thinking*
+
+| Original Video | Mask (Expand to 880x480) | Outpainted Result |
+|:--------------:|:----:|:-----------------:|
+| ![Original Video](https://github.com/scraed/LanPaint/blob/master/examples/Original_Load_Me_in_Loader_example19.gif) | ![Mask](https://github.com/scraed/LanPaint/blob/master/examples/Mask_Example19_.png) | ![Outpainted Result](https://github.com/scraed/LanPaint/blob/master/examples/Outpainted_40frames_Drag_Me_to_ComfyUI_example19.gif) |
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_19)
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+### Resource Consumption
+
+
+<table>
+<thead>
+<tr>
+<th align="left">Processing Mode</th>
+<th align="left">Resolution</th>
+<th align="left">Frames Processed</th>
+<th align="left">VRAM Required</th>
+<th align="left">Total Runtime (20 steps)</th>
+</tr>
+</thead>
+<tbody>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Inpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>40 frames</td>
+<td>39.8 GB</td>
+<td><strong>05:37 min</strong></td>
+</tr>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Inpainting</strong></td>
+<td>480×480 (1:1)</td>
+<td>40 frames</td>
+<td>38.0 GB</td>
+<td><strong>05:35 min</strong></td>
+</tr>
+<tr style="background-color: #e8f4f8;">
+<td><strong>Outpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>40 frames</td>
+<td>40.2 GB</td>
+<td><strong>05:36 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Inpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>81 frames</td>
+<td>43.3 GB</td>
+<td><strong>16:23 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Inpainting</strong></td>
+<td>480×480 (1:1)</td>
+<td>81 frames</td>
+<td>39.8 GB</td>
+<td><strong>14:25 min</strong></td>
+</tr>
+<tr style="background-color: #fff4e6;">
+<td><strong>Outpainting</strong></td>
+<td>880×480 (11:6)</td>
+<td>81 frames</td>
+<td>42.6 GB</td>
+<td><strong>13:46 min</strong></td>
+</tr>
+</tbody>
+</table>
+
+<sub>**Test Platform**: All tests were conducted on an NVIDIA RTX Pro 6000.<br>
+**Model Used**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors`.<br>
+**Processing Steps**:  20 sampling steps x 2 (LanPaint steps of thinking).</sub>
+
+**Note:** 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.
@@ -90,6 +218,25 @@ We are excited to announce that LanPaint now supports Wan2.2 text to image gener
 
 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.
+
+
 
 ### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
 
@@ -101,6 +248,9 @@ You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.com
 
 The following examples utilize a random seed of 0 to generate a batch of 4 images for variance demonstration and fair comparison. (Note: Generating 4 images may exceed your GPU memory; please adjust the batch size as necessary.)
 
+![Qwen Result 1](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_01.jpg) 
+Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_13) and [Qwen Outpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_12). You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
+
 ### Example HiDream: InPaint (LanPaint K Sampler, 5 steps of thinking)
 ![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_11.jpg)  
 [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
@@ -235,6 +385,9 @@ Discover how the community is using LanPaint! Here are some user-created tutoria
 
 Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
 
+## FAQ
+[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
+
 ## Updates
 - 2025/08/08
     - Add Qwen image support

example_workflows/Flux_Inpaint.json

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

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "LanPaint"
-version = "1.3.2"
+version = "1.4.3"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
   {name = "LanPaint", email = "czhengac@connect.ust.hk"}

src/LanPaint/lanpaint.py

@@ -13,6 +13,7 @@ class LanPaint():
         self.friction = Friction
         self.chara_beta = Beta
         self.img_dim_size = None
+
     def add_none_dims(self, array):
         # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
         index = (slice(None),) + (None,) * (self.img_dim_size-1)
@@ -25,6 +26,8 @@ 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 +39,13 @@ 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:
@@ -56,7 +64,7 @@ class LanPaint():
         ############ LanPaint Iterations End ###############
         # out is x_0
         out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
-        out = out * (1-latent_mask) + self.latent_image * latent_mask
+        #out = out * (1-latent_mask) + self.latent_image * latent_mask
         return out
 
     def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):

src/LanPaint/nodes.py

@@ -11,19 +11,57 @@ from comfy.samplers import *
 from comfy.model_base import ModelType
 from .utils import *
 from .lanpaint import LanPaint
+from comfy.model_base import WAN22
 
-
-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 +80,18 @@ def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, con
 
 
 class CFGGuider_LanPaint:
-    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs):
         print("CFGGuider outer_sample")
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
+        if isinstance(self.inner_model, WAN22):
+            print("WAN22 detected")
+            self.inner_model.extra_conds = super(WAN22, self.inner_model).extra_conds
+
         if denoise_mask is not None:
-            denoise_mask = prepare_mask(denoise_mask, noise.shape, device)
+            video_inpainting = self.model_options.get("video_inpainting", False)
+            denoise_mask = prepare_mask(denoise_mask, noise.shape, device, video_inpainting)
 
         noise = noise.to(device)
         latent_image = latent_image.to(device)
@@ -57,7 +100,7 @@ class CFGGuider_LanPaint:
 
         try:
             self.model_patcher.pre_run()
-            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs)
         finally:
             self.model_patcher.cleanup()
 
@@ -86,7 +129,9 @@ 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
@@ -245,6 +290,7 @@ 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 +301,9 @@ 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 +313,13 @@ 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 +341,13 @@ 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 +356,7 @@ 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 +373,15 @@ 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 +476,7 @@ 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 +521,13 @@ 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 +536,14 @@ 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 +556,33 @@ 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 = {