+
+# LanPaint: Universal Inpainting Sampler with "Think Mode"
+[](https://openreview.net/pdf?id=JPC8JyOUSW)
+[](https://github.com/scraed/LanPaintBench)
+[](https://github.com/comfyanonymous/ComfyUI)
+[](https://huggingface.co/charrywhite/LanPaint)
+[](https://scraed.github.io/scraedBlog/)
+[](https://github.com/scraed/LanPaint/stargazers)
+[](https://discord.gg/yN5wYDE6W4)
+
+
+
+Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.
+
+This is the official implementation of ["LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling"](https://arxiv.org/abs/2502.03491), accepted by TMLR.
+
+The repository is for ComfyUI extension.
+
+Diffusers Support: [LanPaint-Diffusers](https://github.com/charrywhite/LanPaint-diffusers) by [@charrywhite](https://github.com/charrywhite/)
+
+Benchmark code for paper reproduce: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+
+## Citation
+
+```
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
+}
+```
+**🎉 NEW 2026: Join our discord!**
+
+[Join our Discord](https://discord.gg/yN5wYDE6W4) to share experiences, discuss features, and explore future development.
+
+**🎬 NEW: LanPaint now supports inpainting and outpainting based on Z-Image!**
+
+`v1.5.0` fixes an important hidden bug that reduced performance and could blur images (especially with `z-image-base`) and also boosts overall LanPaint performance across other models.
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+|  |  |  |
+
+**🎬 NEW: LanPaint now supports Z-Image-Base too!**
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+|  |  |  |
+
+
+**🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
+
+
+
+| Original Video | Mask (edit T-shirt text) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+|  |  |  |
+
+*Video Inpainting Example: 81 frames with temporal consistency*
+
+
+
+Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and
+[Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint) support.
+
+
+## Table of Contents
+- [Features](#features)
+- [Quickstart](#quickstart)
+- [How to Use Examples](#how-to-use-examples)
+- [Video Examples (Beta)](#video-examples-beta)
+ - [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
+ - [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
+ - [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
+ - [Resource Consumption](#resource-consumption)
+- [Image Examples](#image-examples)
+ - [Flux.2.Dev](#example-flux2dev-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Flux 2 klein](#example-flux-2-klein-inpaintlanpaint-k-sampler-2-steps-of-thinking)
+ - [Z-image](#example-z-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Z-image-base](#example-z-image-base-inpaintlanpaint-k-sampler-3-steps-of-thinking)
+ - [Hunyuan T2I](#example-hunyuan-t2i-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Wan 2.2 T2I with reference](#example-wan22-partial-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Qwen Image Edit 2511 2509](#example-qwen-edit-2509-inpaint)
+ - [Qwen Image Edit 2508](#example-qwen-edit-2508-inpaint)
+ - [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
+ - [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Flux](#example-flux-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [SDXL](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
+- [Usage](#usage)
+ - [Basic Sampler](#basic-sampler)
+ - [Advanced Sampler](#lanpaint-ksampler-advanced)
+ - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
+- [Community Showcase](#community-showcase-)
+- [FAQ](#faq)
+- [Updates](#updates)
+- [ToDo](#todo)
+- [Citation](#citation)
+
+## Features
+
+- **Universal Compatibility** – Works instantly with almost any model (**Z-image, Z-image-base, Hunyuan, Wan 2.2, Qwen Image/Edit, HiDream, SD 3.5, Flux-series, SDXL, SD 1.5 or custom LoRAs**) and ControlNet.
+
+- **No Training Needed** – Works out of the box with your existing model.
+- **Easy to Use** – Same workflow as standard ComfyUI KSampler.
+- **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.
+- **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
+- **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters.
+
+**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
+
+## Quickstart
+
+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.
+2. **Install ComfyUI-Manager**: Add the [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) for easy extension management.
+3. **Install LanPaint Nodes**:
+ - **Via ComfyUI-Manager**: Search for "[LanPaint](https://registry.comfy.org/publishers/scraed/nodes/LanPaint)" in the manager and install it directly.
+ - **Manually**: Click "Install via Git URL" in ComfyUI-Manager and input the GitHub repository link:
+ ```
+ https://github.com/scraed/LanPaint.git
+ ```
+ Alternatively, clone this repository into the `ComfyUI/custom_nodes` folder.
+4. **Restart ComfyUI**: Restart ComfyUI to load the LanPaint nodes.
+
+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 |
+|:--------------:|:----:|:----------------:|
+|  |  |  |
+
+[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 |
+|:--------------:|:----:|:-----------------:|
+|  |  |  |
+
+[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
+
+
+| Processing Mode | +Resolution | +Frames Processed | +VRAM Required | +Total Runtime (20 steps) | +
|---|---|---|---|---|
| Inpainting | +880×480 (11:6) | +40 frames | +39.8 GB | +05:37 min | +
| Inpainting | +480×480 (1:1) | +40 frames | +38.0 GB | +05:35 min | +
| Outpainting | +880×480 (11:6) | +40 frames | +40.2 GB | +05:36 min | +
| Inpainting | +880×480 (11:6) | +81 frames | +43.3 GB | +16:23 min | +
| Inpainting | +480×480 (1:1) | +81 frames | +39.8 GB | +14:25 min | +
| Outpainting | +880×480 (11:6) | +81 frames | +42.6 GB | +13:46 min | +
+**Model Used**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors`.
+**Processing Steps**: 20 sampling steps x 2 (LanPaint steps of thinking). + +**Note:** Vram is required by the model, not LanPaint. To further reduce VRAM requirements, we recommend generating less frames and loading CLIP on CPU. + +## Image Examples + +### Example Hunyuan T2I: InPaint(LanPaint K Sampler, 5 steps of thinking) +We are excited to announce that LanPaint now supports inpainting with Hunyuan text to image generation. + +[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_20) + + +You need to follow the ComfyUI version of [Hunyuan workflow](https://docs.comfy.org/tutorials/video/hunyuan-video#hunyuan-text-to-video-workflow) to download and install the model. + +### Example Wan2.2: InPaint(LanPaint K Sampler, 5 steps of thinking) +We are excited to announce that LanPaint now supports Wan2.2 text to image generation with Wan2.2 T2V model. + + +[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_15) + + +You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model. + +### Example Z-image: InPaint(LanPaint K Sampler, 5 steps of thinking) +LanPaint also supports inpainting with the Z-image text-to-image model. + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_21)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Outpaint Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_22)
+
+You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
+
+### Example Z-image-base: InPaint(LanPaint K Sampler, 3 steps of thinking)
+LanPaint also supports inpainting with the Z-image-base model.
+
+**Warning (stability)**: Z-image-base can easily diverge with LanPaint. Start with **small `LanPaint_StepSize`** and **fewer thinking iterations** (lower `LanPaint_NumSteps`) and increase gradually only if stable.
+
+View Z-image Outpainting (Original / Masked / Outpainted)
+ +| Original | Masked | Outpainted | +|:--------:|:------:|:----------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_25)
+
+Workflow template (JSON): [Z_image_base_Inpaint.json](https://github.com/scraed/LanPaint/blob/master/example_workflows/Z_image_base_Inpaint.json)
+
+### 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.
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_16)
+
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+
+### Example Qwen Edit 2509: InPaint
+Check our latest updated [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14) for Qwen Image Edit 2509. Download the model at [Qwen Image Edit 2509 Comfy](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models). This workflow also supports Qwen Image Edit 2511.
+
+
+
+### Example Qwen Edit 2508: InPaint
+
+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)
+
+
+[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.)
+
+
+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)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
+
+### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
+.jpg)
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model. Thanks [Amazon90](https://github.com/Amazon90) for providing this example.
+
+### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
+
+You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki.com/en/tutorial/advanced/stable-diffusion-3-5-comfyui-workflow) to download and install the model.
+
+### Example Flux.2.Dev: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_23)
+
+[Model Used in This Example](https://huggingface.co/Comfy-Org/flux2-dev)
+
+(Note: Prompt First mode is disabled on Flux.2.Dev. As it does not use CFG guidance.)
+
+### Example Flux 2 klein: InPaint(LanPaint K Sampler, 2 steps of thinking)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_24)
+
+[Model Used in This Example](https://docs.comfy.org/zh-CN/tutorials/flux/flux-2-klein). If you have quality problem on Comfy 0.11 and 0.12, check [this issue](https://github.com/scraed/LanPaint/issues/80).
+
+
+### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
+[Model Used in This Example](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors)
+(Note: Prompt First mode is disabled on Flux. As it does not use CFG guidance.)
+
+### Example SDXL 0: Character Consistency (Side View Generation) (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_6)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+
+(Tricks 1: You can emphasize the character by copy it's image multiple times with Photoshop. Here I have made one extra copy.)
+
+(Tricks 2: Use prompts like multiple views, multiple angles, clone, turnaround. Use LanPaint's Prompt first mode (does not support Flux))
+
+(Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!)
+
+
+### Example SDXL 1: Basket to Basket Ball (LanPaint K Sampler, 2 steps of thinking).
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_1)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+### Example SDXL 2: White Shirt to Blue Shirt (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_2)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+### Example SDXL 3: Smile to Sad (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_3)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+### Example SDXL 4: Damage Restoration (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_4)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+### Example SDXL 5: Huge Damage Restoration (LanPaint K Sampler, 20 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_5)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+
+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).
+
+
+## Usage
+
+**Workflow Setup**
+Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes. The inpainting workflow is the same as the [SetLatentNoiseMask](https://comfyui-wiki.com/zh/comfyui-nodes/latent/inpaint/set-latent-noise-mask) inpainting workflow.
+
+**Note**
+- LanPaint requires binary masks (values of 0 or 1) without opacity or smoothing. To ensure compatibility, set the mask's **opacity and hardness to maximum** in your mask editor. During inpainting, any mask with smoothing or gradients will automatically be converted to a binary mask.
+- LanPaint relies heavily on your text prompts to guide inpainting - explicitly describe the content you want generated in the masked area. If results show artifacts or mismatched elements, counteract them with targeted negative prompts.
+
+## Basic Sampler
+
+
+- LanPaint KSampler: The most basic and easy to use sampler for inpainting.
+- LanPaint KSampler (Advanced): Full control of all parameters.
+
+### LanPaint KSampler
+Simplified interface with recommended defaults:
+
+- Steps: 20 - 50. More steps will give more "thinking" and better results.
+- LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks ( which means 5 times slower than sampling without thinking). Use 10 for more challenging tasks.
+- LanPaint Prompt mode: Image First mode and Prompt First mode. Image First mode focuses on the image, inpaint based on image context (maybe ignore prompt), while Prompt First mode focuses more on the prompt. Use Prompt First mode for tasks like character consistency. (Technically, it Prompt First mode change CFG scale to negative value in the BIG score to emphasis prompt, which will costs image quality.)
+
+### LanPaint KSampler (Advanced)
+Full parameter control:
+**Key Parameters**
+
+| Parameter | Range | Description |
+|-----------|-------|-------------|
+| `Steps` | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 20-50. |
+| `LanPaint_NumSteps` | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 2-5. Hard task: 5-10 |
+| `LanPaint_Lambda` | 0.1-50 | Content alignment strength (higher = stricter). Recommend 4.0 - 10.0 |
+| `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. |
+| `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 |
+| `LanPaint_Friction` | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0|
+| `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5|
+| `LanPaint_PromptMode` | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. |
+
+For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.
+
+### LanPaint Mask Blend
+This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly.
+
+## LanPaint KSampler (Advanced) Tuning Guide
+For challenging inpainting tasks:
+
+1️⃣ **Boost Quality**
+Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
+
+2️⃣ **Boost Speed**
+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.
+
+3️⃣ **Fix Unstability**:
+If you find the results have wired texture, try
+- Reduce **LanPaint_Friction** to make the Langevin dynamics more stable.
+- Reduce **LanPaint_StepSize** to use smaller step size.
+- Reduce **LanPaint_Beta** if you are using a high lambda value.
+
+⚠️ **Notes**:
+- For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting. Better to do it with a batche of images to avoid overfitting on a single image.
+
+## Community Showcase [](#community-showcase-)
+
+Discover how the community is using LanPaint! Here are some user-created tutorials:
+
+- [Ai绘画进阶148-三大王炸！庆祝高允贞出道6周年！T8即将直播？当AI绘画学会深度思考？！万能修复神器LanPaint，万物皆可修！-T8 Comfyui教程](https://www.youtube.com/watch?v=Z4DSTv3UPJo)
+- [Ai绘画进阶151-真相了！T8竟是个AI？！LanPaint进阶（二），人物一致性，多视角实验性测试，新参数讲解，工作流分享-T8 Comfyui教程](https://www.youtube.com/watch?v=landiRhvF3k)
+- [重绘和三视图角色一致性解决新方案！LanPaint节点尝试](https://www.youtube.com/watch?v=X0WbXdm6FA0)
+- [ComfyUI: HiDream with Perturbation Upscale, LanPaint Inpainting (Workflow Tutorial)](https://www.youtube.com/watch?v=2-mGe4QVIIw&t=2785s)
+- [ComfyUI必备LanPaint插件超详细使用教程](https://plugin.aix.ink/archives/lanpaint)
+
+Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
+
+## FAQ
+[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
+
+## Updates
+- 2026/03/02
+ - `v1.5.0`: Fixed a hidden bug that hurt performance and caused image blur (especially on `z-image-base`), and improved overall LanPaint performance on other models too.
+- 2026/01/30
+ - Add Z-image-base documentation and Example_25 workflow images.
+- 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.~~ Check our local Python benchmark code [LanPaintBench](https://github.com/scraed/LanPaintBench).
+
+
+## Citation
+
+```
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
+}
+```
+
+
+
+
+
diff --git a/LanPaint/__init__.py b/LanPaint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..87fc590e70e82c8e9b6e9688fdf497b8a2a53e5a
--- /dev/null
+++ b/LanPaint/__init__.py
@@ -0,0 +1,94 @@
+"""Top-level package for LanPaint."""
+
+__all__ = [
+ "NODE_CLASS_MAPPINGS",
+ "NODE_DISPLAY_NAME_MAPPINGS",
+ "WEB_DIRECTORY",
+]
+
+__author__ = """LanPaint"""
+__email__ = "czhengac@connect.ust.hk"
+__version__ = "0.0.1"
+
+
+def _install_lightweight_runtime_stubs() -> None:
+ """Install lightweight stubs so tooling can import this package without ComfyUI.
+
+ This is used by CI tooling (e.g., comfy-org/node-diff) that imports NODE_CLASS_MAPPINGS
+ in an environment where ComfyUI isn't installed.
+ """
+ import sys
+ import types
+
+ # `src/LanPaint/nodes.py` uses `torch.Tensor` in type annotations.
+ try:
+ import torch # noqa: F401
+ except ModuleNotFoundError:
+ torch_mod = types.ModuleType("torch")
+
+ class Tensor: # noqa: N801 (match torch naming)
+ pass
+
+ torch_mod.Tensor = Tensor
+ torch_mod.nn = types.SimpleNamespace(functional=types.SimpleNamespace())
+ sys.modules["torch"] = torch_mod
+
+ if "comfyui_version" not in sys.modules:
+ comfyui_version_mod = types.ModuleType("comfyui_version")
+ comfyui_version_mod.__version__ = "0.0.0"
+ sys.modules["comfyui_version"] = comfyui_version_mod
+
+ sys.modules.setdefault("nodes", types.ModuleType("nodes"))
+ sys.modules.setdefault("latent_preview", types.ModuleType("latent_preview"))
+
+ if "comfy" not in sys.modules:
+ comfy_mod = types.ModuleType("comfy")
+ comfy_mod.__path__ = []
+
+ comfy_utils_mod = types.ModuleType("comfy.utils")
+
+ def repeat_to_batch_size(tensor, batch_size): # type: ignore[no-untyped-def]
+ if getattr(tensor, "shape", ())[0] == batch_size:
+ return tensor
+ return tensor
+
+ comfy_utils_mod.repeat_to_batch_size = repeat_to_batch_size
+
+ comfy_samplers_mod = types.ModuleType("comfy.samplers")
+
+ class DummyKSAMPLER: # noqa: N801 (match ComfyUI naming)
+ pass
+
+ comfy_samplers_mod.KSAMPLER = DummyKSAMPLER
+
+ comfy_model_base_mod = types.ModuleType("comfy.model_base")
+
+ class ModelType: # noqa: N801 (match ComfyUI naming)
+ FLUX = "FLUX"
+ FLOW = "FLOW"
+
+ class WAN22: # noqa: N801 (match ComfyUI naming)
+ pass
+
+ comfy_model_base_mod.ModelType = ModelType
+ comfy_model_base_mod.WAN22 = WAN22
+
+ comfy_mod.utils = comfy_utils_mod
+ comfy_mod.samplers = comfy_samplers_mod
+ comfy_mod.model_base = comfy_model_base_mod
+
+ sys.modules["comfy"] = comfy_mod
+ sys.modules["comfy.utils"] = comfy_utils_mod
+ sys.modules["comfy.samplers"] = comfy_samplers_mod
+ sys.modules["comfy.model_base"] = comfy_model_base_mod
+
+
+try:
+ from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
+ from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
+except ModuleNotFoundError:
+ _install_lightweight_runtime_stubs()
+ from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
+ from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
+
+WEB_DIRECTORY = "./web"
diff --git a/LanPaint/__pycache__/__init__.cpython-312.pyc b/LanPaint/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..13acf84371bf7c2d710e9c2f5c39dac5bb1089d4
Binary files /dev/null and b/LanPaint/__pycache__/__init__.cpython-312.pyc differ
diff --git a/LanPaint/build/lib/LanPaint/__init__.py b/LanPaint/build/lib/LanPaint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/LanPaint/build/lib/LanPaint/earlystop.py b/LanPaint/build/lib/LanPaint/earlystop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1dc244d5b865d60686ce481467af6f471b2226c
--- /dev/null
+++ b/LanPaint/build/lib/LanPaint/earlystop.py
@@ -0,0 +1,337 @@
+"""
+Early Stop Logic Contributed by `https://github.com/godnight10061`.
+"""
+
+import inspect
+from typing import Any, Callable, Optional
+
+import torch
+
+from .types import LangevinState
+
+
+def _clamp01(val: float) -> float:
+ if val <= 0.0:
+ return 0.0
+ if val >= 1.0:
+ return 1.0
+ return val
+
+
+def _abt_scale(abt_val: float) -> float:
+ """
+ Smooth, parameter-free scale based on outer-step noise level.
+
+ - 0 at abt=0/1 (disable at extreme noise / extreme tail)
+ - 1 at abt=0.5 (mid-schedule)
+ """
+ abt_val = _clamp01(abt_val)
+ return _clamp01(4.0 * abt_val * (1.0 - abt_val))
+
+
+def _boundary_weight(latent_mask: torch.Tensor, inpaint_weight: torch.Tensor) -> Optional[torch.Tensor]:
+ """
+ Return a 4-neighbor boundary weight: unknown pixels adjacent to known pixels.
+
+ This replaces the previous dilation-based "ring" (kernel/padding) and has no tunable hyperparameters.
+ """
+ if latent_mask.dim() != 4:
+ return None
+
+ known = latent_mask > 0.5
+ neighbor_known = torch.zeros_like(known)
+ neighbor_known[:, :, 1:, :] |= known[:, :, :-1, :]
+ neighbor_known[:, :, :-1, :] |= known[:, :, 1:, :]
+ neighbor_known[:, :, :, 1:] |= known[:, :, :, :-1]
+ neighbor_known[:, :, :, :-1] |= known[:, :, :, 1:]
+
+ boundary = (~known) & neighbor_known
+ return boundary.to(dtype=torch.float32) * inpaint_weight
+
+
+def _weighted_mse(t1: torch.Tensor, t2: torch.Tensor, weight: torch.Tensor) -> float:
+ diff_sq = (t1.to(dtype=torch.float32) - t2.to(dtype=torch.float32)) ** 2
+ denom = torch.sum(weight) + 1e-12
+ return float((torch.sum(diff_sq * weight) / denom).item())
+
+
+class LanPaintEarlyStopper:
+ """
+ Per-step early-stop logic for LanPaint inner (Langevin) iterations.
+ """
+
+ @classmethod
+ def from_options(
+ cls,
+ *,
+ model_options: Optional[dict],
+ latent_mask: torch.Tensor,
+ abt: torch.Tensor,
+ default_threshold: float,
+ default_patience: int,
+ default_distance_fn: Optional[Callable[..., Any]],
+ ) -> Optional["LanPaintEarlyStopper"]:
+ semantic_stop = model_options.get("lanpaint_semantic_stop") if isinstance(model_options, dict) else None
+
+ threshold = float(default_threshold)
+ patience = int(default_patience)
+ distance_fn = default_distance_fn
+ # distance_fn contract: return None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor)
+
+ if isinstance(semantic_stop, dict):
+ threshold = float(semantic_stop.get("threshold", threshold))
+ patience = int(semantic_stop.get("patience", patience))
+ distance_fn = semantic_stop.get("distance_fn", distance_fn)
+
+ # Backward compatibility: map legacy 'min_steps' to a patience floor so it is not an independent knob.
+ if patience > 0:
+ min_steps = semantic_stop.get("min_steps")
+ if min_steps is not None:
+ try:
+ min_steps_int = int(min_steps)
+ except (TypeError, ValueError):
+ min_steps_int = 0
+ if min_steps_int > 1:
+ patience = max(patience, min_steps_int - 1)
+
+ enabled_early_stop = (threshold > 0.0) and (patience > 0)
+ # Require N+1 consecutive stable checks:
+ # - the first stable step sets patience_counter to 1
+ # - `patience=1` therefore stops after 2 stable steps
+ patience_eff = max(1, patience) + 1
+ threshold_eff = threshold
+ inpaint_weight = ring_weight = trace = abt_val = None
+
+ if enabled_early_stop:
+ try:
+ abt_val = float(torch.mean(abt).item())
+ except (TypeError, ValueError):
+ abt_val = 0.0
+
+ threshold_eff = threshold * _abt_scale(abt_val)
+ if threshold_eff <= 0.0:
+ enabled_early_stop = False
+ else:
+ inpaint_weight = (1 - latent_mask).to(dtype=torch.float32)
+ if float(torch.sum(inpaint_weight).item()) < 1e-6:
+ enabled_early_stop = False
+ else:
+ ring_weight = _boundary_weight(latent_mask, inpaint_weight)
+ if isinstance(model_options, dict):
+ trace = model_options.get("lanpaint_semantic_trace")
+
+ if not enabled_early_stop:
+ return None
+
+ # Pre-fetch trace keys to avoid repeated dict lookups
+ bench_case_id = bench_outer_step = bench_timestep = None
+ if isinstance(trace, list) and isinstance(model_options, dict):
+ bench_case_id = model_options.get("bench_case_id")
+ bench_outer_step = model_options.get("bench_outer_step")
+ bench_timestep = model_options.get("bench_timestep")
+
+ return cls(
+ enabled=enabled_early_stop,
+ threshold=threshold,
+ threshold_eff=threshold_eff,
+ patience_eff=patience_eff,
+ inpaint_weight=inpaint_weight,
+ ring_weight=ring_weight,
+ distance_fn=distance_fn,
+ trace=trace,
+ bench_case_id=bench_case_id,
+ bench_outer_step=bench_outer_step,
+ bench_timestep=bench_timestep,
+ abt_val=abt_val,
+ )
+
+ def __init__(
+ self,
+ *,
+ enabled: bool,
+ threshold: float,
+ threshold_eff: float,
+ patience_eff: int,
+ inpaint_weight: Optional[torch.Tensor],
+ ring_weight: Optional[torch.Tensor],
+ distance_fn: Optional[Callable[..., Any]] = None,
+ trace: Optional[list] = None,
+ bench_case_id: Any = None,
+ bench_outer_step: Any = None,
+ bench_timestep: Any = None,
+ abt_val: Optional[float] = None,
+ ) -> None:
+ self.enabled = bool(enabled)
+ self.threshold = float(threshold)
+ self.threshold_eff = float(threshold_eff)
+ self.patience_eff = int(patience_eff)
+
+ self.inpaint_weight = inpaint_weight
+ self.ring_weight = ring_weight
+
+ self.trace = trace
+ self.bench_case_id = bench_case_id
+ self.bench_outer_step = bench_outer_step
+ self.bench_timestep = bench_timestep
+ self.abt_val = abt_val
+
+ self.patience_counter = 0
+ self.x0_anchor = None
+
+ self._dist_wrapper = self._wrap_distance_fn(distance_fn) if self.enabled else None
+
+ @property
+ def has_custom_distance_fn(self) -> bool:
+ return self._dist_wrapper is not None
+
+ @staticmethod
+ def _wrap_distance_fn(distance_fn: Optional[Callable[..., Any]]):
+ """
+ Wrap a user-provided `distance_fn` into a normalized callable: fn(prev, cur, ctx) -> dist|None.
+
+ Supported signatures:
+ - 3+ positional (or *args): `distance_fn(prev, cur, ctx)`
+ - explicit / **kwargs ctx: `distance_fn(prev, cur, ctx=ctx)`
+ - default 2-arg: `distance_fn(cur, prev)`
+
+ Return contract: None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor).
+ """
+ if not callable(distance_fn):
+ return None
+
+ try:
+ sig = inspect.signature(distance_fn)
+ params = list(sig.parameters.values())
+
+ has_ctx_param = "ctx" in sig.parameters
+ has_var_kw = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params)
+ has_var_pos = any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params)
+
+ pos_params = [
+ p
+ for p in params
+ if p.kind in (inspect.Parameter.POSITIONAL_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD)
+ ]
+
+ if len(pos_params) >= 3 or has_var_pos:
+ # 3-arg positional: fn(prev, cur, ctx)
+ return lambda p, c, ctx: distance_fn(p, c, ctx)
+ if has_ctx_param or has_var_kw:
+ # keyword ctx: fn(prev, cur, ctx=ctx)
+ return lambda p, c, ctx: distance_fn(p, c, ctx=ctx)
+
+ # Default 2-arg: fn(cur, prev)
+ return lambda p, c, ctx: distance_fn(c, p)
+ except (ValueError, TypeError):
+ # Fallback for built-ins or complex callables.
+ def fallback_wrapper(p, c, ctx):
+ try:
+ return distance_fn(p, c, ctx)
+ except TypeError as e:
+ tb = e.__traceback__
+ if tb is not None and tb.tb_frame.f_code is not fallback_wrapper.__code__:
+ raise
+ return distance_fn(c, p)
+
+ return fallback_wrapper
+
+ def step(
+ self,
+ *,
+ i: int,
+ n_steps: int,
+ x_t_before: torch.Tensor,
+ x_t_after: torch.Tensor,
+ x_t_prev_for_custom: Optional[torch.Tensor],
+ prev_args: Any,
+ args: Any,
+ ctx: dict,
+ ) -> bool:
+ if not self.enabled:
+ return False
+
+ # 'inpaint_weight' is guaranteed to be set when enabled is True in the caller.
+ inpaint = self.inpaint_weight
+ if inpaint is None:
+ return False
+
+ dist = None
+ custom_dist = False
+ dist_inpaint = dist_ring = dist_drift = x0_prev = x0_cur = None
+
+ if self._dist_wrapper is not None:
+ dist = self._dist_wrapper(x_t_prev_for_custom, x_t_after, ctx)
+ if dist is not None:
+ if isinstance(dist, torch.Tensor):
+ if dist.numel() != 1:
+ raise TypeError("distance_fn must return None or a scalar / 0-d (1-element) tensor")
+ dist = float(dist.item())
+ else:
+ dist = float(dist)
+ custom_dist = dist is not None
+
+ if dist is None:
+ def _get_x0(arg: Any) -> Optional[torch.Tensor]:
+ if isinstance(arg, LangevinState):
+ return arg.x0
+ if isinstance(arg, tuple) and len(arg) >= 3:
+ return arg[2]
+ return None
+
+ x0_prev = _get_x0(prev_args)
+ x0_cur = _get_x0(args)
+
+ if x0_prev is not None and x0_cur is not None:
+ dist_inpaint = _weighted_mse(x0_cur, x0_prev, inpaint)
+ dist_ring = _weighted_mse(x0_cur, x0_prev, self.ring_weight) if self.ring_weight is not None else None
+ dist = dist_inpaint if dist_ring is None else max(dist_inpaint, dist_ring)
+ else:
+ dist_inpaint = _weighted_mse(x_t_after, x_t_before, inpaint)
+ dist = dist_inpaint
+
+ threshold_used = self.threshold if custom_dist else self.threshold_eff
+
+ # Drift guard (only for default metric with x0_cur).
+ if x0_cur is not None and not custom_dist:
+ if dist <= threshold_used:
+ if self.x0_anchor is None:
+ self.x0_anchor = x0_cur.detach()
+ else:
+ drift_inpaint = _weighted_mse(x0_cur, self.x0_anchor, inpaint)
+ drift_ring = _weighted_mse(x0_cur, self.x0_anchor, self.ring_weight) if self.ring_weight is not None else None
+ dist_drift = drift_inpaint if drift_ring is None else max(drift_inpaint, drift_ring)
+ dist = max(dist, dist_drift)
+ else:
+ self.x0_anchor = None
+
+ if dist <= threshold_used:
+ self.patience_counter += 1
+ else:
+ self.patience_counter = 0
+ self.x0_anchor = None
+
+ should_stop = self.patience_counter >= self.patience_eff
+
+ if isinstance(self.trace, list):
+ self.trace.append(
+ {
+ "case_id": self.bench_case_id,
+ "outer_step": self.bench_outer_step,
+ "bench_timestep": self.bench_timestep,
+ "inner_step": i + 1,
+ "dist": dist,
+ "dist_inpaint": None if dist_inpaint is None else float(dist_inpaint),
+ "dist_ring": None if dist_ring is None else float(dist_ring),
+ "dist_drift": None if dist_drift is None else float(dist_drift),
+ "threshold": float(threshold_used),
+ "threshold_eff": float(self.threshold_eff),
+ "patience_counter": int(self.patience_counter),
+ "patience_eff": int(self.patience_eff),
+ "abt": None if self.abt_val is None else float(self.abt_val),
+ "custom_dist": bool(custom_dist),
+ "stopped": bool(should_stop),
+ }
+ )
+
+ return bool(should_stop)
+
diff --git a/LanPaint/build/lib/LanPaint/lanpaint.py b/LanPaint/build/lib/LanPaint/lanpaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..70b38d54abda0771d0f4c13a645b55f28fefe126
--- /dev/null
+++ b/LanPaint/build/lib/LanPaint/lanpaint.py
@@ -0,0 +1,272 @@
+import torch
+from .utils import StochasticHarmonicOscillator
+from functools import partial
+from .earlystop import LanPaintEarlyStopper
+from .types import LangevinState
+
+class LanPaint():
+ def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False, EarlyStopThreshold = 0.0, EarlyStopPatience = 1, EarlyStopHook = None):
+ self.n_steps = NSteps
+ self.chara_lamb = Lambda
+ self.IS_FLUX = IS_FLUX
+ self.IS_FLOW = IS_FLOW
+ self.step_size = StepSize
+ self.inner_model = Model
+ self.friction = Friction
+ self.chara_beta = Beta
+ self.img_dim_size = None
+ self.early_stop_threshold = EarlyStopThreshold
+ self.early_stop_patience = EarlyStopPatience
+ self.early_stop_hook = EarlyStopHook
+
+ def add_none_dims(self, array):
+ # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+ index = (slice(None),) + (None,) * (self.img_dim_size-1)
+ return array[index]
+ def remove_none_dims(self, array):
+ # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+ index = (slice(None),) + (0,) * (self.img_dim_size-1)
+ return array[index]
+ def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None):
+ self.img_dim_size = len(x.shape)
+ self.latent_image = latent_image
+ self.noise = noise
+ if torch.mean(torch.abs(self.noise)) < 1e-8:
+ self.noise = torch.randn_like(self.noise)
+ if n_steps is None:
+ n_steps = self.n_steps
+ return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
+ def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW):
+ input_x = x
+ VE_Sigma, abt, Flow_t = current_times
+
+ step_size = self.step_size * (1 - abt)
+ step_size = self.add_none_dims(step_size)
+ # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
+ # This is the replace step
+ 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:
+ x_t = x / ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
+
+ ############ LanPaint Iterations Start ###############
+ # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
+ args = None
+ stopper = LanPaintEarlyStopper.from_options(
+ model_options=model_options if isinstance(model_options, dict) else None,
+ latent_mask=latent_mask,
+ abt=abt,
+ default_threshold=self.early_stop_threshold,
+ default_patience=self.early_stop_patience,
+ default_distance_fn=self.early_stop_hook,
+ )
+
+ for i in range(n_steps):
+ score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = self.add_none_dims(abt), sigma = self.add_none_dims(VE_Sigma), tflow = self.add_none_dims(Flow_t), model_options = model_options, seed = seed )
+
+ prev_args = args
+ x_t_prev = x_t.detach() if (stopper is not None and stopper.has_custom_distance_fn) else None
+ x_t_before = x_t if (stopper is not None and stopper.enabled) else None
+
+ x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)
+
+ if stopper is not None:
+ ctx = {
+ "step": i,
+ "steps_done": i + 1,
+ "n_steps": n_steps,
+ "mask": latent_mask,
+ "latent_image": self.latent_image,
+ "current_times": current_times,
+ "seed": seed,
+ }
+ if stopper.step(
+ i=i,
+ n_steps=n_steps,
+ x_t_before=x_t_before,
+ x_t_after=x_t,
+ x_t_prev_for_custom=x_t_prev,
+ prev_args=prev_args,
+ args=args,
+ ctx=ctx,
+ ):
+ break
+
+ if IS_FLUX or IS_FLOW:
+ x = x_t / ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
+ else:
+ x = x_t * ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
+ ############ LanPaint Iterations End ###############
+ # out is x_0
+
+ out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+ out = out * (1-latent_mask) + self.latent_image * latent_mask
+
+ input_x.copy_(x)
+ return out
+
+ def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):
+ lamb = self.chara_lamb
+ if self.IS_FLUX or self.IS_FLOW:
+ # compute t for flow model, with a small epsilon compensating for numerical error.
+ x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching
+ x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
+ else:
+ x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding
+ x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)
+
+ score_x = -(x_t - x_0)
+ score_y = - (1 + lamb) * ( x_t - y ) + lamb * (x_t - x_0_BIG)
+ return score_x * (1 - mask) + score_y * mask
+ def sigma_x(self, abt):
+ # the time scale for the x_t update
+ return abt**0
+ def sigma_y(self, abt):
+ beta = self.chara_beta * abt ** 0
+ return beta
+
+ def langevin_dynamics(self, x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None):
+ if args is not None and not isinstance(args, LangevinState):
+ if isinstance(args, tuple):
+ if len(args) == 2:
+ # Backwards compat: older state was (v, C) without x0.
+ args = LangevinState(args[0], args[1], None)
+ elif len(args) >= 3:
+ args = LangevinState(args[0], args[1], args[2])
+ # prepare the step size and time parameters
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ step_sizes = self.prepare_step_size(current_times, step_size, sigma_x, sigma_y)
+ sigma, abt, dtx, dty, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y = step_sizes
+ # print('mask',mask.device)
+ if torch.mean(dtx) <= 0.:
+ return x_t, args
+ # -------------------------------------------------------------------------
+ # 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)
+ A = A_x * (1-mask) + A_y * mask
+ D = D_x * (1-mask) + D_y * mask
+ dt = dtx * (1-mask) + dty * mask
+ Gamma = Gamma_x * (1-mask) + Gamma_y * mask
+
+ def Coef_C(x_t):
+ x0 = x_t + score(x_t)
+ C = (abt**0.5 * x0 - x_t )/ (1-abt) + A * x_t
+ return C, x0
+ def advance_time(x_t, v, dt, Gamma, A, C, D):
+ dtype = x_t.dtype
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ osc = StochasticHarmonicOscillator(Gamma, A, C, D )
+ x_t, v = osc.dynamics(x_t, v, dt )
+ x_t = x_t.to(dtype)
+ v = v.to(dtype)
+ return x_t, v
+
+ def advance_time_overdamped(x_t, dt, A, C, D):
+ """
+ Overdamped (Gamma -> infinity) limit:
+ dx = -A x dt + C dt + D dW_t
+ with C treated as constant over this substep.
+ """
+ dtype = x_t.dtype
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ A_dt = A * dt
+ exp_neg = torch.exp(-A_dt)
+
+ eps = 1e-8
+ abs_A = torch.abs(A)
+ # k = (1 - exp(-A dt)) / A -> dt when A -> 0
+ k = torch.where(abs_A < eps, dt, (-torch.expm1(-A_dt)) / A)
+ # k2 = (1 - exp(-2 A dt)) / (2 A) -> dt when A -> 0
+ k2 = torch.where(abs_A < eps, dt, (-torch.expm1(-2 * A_dt)) / (2 * A))
+
+ mean = exp_neg * x_t + k * C
+ var = (D ** 2) * k2
+ noise = torch.randn_like(x_t) * torch.sqrt(torch.clamp(var, min=0.0))
+ x_t = mean + noise
+ return x_t.to(dtype)
+
+ def run_damped(x_t, args):
+ if args is None:
+ v = None
+ C, x0 = Coef_C(x_t)
+ x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
+ else:
+ v = args.v
+ C = args.C
+ x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+ C_new, x0 = Coef_C(x_t)
+ v = v + Gamma**0.5 * ( C_new - C) *dt
+ x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+ C = C_new
+ # args is (v, C, x0) for the next inner step.
+ return x_t, LangevinState(v, C, x0)
+
+ def run_overdamped(x_t, args):
+ if args is None:
+ C, x0 = Coef_C(x_t)
+ x_t = advance_time_overdamped(x_t, dt, A, C, D)
+ else:
+ C = args.C
+ x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+ C_new, x0 = Coef_C(x_t)
+ x_t = x_t + (C_new - C) * dt
+ x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+ C = C_new
+ # args is (v, C, x0); v is None in the overdamped fallback.
+ return x_t, LangevinState(None, C, x0)
+
+ try:
+ x_t_next, state = run_damped(x_t, args)
+
+ v_next = state.v
+ if torch.isnan(x_t_next).any() or (v_next is not None and torch.isnan(v_next).any()):
+ raise ValueError("NaN detected")
+
+ x_t = x_t_next
+ except Exception:
+ x_t, state = run_overdamped(x_t, args)
+
+ # args is (v, C, x0); v can be None if we fell back to the overdamped update.
+ return x_t, state
+
+ def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
+ # -------------------------------------------------------------------------
+ # Unpack current times parameters (sigma and abt)
+ sigma, abt, flow_t = current_times
+ sigma = self.add_none_dims(sigma)
+ abt = self.add_none_dims(abt)
+ # Compute time step (dtx, dty) for x and y branches.
+ dtx = 2 * step_size * sigma_x
+ dty = 2 * step_size * sigma_y
+
+ # -------------------------------------------------------------------------
+ # Define friction parameter Gamma_hat for each branch.
+ # Using dtx**0 provides a tensor of the proper device/dtype.
+
+ Gamma_hat_x = self.friction **2 * self.step_size * sigma_x / 0.1 * sigma**0
+ Gamma_hat_y = self.friction **2 * self.step_size * sigma_y / 0.1 * sigma**0
+ #print("Gamma_hat_x", torch.mean(Gamma_hat_x).item(), "Gamma_hat_y", torch.mean(Gamma_hat_y).item())
+ # adjust dt to match denoise-addnoise steps sizes
+ Gamma_hat_x /= 2.
+ Gamma_hat_y /= 2.
+ A_t_x = (1) / ( 1 - abt ) * dtx / 2
+ A_t_y = (1+self.chara_lamb) / ( 1 - abt ) * dty / 2
+
+
+ A_x = A_t_x / (dtx/2)
+ A_y = A_t_y / (dty/2)
+ Gamma_x = Gamma_hat_x / (dtx/2)
+ Gamma_y = Gamma_hat_y / (dty/2)
+
+ #D_x = (2 * (1 + sigma**2) )**0.5
+ #D_y = (2 * (1 + sigma**2) )**0.5
+ D_x = (2 * abt**0 )**0.5
+ D_y = (2 * abt**0 )**0.5
+ return sigma, abt, dtx/2, dty/2, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y
diff --git a/LanPaint/build/lib/LanPaint/nodes.py b/LanPaint/build/lib/LanPaint/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c7064605b6540ebb8f2b5c5f43d4aa3a9048bd3
--- /dev/null
+++ b/LanPaint/build/lib/LanPaint/nodes.py
@@ -0,0 +1,648 @@
+from contextlib import contextmanager
+import math
+# import nodes.py
+import comfy
+import nodes
+import latent_preview
+import torch
+from comfy.utils import repeat_to_batch_size
+from comfy.samplers import *
+from comfy.model_base import ModelType
+from .lanpaint import LanPaint
+from comfy.model_base import WAN22
+import comfyui_version
+
+def _version_tuple(value):
+ return tuple(int(part) if part.isdigit() else 0 for part in value.split("."))
+
+COMFYUI_VERSION_060_OR_NEWER = _version_tuple(comfyui_version.__version__) >= (0, 6, 0)
+
+def reshape_mask(input_mask, output_shape,video_inpainting=False):
+ dims = len(output_shape) - 2
+ print('output shape',output_shape)
+ scale_mode = "nearest-exact"
+ print('input mask',input_mask.shape,type(input_mask),torch.max(input_mask),torch.min(input_mask))
+ print('target output_shape',output_shape)
+ print('input_mask.ndim:', input_mask.ndim, 'output_shape len:', len(output_shape))
+
+ # Handle input mask dimensions
+ if input_mask.ndim == 2:
+ input_mask = input_mask.unsqueeze(0).unsqueeze(0)
+ elif input_mask.ndim == 3:
+ input_mask = input_mask.unsqueeze(1)
+
+ # Handle 5D output shape (B, C, F, H, W) by ensuring input is 5D
+ if len(output_shape) == 5 and input_mask.ndim == 4:
+ if COMFYUI_VERSION_060_OR_NEWER:
+ input_mask = input_mask.unsqueeze(2) # (B, C, 1, H, W)
+
+ # Handle video case with temporal dimension
+ if video_inpainting: # Video case: (batch, channels, frames, height, width)
+ target_frames = output_shape[2]
+ target_height, target_width = output_shape[-2:]
+
+ print('Video case - input_mask initial shape:', input_mask.shape)
+
+ # First reshape input_mask to have proper dimensions for video processing
+ # Assume input is (frames, channels, height, width) -> (1, channels, frames, height, width)
+ ## if comfy version < 0.6.0
+ if not COMFYUI_VERSION_060_OR_NEWER:
+ input_mask = input_mask.permute(1, 0, 2, 3).unsqueeze(0)
+ print('Video case - input_mask after reshaping:', input_mask.shape)
+ # Ensure we have the correct 5D shape: (batch, channels, frames, height, width)
+ batch_size, channels, frames, height, width = input_mask.shape
+ print('Video case - dimensions: batch_size={}, channels={}, frames={}, height={}, width={}'.format(batch_size, channels, frames, height, width))
+ print('Video case - target size:', (target_frames, target_height, target_width))
+
+ # 3D nearest-exact interpolation: (batch, channels, frames, height, width) -> (batch, channels, target_frames, target_height, target_width)
+ temp_mask = torch.nn.functional.interpolate(
+ input_mask,
+ size=(target_frames, target_height, target_width),
+ mode=scale_mode,
+ )
+
+ # temp_mask is already 5D: (batch, channels, target_frames, target_height, target_width)
+ mask = temp_mask
+ print('after mask',mask.shape)
+ # Handle channel dimension expansion if needed
+ if mask.shape[1] < output_shape[1]:
+ mask = mask.repeat(1, output_shape[1], 1, 1, 1)[:, :output_shape[1]]
+ # Handle batch dimension
+ mask = repeat_to_batch_size(mask, output_shape[0])
+ else: # Original 2D image case
+ if not COMFYUI_VERSION_060_OR_NEWER:
+ mask = torch.nn.functional.interpolate(input_mask, size=output_shape[-2:], mode=scale_mode)
+ else:
+ mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+ if mask.shape[1] < output_shape[1]:
+ mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+ mask = repeat_to_batch_size(mask, output_shape[0])
+
+
+ return mask
+def prepare_mask(noise_mask, shape, device,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
+ else:
+ uncond_ = uncond
+
+ conds = [cond, uncond_]
+ out = calc_cond_batch(model, conds, x, timestep, model_options)
+
+ for fn in model_options.get("sampler_pre_cfg_function", []):
+ args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
+ "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+ out = fn(args)
+
+ return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_), cfg_function(model, out[0], out[1], cond_scale_BIG, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
+
+
+class CFGGuider_LanPaint:
+ 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:
+ 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)
+ sigmas = sigmas.to(device)
+ cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
+
+ try:
+ self.model_patcher.pre_run()
+ output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs)
+ finally:
+ self.model_patcher.cleanup()
+
+ comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+ del self.inner_model
+ del self.loaded_models
+ return output
+ def predict_noise(self, x, timestep, model_options={}, seed=None):
+ return sampling_function_LanPaint(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, self.cfg_BIG, model_options=model_options, seed=seed)
+
+#CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample
+#CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise
+
+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 )
+ 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
+ # sigma is the noise level
+ ### For flux model
+ # x is rectified flow x_t = sigma * noise + (1.0 - sigma) * x_0
+
+ IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX
+ IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW
+ #print("model class", type(self.inner_model.inner_model))
+ #print("model type", self.inner_model.inner_model.model_type, "IS_FLUX", IS_FLUX, "IS_FLOW", IS_FLOW)
+ #print("sigma", torch.mean(sigma).item(), torch.min(sigma).item(), torch.max(sigma).item())
+ # unify the notations into variance exploding diffusion model
+ if IS_FLUX or IS_FLOW:
+ Flow_t = sigma
+ abt = (1 - Flow_t)**2 / ((1 - Flow_t)**2 + Flow_t**2 )
+ VE_Sigma = Flow_t / (1 - Flow_t)
+ #print("t", torch.mean( sigma ).item(), "VE_Sigma", torch.mean( VE_Sigma ).item())
+
+
+ else:
+ VE_Sigma = sigma
+ abt = 1/( 1+VE_Sigma**2 )
+ Flow_t = (1-abt)**0.5 / ( (1-abt)**0.5 + abt**0.5 )
+
+ if denoise_mask is not None:
+ if "denoise_mask_function" in model_options:
+ denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
+
+ denoise_mask = (denoise_mask > 0.5).float()
+
+ latent_mask = 1 - denoise_mask
+ current_times = (VE_Sigma, abt, Flow_t)
+
+ current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) )
+ total_steps = len(self.sigmas)-1
+
+ if total_steps - current_step <= self.LanPaint_early_stop:
+ out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed, n_steps=0)
+ else:
+ out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed)
+ else:
+ out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+
+ # Add TAESD preview support - directly use the latent_preview module
+ current_step = model_options.get("i", kwargs.get("i", 0))
+ total_steps = model_options.get("total_steps", 0)
+
+ # Only show preview every few steps to improve performance
+ if current_step % 2 == 0:
+ # Directly call the preview callback if it exists
+ callback = model_options.get("callback", None)
+ if callback is not None:
+ callback({"i": current_step, "denoised": out, "x": x})
+
+ return out
+
+# Custom sampler class extending ComfyUI's KSAMPLER for LanPaint
+class KSAMPLER(comfy.samplers.KSAMPLER):
+ def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+ #noise here is a randn noise from comfy.sample.prepare_noise
+ #latent_image is the latent image as input of the KSampler node. For inpainting, it is the masked latent image. Otherwise it is zero tensor.
+ extra_args["denoise_mask"] = denoise_mask
+ model_k = KSamplerX0Inpaint(model_wrap, sigmas)
+ model_k.latent_image = latent_image
+ if self.inpaint_options.get("random", False): #TODO: Should this be the default?
+ generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
+ model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
+ else:
+ model_k.noise = noise
+
+ IS_FLUX = model_wrap.inner_model.model_type == ModelType.FLUX
+ IS_FLOW = model_wrap.inner_model.model_type == ModelType.FLOW
+ # unify the notations into variance exploding diffusion model
+ if IS_FLUX:
+ model_wrap.cfg_BIG = 1.0
+ else:
+ model_wrap.cfg_BIG = model_wrap.model_patcher.LanPaint_cfg_BIG
+ noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
+
+ model_k.PaintMethod = LanPaint(model_k.inner_model,
+ model_wrap.model_patcher.LanPaint_NumSteps,
+ model_wrap.model_patcher.LanPaint_Friction,
+ model_wrap.model_patcher.LanPaint_Lambda,
+ model_wrap.model_patcher.LanPaint_Beta,
+ model_wrap.model_patcher.LanPaint_StepSize,
+ IS_FLUX = IS_FLUX,
+ IS_FLOW = IS_FLOW,
+ EarlyStopThreshold = getattr(model_wrap.model_patcher, "LanPaint_InnerThreshold", 0.0),
+ EarlyStopPatience = getattr(model_wrap.model_patcher, "LanPaint_InnerPatience", 1),
+ EarlyStopHook = extra_args.get("model_options", {}).get("lanpaint_semantic_hook", None))
+ model_k.LanPaint_early_stop = model_wrap.model_patcher.LanPaint_EarlyStop
+ #if not inpainting, after noise_scaling, noise = noise * sigma, which is the noise added to the clean latent image in the variance exploding diffusion model notation.
+ #if inpainting, after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
+ k_callback = None
+ total_steps = len(sigmas) - 1
+ if callback is not None:
+ k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
+ #print("LanPaint KSampler call sampler_function", self.sampler_function)
+ # The main loop!
+ #print("##########")
+ #print("Sampling with ", self.sampler_function)
+ #print("##########")
+ samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
+ #print("LanPaint KSampler end sampler_function")
+ samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
+ return samples
+
+@contextmanager
+def override_sample_function():
+ original_outer_sample = comfy.samplers.CFGGuider.outer_sample
+ comfy.samplers.CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample
+
+ original_predict_noise = comfy.samplers.CFGGuider.predict_noise
+ comfy.samplers.CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise
+
+ original_sample = comfy.samplers.KSAMPLER.sample
+ comfy.samplers.KSAMPLER.sample = KSAMPLER.sample
+
+ try:
+ yield
+ finally:
+ comfy.samplers.KSAMPLER.sample = original_sample
+ comfy.samplers.CFGGuider.predict_noise = original_predict_noise
+ comfy.samplers.CFGGuider.outer_sample = original_outer_sample
+
+
+class LanPaint_UpSale_LatentNoiseMask:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "samples": ("LATENT",),
+ "scale": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1}),
+ }}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "set_mask"
+
+
+ CATEGORY = "latent/inpaint"
+
+ def set_mask(self, samples, scale):
+ s = samples.copy()
+ samples = s['samples']
+ # generate a mask with every scaleth pixel set to 1
+ mask = torch.zeros(samples.shape[0], 1, samples.shape[2], samples.shape[3], device=samples.device) + 1
+ mask[:, :, ::scale, ::scale] = 0
+ s["noise_mask"] = mask
+ return (s,)
+
+#KSAMPLER_NAMES = ["euler", "dpmpp_2m", "uni_pc"]
+KSAMPLER_NAMES = ["euler","euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+ "dpm_fast", "dpmpp_sde", "dpmpp_sde_gpu",
+ "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm",
+ "deis", "res_multistep", "res_multistep_ancestral",
+ "gradient_estimation", "er_sde", "seeds_2", "seeds_3"]
+
+class LanPaint_KSampler():
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}),
+ "steps": ("INT", {"default": 30, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
+ "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
+ "sampler_name": (KSAMPLER_NAMES, {"tooltip": "Recommended: euler."}),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras", "tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
+ "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
+ "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
+ "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
+ "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"}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ OUTPUT_TOOLTIPS = ("The denoised latent.",)
+ FUNCTION = "sample"
+
+ 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="",Inpainting_mode="🖼️ Image Inpainting"):
+
+ model.LanPaint_StepSize = 0.2
+ model.LanPaint_Lambda = 16.0
+ model.LanPaint_Beta = 1.
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = 15.
+ model.LanPaint_EarlyStop = 1
+ model.LanPaint_InnerThreshold = 0.0
+ model.LanPaint_InnerPatience = 1
+ if LanPaint_PromptMode == "Image First":
+ 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:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": (["enable", "disable"], ),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "sampler_name": (KSAMPLER_NAMES, ),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "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"], ),
+ "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": 0.2, "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"}),
+ "LanPaint_InnerThreshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001, "round": 0.0001, "tooltip": "Early stop threshold for Langevin iterations based on semantic distance. 0.0 to disable. (Contributed by godnight10061)"}),
+ "LanPaint_InnerPatience": ("INT", {"default": 1, "min": 1, "max": 100, "tooltip": "Number of consecutive steps below threshold required to stop. (Contributed by godnight10061)"}),
+ },
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "sample"
+
+ 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, 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", LanPaint_InnerThreshold=0.0, LanPaint_InnerPatience=1):
+ force_full_denoise = True
+ if return_with_leftover_noise == "enable":
+ force_full_denoise = False
+ disable_noise = False
+ if add_noise == "disable":
+ disable_noise = True
+ model.LanPaint_StepSize = LanPaint_StepSize
+ model.LanPaint_Lambda = LanPaint_Lambda
+ model.LanPaint_Beta = LanPaint_Beta
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = LanPaint_Friction
+ model.LanPaint_EarlyStop = LanPaint_EarlyStop
+ model.LanPaint_InnerThreshold = LanPaint_InnerThreshold
+ model.LanPaint_InnerPatience = LanPaint_InnerPatience
+ if LanPaint_PromptMode == "Image First":
+ 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=1.0, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
+
+
+class MaskBlend:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image1": ("IMAGE", {"tooltip": "Image before inpaint"}),
+ "image2": ("IMAGE", {"tooltip": "Image after inpaint"}),
+ "mask": ("MASK",),
+ "blend_overlap": ("INT", {"default": 1, "min": 1, "max": 51, "step": 2, "tooltip": "The number of pixels to blend between the two images."})
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "blend_images"
+
+ CATEGORY = "image/postprocessing"
+
+ def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, mask: torch.Tensor, blend_overlap: int):
+ # smooth the binary 01 mask, keep 1 still 1, but smooth the transition from 1 to 0
+ # for each mask pixel, find out the nearest 1 pixel, and set the mask value to the distance between the two pixels
+ # check the size of mask and image1, image2, if not the same, assert error
+ if image1.shape[1] != image2.shape[1] or image1.shape[2] != image2.shape[2]:
+ raise ValueError(
+ "Image size mismatch: Image1 and Image2 must have the same dimensions.\n"
+ "Additionally, ensure both images have width and height that are multiples of 8.\n"
+ "This is required because VAE decode always generates images with dimensions that are multiples of 8.\n"
+ "If your input images are not multiples of 8, a size mismatch will occur during the decoding process.\n"
+ "Please resize your images using an image resize node to ensure compatibility.\n"
+ "Current sizes - Image1: {}x{}, Image2: {}x{}".format(
+ image1.shape[2], image1.shape[1], image2.shape[2], image2.shape[1]
+ )
+ )
+ mask = mask.float()
+ mask = torch.nn.functional.max_pool2d(mask, kernel_size=blend_overlap, stride=1, padding=blend_overlap//2)
+ # apply Gaussian blur with kernel size blend_overlap
+ kernel = self.gaussian_kernel(blend_overlap)
+ kernel = kernel.to(image1.device)
+ kernel = kernel[None, None, ...]
+
+ mask = torch.nn.functional.conv2d(mask[:,None,:,:], kernel, padding=blend_overlap//2)[:,0,:,:]
+
+
+ blended_image = image1 * (1 - mask[...,None]) + image2 * mask[...,None]
+ return (blended_image,)
+ def gaussian_kernel(self,kernel_size):
+ """
+ Creates a 2D Gaussian kernel with the given size and standard deviation (sigma).
+ """
+ sigma = (kernel_size - 1)/4
+ # Create a grid of (x, y) coordinates
+ x = torch.arange(kernel_size).float() - kernel_size // 2
+ y = torch.arange(kernel_size).float() - kernel_size // 2
+ x_grid, y_grid = torch.meshgrid(x, y, indexing='ij')
+
+ # Compute the Gaussian function
+ kernel = torch.exp(-(x_grid ** 2 + y_grid ** 2) / (2 * sigma ** 2))
+ kernel = kernel / kernel.sum() # Normalize the kernel
+
+ return kernel
+
+class Noise_EmptyNoise:
+ def generate_noise(self, latent):
+ return torch.zeros_like(latent["samples"])
+
+class Noise_RandomNoise:
+ def __init__(self, seed):
+ self.seed = seed
+ def generate_noise(self, latent):
+ torch.manual_seed(self.seed)
+ return torch.randn_like(latent["samples"])
+
+# Custom sampler implementation mimmicking base comfy nodes_custom_sampler.py
+class LanPaint_SamplerCustom:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": ("BOOLEAN", {"default": True}),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+ "positive": ("CONDITIONING",),
+ "negative": ("CONDITIONING",),
+ "sampler": ("SAMPLER",),
+ "sigmas": ("SIGMAS",),
+ "latent_image": ("LATENT",),
+ "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
+ "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
+ "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT", "LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+ FUNCTION = "sample"
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(self, model, sampler, sigmas, add_noise, noise_seed, cfg, positive, negative, latent_image, LanPaint_NumSteps, LanPaint_PromptMode, LanPaint_Info=""):
+ model.LanPaint_StepSize = 0.2
+ model.LanPaint_Lambda = 16.0
+ model.LanPaint_Beta = 1.
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = 15.
+ model.LanPaint_EarlyStop = 1
+ model.LanPaint_InnerThreshold = 0.0
+ model.LanPaint_InnerPatience = 1
+ if LanPaint_PromptMode == "Image First":
+ model.LanPaint_cfg_BIG = cfg
+ else:
+ model.LanPaint_cfg_BIG = 0 * cfg - 0.5
+ with override_sample_function():
+ latent = latent_image.copy()
+ latent_image = latent["samples"]
+ latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+ latent["samples"] = latent_image
+
+ if not add_noise:
+ noise = Noise_EmptyNoise().generate_noise(latent)
+ else:
+ noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+ noise_mask = None
+ if "noise_mask" in latent:
+ noise_mask = latent["noise_mask"]
+
+ x0_output = {}
+ callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+ disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+ samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+ out = latent.copy()
+ out["samples"] = samples
+ if "x0" in x0_output:
+ out_denoised = latent.copy()
+ out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+ else:
+ out_denoised = out
+ return (out, out_denoised)
+
+class LanPaint_SamplerCustomAdvanced:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"noise": ("NOISE",),
+ "guider": ("GUIDER", ),
+ "sampler": ("SAMPLER", ),
+ "sigmas": ("SIGMAS", ),
+ "latent_image": ("LATENT", ),
+ "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
+ "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
+ "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+ "LanPaint_Beta": ("FLOAT", {"default": 1.0, "min": 0.0001, "max": 5.0, "step": 0.1, "tooltip": "Step size ratio between masked/unmasked regions. Lower values balance high Lambda."}),
+ "LanPaint_Friction": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Friction parameter for fast Langevin. Lower values speed convergence but may be unstable."}),
+ "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
+ "LanPaint_EarlyStop": ("INT", {"default": 1, "min": 0, "max": 10000, "tooltip": "Steps to stop LanPaint early, preventing irregular patterns."}),
+ "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler Adv. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+ "LanPaint_InnerThreshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001, "round": 0.0001, "tooltip": "Early stop threshold for Langevin iterations based on semantic distance. 0.0 to disable. (Contributed by godnight10061)"}),
+ "LanPaint_InnerPatience": ("INT", {"default": 1, "min": 1, "max": 100, "tooltip": "Number of consecutive steps below threshold required to stop. (Contributed by godnight10061)"}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT","LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+
+ FUNCTION = "sample"
+
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(self, noise, guider, sampler, sigmas, latent_image, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info="", LanPaint_InnerThreshold=0.0, LanPaint_InnerPatience=1):
+ model = guider.model_patcher
+ model.LanPaint_StepSize = LanPaint_StepSize
+ model.LanPaint_Lambda = LanPaint_Lambda
+ model.LanPaint_Beta = LanPaint_Beta
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = LanPaint_Friction
+ model.LanPaint_EarlyStop = LanPaint_EarlyStop
+ model.LanPaint_InnerThreshold = LanPaint_InnerThreshold
+ model.LanPaint_InnerPatience = LanPaint_InnerPatience
+ if LanPaint_PromptMode == "Image First":
+ model.LanPaint_cfg_BIG = guider.cfg
+ else:
+ model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
+ with override_sample_function():
+ 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"]
+
+ x0_output = {}
+ callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
+ disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+ samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+
+ out = latent.copy()
+ out["samples"] = samples
+ if "x0" in x0_output:
+ out_denoised = latent.copy()
+ out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+ else:
+ out_denoised = out
+ 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 = {
+ "LanPaint_KSampler": LanPaint_KSampler,
+ "LanPaint_KSamplerAdvanced": LanPaint_KSamplerAdvanced,
+ "LanPaint_SamplerCustom" : LanPaint_SamplerCustom,
+ "LanPaint_SamplerCustomAdvanced" : LanPaint_SamplerCustomAdvanced,
+ "LanPaint_MaskBlend": MaskBlend,
+# "LanPaint_UpSale_LatentNoiseMask": LanPaint_UpSale_LatentNoiseMask,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "LanPaint_KSampler": "LanPaint KSampler",
+ "LanPaint_KSamplerAdvanced": "LanPaint KSampler (Advanced)",
+ "LanPaint_SamplerCustom" : "LanPaint Sampler Custom",
+ "LanPaint_SamplerCustomAdvanced" : "LanPaint Sampler Custom (Advanced)",
+ "LanPaint_MaskBlend": "LanPaint Mask Blend",
+# "LanPaint_UpSale_LatentNoiseMask": "LanPaint UpSale Latent Noise Mask"
+}
diff --git a/LanPaint/build/lib/LanPaint/types.py b/LanPaint/build/lib/LanPaint/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a738a857d08623d64fe153aed731c56440bd9cb0
--- /dev/null
+++ b/LanPaint/build/lib/LanPaint/types.py
@@ -0,0 +1,10 @@
+from typing import NamedTuple, Optional
+
+import torch
+
+
+class LangevinState(NamedTuple):
+ v: Optional[torch.Tensor]
+ C: Optional[torch.Tensor]
+ x0: Optional[torch.Tensor]
+
diff --git a/LanPaint/build/lib/LanPaint/utils.py b/LanPaint/build/lib/LanPaint/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..774ad4c7bd4980a27e6d892f7a5ed9ae70c03e25
--- /dev/null
+++ b/LanPaint/build/lib/LanPaint/utils.py
@@ -0,0 +1,300 @@
+import torch
+def epxm1_x(x):
+ # Compute the (exp(x) - 1) / x term with a small value to avoid division by zero.
+ result = torch.special.expm1(x) / x
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x) < 1e-2
+ result = torch.where(mask, 1 + x/2. + x**2 / 6., result)
+ return result
+def epxm1mx_x2(x):
+ # Compute the (exp(x) - 1 - x) / x**2 term with a small value to avoid division by zero.
+ result = (torch.special.expm1(x) - x) / x**2
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x**2) < 1e-2
+ result = torch.where(mask, 1/2. + x/6 + x**2 / 24 + x**3 / 120, result)
+ return result
+
+def expm1mxmhx2_x3(x):
+ # Compute the (exp(x) - 1 - x - x**2 / 2) / x**3 term with a small value to avoid division by zero.
+ result = (torch.special.expm1(x) - x - x**2 / 2) / x**3
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x**3) < 1e-2
+ result = torch.where(mask, 1/6 + x/24 + x**2 / 120 + x**3 / 720 + x**4 / 5040, result)
+ return result
+
+def exp_1mcosh_GD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ # Main computation
+ is_positive = delta > 0
+ sqrt_abs_delta = torch.sqrt(torch.abs(delta))
+ gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta
+ numerator_pos = torch.exp(-gamma_t) - (torch.exp(gamma_t * (sqrt_abs_delta - 1)) + torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2
+ numerator_neg = torch.exp(-gamma_t) * ( 1 - torch.cos(gamma_t * sqrt_abs_delta ) )
+ numerator = torch.where(is_positive, numerator_pos, numerator_neg)
+ result = numerator / (delta * gamma_t**2 )
+ # Handle NaN/inf cases
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ # Handle numerical instability for small delta
+ mask = torch.abs(gamma_t_sqrt_delta**2) < 5e-2
+ taylor = ( -0.5 - gamma_t**2 / 24 * delta - gamma_t**4 / 720 * delta**2 ) * torch.exp(-gamma_t)
+ result = torch.where(mask, taylor, result)
+ return result
+
+def exp_sinh_GsqrtD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ)
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ # Main computation
+ is_positive = delta > 0
+ sqrt_abs_delta = torch.sqrt(torch.abs(delta))
+ gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta
+ numerator_pos = (torch.exp(gamma_t * (sqrt_abs_delta - 1)) - torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2
+ result_pos = numerator_pos / gamma_t_sqrt_delta
+ result_pos = torch.where(torch.isfinite(result_pos), result_pos, torch.zeros_like(result_pos))
+
+ # Taylor expansion for small gamma_t_sqrt_delta
+ mask = torch.abs(gamma_t_sqrt_delta) < 1e-2
+ taylor = ( 1 + gamma_t**2 / 6 * delta + gamma_t**4 / 120 * delta**2 ) * torch.exp(-gamma_t)
+ result_pos = torch.where(mask, taylor, result_pos)
+
+ # Handle negative delta
+ result_neg = torch.exp(-gamma_t) * torch.special.sinc(gamma_t_sqrt_delta/torch.pi)
+ result = torch.where(is_positive, result_pos, result_neg)
+ return result
+
+def exp_cosh(gamma_t, delta):
+ """
+ Compute e^(-Γt) * cosh(Γt√Δ)
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_1mcosh_GD_result = exp_1mcosh_GD(gamma_t, delta) # e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+ result = torch.exp(-gamma_t) - gamma_t**2 * delta * exp_1mcosh_GD_result
+ return result
+def exp_sinh_sqrtD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * sinh(Γt√Δ) / √Δ
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_sinh_GsqrtD_result = exp_sinh_GsqrtD(gamma_t, delta) # e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ)
+ result = gamma_t * exp_sinh_GsqrtD_result
+ return result
+
+
+
+def zeta1(gamma_t, delta):
+ # Compute hyperbolic terms and exponential
+ half_gamma_t = gamma_t / 2
+ exp_cosh_term = exp_cosh(half_gamma_t, delta)
+ exp_sinh_term = exp_sinh_sqrtD(half_gamma_t, delta)
+
+
+ # Main computation
+ numerator = 1 - (exp_cosh_term + exp_sinh_term)
+ denominator = gamma_t * (1 - delta) / 4
+ result = 1 - numerator / denominator
+
+ # Handle numerical instability
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+
+ # Taylor expansion for small x (similar to your epxm1Dx approach)
+ mask = torch.abs(denominator) < 5e-3
+ term1 = epxm1_x(-gamma_t)
+ term2 = epxm1mx_x2(-gamma_t)
+ term3 = expm1mxmhx2_x3(-gamma_t)
+ taylor = term1 + (1/2.+ term1-3*term2)*denominator + (-1/6. + term1/2 - 4 * term2 + 10 * term3) * denominator**2
+ result = torch.where(mask, taylor, result)
+
+ return result
+
+def exp_cosh_minus_terms(gamma_t, delta):
+ """
+ Compute E^(-tΓ) * (Cosh[tΓ] - 1 - (Cosh[tΓ√Δ] - 1)/Δ) / (tΓ(1 - Δ))
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_term = torch.exp(-gamma_t)
+ # Compute individual terms
+ exp_cosh_term = exp_cosh(gamma_t, gamma_t**0) - exp_term # E^(-tΓ) (Cosh[tΓ] - 1) term
+ exp_cosh_delta_term = - gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) # E^(-tΓ) (Cosh[tΓ√Δ] - 1)/Δ term
+
+ #exp_1mcosh_GD e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+ # Main computation
+ numerator = exp_cosh_term - exp_cosh_delta_term
+ denominator = gamma_t * (1 - delta)
+
+ result = numerator / denominator
+
+ # Handle numerical instability
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+
+ # Taylor expansion for small gamma_t and delta near 1
+ mask = (torch.abs(denominator) < 1e-1)
+ exp_1mcosh_GD_term = exp_1mcosh_GD(gamma_t, delta**0)
+ taylor = (
+ gamma_t*exp_1mcosh_GD_term + 0.5 * gamma_t * exp_sinh_GsqrtD(gamma_t, delta**0)
+ - denominator / 4 * ( 0.5 * exp_cosh(gamma_t, delta**0) - 4 * exp_1mcosh_GD_term - 5 /2 * exp_sinh_GsqrtD(gamma_t, delta**0) )
+ )
+ result = torch.where(mask, taylor, result)
+
+ return result
+
+
+def zeta2(gamma_t, delta):
+ half_gamma_t = gamma_t / 2
+ return exp_sinh_GsqrtD(half_gamma_t, delta)
+
+def sig11(gamma_t, delta):
+ return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta)
+
+
+def Zcoefs(gamma_t, delta):
+ Zeta1 = zeta1(gamma_t, delta)
+ Zeta2 = zeta2(gamma_t, delta)
+
+ sq_total = 1 - Zeta1 + gamma_t * (delta - 1) * (Zeta1 - 1)**2 / 8
+ amplitude = torch.sqrt(sq_total)
+ Zcoef1 = ( gamma_t**0.5 * Zeta2 / 2 **0.5 ) / amplitude
+ Zcoef2 = Zcoef1 * gamma_t *( - 2 * exp_1mcosh_GD(gamma_t, delta) / sig11(gamma_t, delta) ) ** 0.5
+ #cterm = exp_cosh_minus_terms(gamma_t, delta)
+ #sterm = exp_sinh_sqrtD(gamma_t, delta**0) + exp_sinh_sqrtD(gamma_t, delta)
+ #Zcoef3 = 2 * torch.sqrt( cterm / ( gamma_t * (1 - delta) * cterm + sterm ) )
+ Zcoef3 = torch.sqrt( torch.maximum(1 - Zcoef1**2 - Zcoef2**2, sq_total.new_zeros(sq_total.shape)) )
+
+ return Zcoef1 * amplitude, Zcoef2 * amplitude, Zcoef3 * amplitude, amplitude
+
+def Zcoefs_asymp(gamma_t, delta):
+ A_t = (gamma_t * (1 - delta) )/4
+ return epxm1_x(- 2 * A_t)
+
+class StochasticHarmonicOscillator:
+ """
+ Simulates a stochastic harmonic oscillator governed by the equations:
+ dy(t) = q(t) dt
+ dq(t) = -Γ A y(t) dt + Γ C dt + Γ D dw(t) - Γ q(t) dt
+
+ Also define v(t) = q(t) / √Γ, which is numerically more stable.
+
+ Where:
+ y(t) - Position variable
+ q(t) - Velocity variable
+ Γ - Damping coefficient
+ A - Harmonic potential strength
+ C - Constant force term
+ D - Noise amplitude
+ dw(t) - Wiener process (Brownian motion)
+ """
+ def __init__(self, Gamma, A, C, D):
+ self.Gamma = Gamma
+ self.A = A
+ self.C = C
+ self.D = D
+ self.Delta = 1 - 4 * A / Gamma
+ def sig11(self, gamma_t, delta):
+ return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta)
+ def sig22(self, gamma_t, delta):
+ return 1- zeta1(2*gamma_t, delta) + 2 * gamma_t * exp_1mcosh_GD(gamma_t, delta)
+ def dynamics(self, y0, v0, t):
+ """
+ Calculates the position and velocity variables at time t.
+
+ Parameters:
+ y0 (float): Initial position
+ v0 (float): Initial velocity v(0) = q(0) / √Γ
+ t (float): Time at which to evaluate the dynamics
+ Returns:
+ tuple: (y(t), v(t))
+ """
+
+ dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0
+ Delta = self.Delta + dummyzero
+ Gamma_hat = self.Gamma * t + dummyzero
+ A = self.A + dummyzero
+ C = self.C + dummyzero
+ D = self.D + dummyzero
+ Gamma = self.Gamma + dummyzero
+ zeta_1 = zeta1( Gamma_hat, Delta)
+ zeta_2 = zeta2( Gamma_hat, Delta)
+ EE = 1 - Gamma_hat * zeta_2
+
+ if v0 is None:
+ v0 = torch.randn_like(y0) * D / 2 ** 0.5
+ #v0 = (C - A * y0)/Gamma**0.5
+
+ # Calculate mean position and velocity
+ term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t
+ y_mean = term1 + y0
+ v_mean = (1 - EE)*(C - A * y0) / (Gamma ** 0.5) + (EE - A * t * (1 - zeta_1)) * v0
+
+ cov_yy = D**2 * t * self.sig22(Gamma_hat, Delta)
+ cov_vv = D**2 * self.sig11(Gamma_hat, Delta) / 2
+ cov_yv = (zeta2(Gamma_hat, Delta) * Gamma_hat * D ) **2 / 2 / (Gamma ** 0.5)
+
+ # sample new position and velocity with multivariate normal distribution
+
+ batch_shape = y0.shape
+ cov_matrix = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
+ cov_matrix[..., 0, 0] = cov_yy
+ cov_matrix[..., 0, 1] = cov_yv
+ cov_matrix[..., 1, 0] = cov_yv # symmetric
+ cov_matrix[..., 1, 1] = cov_vv
+
+
+
+ # Compute the Cholesky decomposition to get scale_tril
+ #scale_tril = torch.linalg.cholesky(cov_matrix)
+ scale_tril = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
+ tol = 1e-8
+ cov_yy = torch.clamp( cov_yy, min = tol )
+ sd_yy = torch.sqrt( cov_yy )
+ inv_sd_yy = 1/(sd_yy)
+
+ scale_tril[..., 0, 0] = sd_yy
+ scale_tril[..., 0, 1] = 0.
+ scale_tril[..., 1, 0] = cov_yv * inv_sd_yy
+ scale_tril[..., 1, 1] = torch.clamp( cov_vv - cov_yv**2 / cov_yy, min = tol ) ** 0.5
+ # check if it matches torch.linalg.
+ #assert torch.allclose(torch.linalg.cholesky(cov_matrix), scale_tril, atol = 1e-4, rtol = 1e-4 )
+ # Sample correlated noise from multivariate normal
+ mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype)
+ mean[..., 0] = y_mean
+ mean[..., 1] = v_mean
+ new_yv = torch.distributions.MultivariateNormal(
+ loc=mean,
+ scale_tril=scale_tril
+ ).sample()
+
+ return new_yv[...,0], new_yv[...,1]
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+ "We are using quantized weights in this workflow, the original flux 2 repo is [here](https://huggingface.co/black-forest-labs/FLUX.2-dev/)\n\nGuide: [Subgraph](https://docs.comfy.org/interface/features/subgraph)\n\n## Report issue\n\nNote: please update ComfyUI first ([guide](https://docs.comfy.org/zh-CN/installation/update_comfyui)) and prepare the required models. Desktop/Cloud ship stable builds; nightly-supported models may not be included yet, please wait for the next stable release.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n\n\n## Model links (for local user)\n\n**text_encoders**\n\n- [mistral_3_small_flux2_bf16.safetensors](https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/text_encoders/mistral_3_small_flux2_bf16.safetensors)\n\n**loras**\n\n- [Flux_2-Turbo-LoRA_comfyui.safetensors](https://huggingface.co/ByteZSzn/Flux.2-Turbo-ComfyUI/resolve/main/Flux_2-Turbo-LoRA_comfyui.safetensors)\n\n**diffusion_models**\n\n- [flux2_dev_fp8mixed.safetensors](https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/diffusion_models/flux2_dev_fp8mixed.safetensors)\n\n**vae**\n\n- [flux2-vae.safetensors](https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/vae/flux2-vae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 text_encoders/\n│ │ └── mistral_3_small_flux2_bf16.safetensors\n│ ├── 📂 loras/\n│ │ └── Flux_2-Turbo-LoRA_comfyui.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── flux2_dev_fp8mixed.safetensors\n│ └── 📂 vae/\n│ └── flux2-vae.safetensors\n```\n"
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+ "Guide: [Subgraph](https://docs.comfy.org/interface/features/subgraph)\n\n## Model links (for local users)\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n**diffusion_models**\n\n- [lux-2-klein-base-4b-fp8.safetensors](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4b-fp8/resolve/main/flux-2-klein-base-4b-fp8.safetensors)\n\n**vae**\n\n- [flux2-vae.safetensors](https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/vae/flux2-vae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 text_encoders/\n│ │ └── qwen_3_4b.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── lux-2-klein-base-4b-fp8.safetensors\n│ └── 📂 vae/\n│ └── flux2-vae.safetensors\n```\n\n## Report issue\n\nNote: please update ComfyUI first ([guide](https://docs.comfy.org/zh-CN/installation/update_comfyui)) and prepare the required models. Desktop/Cloud ship stable builds; nightly-supported models may not be included yet, please wait for the next stable release.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n\n"
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diff --git a/LanPaint/example_workflows/Masked_Qwen_Image_Edit.jpg b/LanPaint/example_workflows/Masked_Qwen_Image_Edit.jpg
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diff --git a/LanPaint/example_workflows/Masked_Qwen_Image_Edit.json b/LanPaint/example_workflows/Masked_Qwen_Image_Edit.json
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+ "[Tutorial](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) | [教程](https://docs.comfy.org/zh-CN/tutorials/image/qwen/qwen-image-edit)\n\n\n## Model links\n\nYou can find all the models on [Comfy-Org/Qwen-Image_ComfyUI](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main) and [Comfy-Org/Qwen-Image-Edit_ComfyUI](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI) \n\n**Diffusion model**\n\n- [qwen_image_edit_fp8_e4m3fn.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/resolve/main/split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors)\n\n**LoRA**\n\n- [Qwen-Image-Lightning-4steps-V1.0.safetensors](https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-4steps-V1.0.safetensors)\n\n**Text encoder**\n\n- [qwen_2.5_vl_7b_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors)\n\n**VAE**\n\n- [qwen_image_vae.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/vae/qwen_image_vae.safetensors)\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 diffusion_models/\n│ │ └── qwen_image_edit_fp8_e4m3fn.safetensors\n│ ├── 📂 loras/\n│ │ └── Qwen-Image-Lightning-4steps-V1.0.safetensors\n│ ├── 📂 vae/\n│ │ └── qwen_image_vae.safetensors\n│ └── 📂 text_encoders/\n│ └── qwen_2.5_vl_7b_fp8_scaled.safetensors\n```\n"
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+ "[Tutorial](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) | [教程](https://docs.comfy.org/zh-CN/tutorials/image/qwen/qwen-image-edit)\n\n\n## Model links\n\nYou can find all the models on [Comfy-Org/Qwen-Image_ComfyUI](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main) and [Comfy-Org/Qwen-Image-Edit_ComfyUI](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI) \n\n**Diffusion model**\n\n- [qwen_image_edit_2509_fp8_e4m3fn.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/resolve/main/split_files/diffusion_models/qwen_image_edit_2509_fp8_e4m3fn.safetensors)\n\n**LoRA**\n\n- [Qwen-Image-Lightning-4steps-V1.0.safetensors](https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-4steps-V1.0.safetensors)\n\n**Text encoder**\n\n- [qwen_2.5_vl_7b_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors)\n\n**VAE**\n\n- [qwen_image_vae.safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/vae/qwen_image_vae.safetensors)\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 diffusion_models/\n│ │ └── qwen_image_edit_2509_fp8_e4m3fn.safetensors\n│ ├── 📂 loras/\n│ │ └── Qwen-Image-Lightning-4steps-V1.0.safetensors\n│ ├── 📂 vae/\n│ │ └── qwen_image_vae.safetensors\n│ └── 📂 text_encoders/\n│ └── qwen_2.5_vl_7b_fp8_scaled.safetensors\n```\n"
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+ "## Report workflow issue\n\nIf you found any issues when running this workflow, [report template issue here](https://github.com/Comfy-Org/workflow_templates/issues)\n\n\n## Model links\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n**loras**\n\n- [pixel_art_style_z_image_turbo.safetensors](https://huggingface.co/tarn59/pixel_art_style_lora_z_image_turbo/resolve/main/pixel_art_style_z_image_turbo.safetensors)\n\n**diffusion_models**\n\n- [z_image_turbo_bf16.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors)\n\n**vae**\n\n- [ae.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 text_encoders/\n│ │ └── qwen_3_4b.safetensors\n│ ├── 📂 loras/\n│ │ └── pixel_art_style_z_image_turbo.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── z_image_turbo_bf16.safetensors\n│ └── 📂 vae/\n│ └── ae.safetensors\n```\n"
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+ "## Report workflow issue\n\nIf you found any issues when running this workflow, [report template issue here](https://github.com/Comfy-Org/workflow_templates/issues)\n\n\n## Model links\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n**loras**\n\n- [pixel_art_style_z_image_turbo.safetensors](https://huggingface.co/tarn59/pixel_art_style_lora_z_image_turbo/resolve/main/pixel_art_style_z_image_turbo.safetensors)\n\n**diffusion_models**\n\n- [z_image_turbo_bf16.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors)\n\n**vae**\n\n- [ae.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 text_encoders/\n│ │ └── qwen_3_4b.safetensors\n│ ├── 📂 loras/\n│ │ └── pixel_art_style_z_image_turbo.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── z_image_turbo_bf16.safetensors\n│ └── 📂 vae/\n│ └── ae.safetensors\n```\n"
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+ "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"
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diff --git a/LanPaint/pyproject.toml b/LanPaint/pyproject.toml
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index 0000000000000000000000000000000000000000..071d3ba41869059fc34c49daa83569f463575041
--- /dev/null
+++ b/LanPaint/pyproject.toml
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+[build-system]
+requires = ["setuptools>=70.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "LanPaint"
+version = "1.5.0"
+description = "Achieve seamless inpainting results without needing a specialized inpainting model."
+authors = [
+ {name = "LanPaint", email = "czhengac@connect.ust.hk"}
+]
+readme = "README.md"
+license = {text = "GNU General Public License v3"}
+classifiers = []
+dependencies = [
+
+]
+
+[project.optional-dependencies]
+dev = [
+ "bump-my-version",
+ "coverage", # testing
+ "mypy", # linting
+ "pre-commit", # runs linting on commit
+ "pytest", # testing
+ "ruff", # linting
+]
+
+[project.urls]
+bugs = "https://github.com/scraed/LanPaint/issues"
+homepage = "https://github.com/scraed/LanPaint"
+Repository = "https://github.com/scraed/LanPaint"
+
+[tool.comfy]
+PublisherId = "scraed"
+DisplayName = "LanPaint"
+Icon = ""
+
+[tool.setuptools.package-data]
+"*" = ["*.*"]
+
+[tool.pytest.ini_options]
+minversion = "8.0"
+testpaths = [
+ "tests",
+]
+
+[tool.mypy]
+files = "."
+
+# Use strict defaults
+strict = true
+warn_unreachable = true
+warn_no_return = true
+
+[[tool.mypy.overrides]]
+# Don't require test functions to include types
+module = "tests.*"
+allow_untyped_defs = true
+disable_error_code = "attr-defined"
+
+[tool.ruff]
+# extend-exclude = ["static", "ci/templates"]
+line-length = 140
+src = ["src", "tests"]
+target-version = "py39"
+
+# Add rules to ban exec/eval
+[tool.ruff.lint]
+select = [
+ "S102", # exec-builtin
+ "S307", # eval-used
+ "W293",
+ "F", # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
+ # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
+]
+
+[tool.ruff.lint.per-file-ignores]
+"src/LanPaint/nodes.py" = ["F403", "F405"]
+
+[tool.ruff.lint.flake8-quotes]
+inline-quotes = "double"
diff --git a/LanPaint/src/LanPaint.egg-info/PKG-INFO b/LanPaint/src/LanPaint.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..ab9cf677c06d7209168c7ad2979211d030385103
--- /dev/null
+++ b/LanPaint/src/LanPaint.egg-info/PKG-INFO
@@ -0,0 +1,585 @@
+Metadata-Version: 2.4
+Name: LanPaint
+Version: 1.5.0
+Summary: Achieve seamless inpainting results without needing a specialized inpainting model.
+Author-email: LanPaint View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+# LanPaint: Universal Inpainting Sampler with "Think Mode"
+[](https://openreview.net/pdf?id=JPC8JyOUSW)
+[](https://github.com/scraed/LanPaintBench)
+[](https://github.com/comfyanonymous/ComfyUI)
+[](https://huggingface.co/charrywhite/LanPaint)
+[](https://scraed.github.io/scraedBlog/)
+[](https://github.com/scraed/LanPaint/stargazers)
+[](https://discord.gg/yN5wYDE6W4)
+
+
+
+Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.
+
+This is the official implementation of ["LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling"](https://arxiv.org/abs/2502.03491), accepted by TMLR.
+
+The repository is for ComfyUI extension.
+
+Diffusers Support: [LanPaint-Diffusers](https://github.com/charrywhite/LanPaint-diffusers) by [@charrywhite](https://github.com/charrywhite/)
+
+Benchmark code for paper reproduce: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+
+## Citation
+
+```
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
+}
+```
+**🎉 NEW 2026: Join our discord!**
+
+[Join our Discord](https://discord.gg/yN5wYDE6W4) to share experiences, discuss features, and explore future development.
+
+**🎬 NEW: LanPaint now supports inpainting and outpainting based on Z-Image!**
+
+`v1.5.0` fixes an important hidden bug that reduced performance and could blur images (especially with `z-image-base`) and also boosts overall LanPaint performance across other models.
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+|  |  |  |
+
+**🎬 NEW: LanPaint now supports Z-Image-Base too!**
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+|  |  |  |
+
+
+**🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
+
+
+
+| Original Video | Mask (edit T-shirt text) | Inpainted Result |
+|:--------------:|:----:|:----------------:|
+|  |  |  |
+
+*Video Inpainting Example: 81 frames with temporal consistency*
+
+
+
+Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and
+[Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint) support.
+
+
+## Table of Contents
+- [Features](#features)
+- [Quickstart](#quickstart)
+- [How to Use Examples](#how-to-use-examples)
+- [Video Examples (Beta)](#video-examples-beta)
+ - [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
+ - [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
+ - [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
+ - [Resource Consumption](#resource-consumption)
+- [Image Examples](#image-examples)
+ - [Flux.2.Dev](#example-flux2dev-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Flux 2 klein](#example-flux-2-klein-inpaintlanpaint-k-sampler-2-steps-of-thinking)
+ - [Z-image](#example-z-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Z-image-base](#example-z-image-base-inpaintlanpaint-k-sampler-3-steps-of-thinking)
+ - [Hunyuan T2I](#example-hunyuan-t2i-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Wan 2.2 T2I with reference](#example-wan22-partial-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Qwen Image Edit 2511 2509](#example-qwen-edit-2509-inpaint)
+ - [Qwen Image Edit 2508](#example-qwen-edit-2508-inpaint)
+ - [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
+ - [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [Flux](#example-flux-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+ - [SDXL](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
+- [Usage](#usage)
+ - [Basic Sampler](#basic-sampler)
+ - [Advanced Sampler](#lanpaint-ksampler-advanced)
+ - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
+- [Community Showcase](#community-showcase-)
+- [FAQ](#faq)
+- [Updates](#updates)
+- [ToDo](#todo)
+- [Citation](#citation)
+
+## Features
+
+- **Universal Compatibility** – Works instantly with almost any model (**Z-image, Z-image-base, Hunyuan, Wan 2.2, Qwen Image/Edit, HiDream, SD 3.5, Flux-series, SDXL, SD 1.5 or custom LoRAs**) and ControlNet.
+
+- **No Training Needed** – Works out of the box with your existing model.
+- **Easy to Use** – Same workflow as standard ComfyUI KSampler.
+- **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.
+- **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
+- **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters.
+
+**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
+
+## Quickstart
+
+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.
+2. **Install ComfyUI-Manager**: Add the [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) for easy extension management.
+3. **Install LanPaint Nodes**:
+ - **Via ComfyUI-Manager**: Search for "[LanPaint](https://registry.comfy.org/publishers/scraed/nodes/LanPaint)" in the manager and install it directly.
+ - **Manually**: Click "Install via Git URL" in ComfyUI-Manager and input the GitHub repository link:
+ ```
+ https://github.com/scraed/LanPaint.git
+ ```
+ Alternatively, clone this repository into the `ComfyUI/custom_nodes` folder.
+4. **Restart ComfyUI**: Restart ComfyUI to load the LanPaint nodes.
+
+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 |
+|:--------------:|:----:|:----------------:|
+|  |  |  |
+
+[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 |
+|:--------------:|:----:|:-----------------:|
+|  |  |  |
+
+[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
+
+
+| Processing Mode | +Resolution | +Frames Processed | +VRAM Required | +Total Runtime (20 steps) | +
|---|---|---|---|---|
| Inpainting | +880×480 (11:6) | +40 frames | +39.8 GB | +05:37 min | +
| Inpainting | +480×480 (1:1) | +40 frames | +38.0 GB | +05:35 min | +
| Outpainting | +880×480 (11:6) | +40 frames | +40.2 GB | +05:36 min | +
| Inpainting | +880×480 (11:6) | +81 frames | +43.3 GB | +16:23 min | +
| Inpainting | +480×480 (1:1) | +81 frames | +39.8 GB | +14:25 min | +
| Outpainting | +880×480 (11:6) | +81 frames | +42.6 GB | +13:46 min | +
+**Model Used**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors`.
+**Processing Steps**: 20 sampling steps x 2 (LanPaint steps of thinking). + +**Note:** Vram is required by the model, not LanPaint. To further reduce VRAM requirements, we recommend generating less frames and loading CLIP on CPU. + +## Image Examples + +### Example Hunyuan T2I: InPaint(LanPaint K Sampler, 5 steps of thinking) +We are excited to announce that LanPaint now supports inpainting with Hunyuan text to image generation. + +[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_20) + + +You need to follow the ComfyUI version of [Hunyuan workflow](https://docs.comfy.org/tutorials/video/hunyuan-video#hunyuan-text-to-video-workflow) to download and install the model. + +### Example Wan2.2: InPaint(LanPaint K Sampler, 5 steps of thinking) +We are excited to announce that LanPaint now supports Wan2.2 text to image generation with Wan2.2 T2V model. + + +[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_15) + + +You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model. + +### Example Z-image: InPaint(LanPaint K Sampler, 5 steps of thinking) +LanPaint also supports inpainting with the Z-image text-to-image model. + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_21)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Outpaint Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_22)
+
+You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
+
+### Example Z-image-base: InPaint(LanPaint K Sampler, 3 steps of thinking)
+LanPaint also supports inpainting with the Z-image-base model.
+
+**Warning (stability)**: Z-image-base can easily diverge with LanPaint. Start with **small `LanPaint_StepSize`** and **fewer thinking iterations** (lower `LanPaint_NumSteps`) and increase gradually only if stable.
+
+View Z-image Outpainting (Original / Masked / Outpainted)
+ +| Original | Masked | Outpainted | +|:--------:|:------:|:----------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_25)
+
+Workflow template (JSON): [Z_image_base_Inpaint.json](https://github.com/scraed/LanPaint/blob/master/example_workflows/Z_image_base_Inpaint.json)
+
+### 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.
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_16)
+
+
+You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
+
+
+### Example Qwen Edit 2509: InPaint
+Check our latest updated [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14) for Qwen Image Edit 2509. Download the model at [Qwen Image Edit 2509 Comfy](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models). This workflow also supports Qwen Image Edit 2511.
+
+
+
+### Example Qwen Edit 2508: InPaint
+
+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)
+
+
+[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.)
+
+
+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)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
+
+### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
+.jpg)
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model. Thanks [Amazon90](https://github.com/Amazon90) for providing this example.
+
+### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
+
+You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki.com/en/tutorial/advanced/stable-diffusion-3-5-comfyui-workflow) to download and install the model.
+
+### Example Flux.2.Dev: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_23)
+
+[Model Used in This Example](https://huggingface.co/Comfy-Org/flux2-dev)
+
+(Note: Prompt First mode is disabled on Flux.2.Dev. As it does not use CFG guidance.)
+
+### Example Flux 2 klein: InPaint(LanPaint K Sampler, 2 steps of thinking)
+
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
+
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_24)
+
+[Model Used in This Example](https://docs.comfy.org/zh-CN/tutorials/flux/flux-2-klein). If you have quality problem on Comfy 0.11 and 0.12, check [this issue](https://github.com/scraed/LanPaint/issues/80).
+
+
+### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
+[Model Used in This Example](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors)
+(Note: Prompt First mode is disabled on Flux. As it does not use CFG guidance.)
+
+### Example SDXL 0: Character Consistency (Side View Generation) (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_6)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+
+(Tricks 1: You can emphasize the character by copy it's image multiple times with Photoshop. Here I have made one extra copy.)
+
+(Tricks 2: Use prompts like multiple views, multiple angles, clone, turnaround. Use LanPaint's Prompt first mode (does not support Flux))
+
+(Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!)
+
+
+### Example SDXL 1: Basket to Basket Ball (LanPaint K Sampler, 2 steps of thinking).
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_1)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+### Example SDXL 2: White Shirt to Blue Shirt (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_2)
+[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
+### Example SDXL 3: Smile to Sad (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_3)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+### Example SDXL 4: Damage Restoration (LanPaint K Sampler, 5 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_4)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+### Example SDXL 5: Huge Damage Restoration (LanPaint K Sampler, 20 steps of thinking)
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_5)
+[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
+
+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).
+
+
+## Usage
+
+**Workflow Setup**
+Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes. The inpainting workflow is the same as the [SetLatentNoiseMask](https://comfyui-wiki.com/zh/comfyui-nodes/latent/inpaint/set-latent-noise-mask) inpainting workflow.
+
+**Note**
+- LanPaint requires binary masks (values of 0 or 1) without opacity or smoothing. To ensure compatibility, set the mask's **opacity and hardness to maximum** in your mask editor. During inpainting, any mask with smoothing or gradients will automatically be converted to a binary mask.
+- LanPaint relies heavily on your text prompts to guide inpainting - explicitly describe the content you want generated in the masked area. If results show artifacts or mismatched elements, counteract them with targeted negative prompts.
+
+## Basic Sampler
+
+
+- LanPaint KSampler: The most basic and easy to use sampler for inpainting.
+- LanPaint KSampler (Advanced): Full control of all parameters.
+
+### LanPaint KSampler
+Simplified interface with recommended defaults:
+
+- Steps: 20 - 50. More steps will give more "thinking" and better results.
+- LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks ( which means 5 times slower than sampling without thinking). Use 10 for more challenging tasks.
+- LanPaint Prompt mode: Image First mode and Prompt First mode. Image First mode focuses on the image, inpaint based on image context (maybe ignore prompt), while Prompt First mode focuses more on the prompt. Use Prompt First mode for tasks like character consistency. (Technically, it Prompt First mode change CFG scale to negative value in the BIG score to emphasis prompt, which will costs image quality.)
+
+### LanPaint KSampler (Advanced)
+Full parameter control:
+**Key Parameters**
+
+| Parameter | Range | Description |
+|-----------|-------|-------------|
+| `Steps` | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 20-50. |
+| `LanPaint_NumSteps` | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 2-5. Hard task: 5-10 |
+| `LanPaint_Lambda` | 0.1-50 | Content alignment strength (higher = stricter). Recommend 4.0 - 10.0 |
+| `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. |
+| `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 |
+| `LanPaint_Friction` | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0|
+| `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5|
+| `LanPaint_PromptMode` | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. |
+
+For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.
+
+### LanPaint Mask Blend
+This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly.
+
+## LanPaint KSampler (Advanced) Tuning Guide
+For challenging inpainting tasks:
+
+1️⃣ **Boost Quality**
+Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
+
+2️⃣ **Boost Speed**
+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.
+
+3️⃣ **Fix Unstability**:
+If you find the results have wired texture, try
+- Reduce **LanPaint_Friction** to make the Langevin dynamics more stable.
+- Reduce **LanPaint_StepSize** to use smaller step size.
+- Reduce **LanPaint_Beta** if you are using a high lambda value.
+
+⚠️ **Notes**:
+- For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting. Better to do it with a batche of images to avoid overfitting on a single image.
+
+## Community Showcase [](#community-showcase-)
+
+Discover how the community is using LanPaint! Here are some user-created tutorials:
+
+- [Ai绘画进阶148-三大王炸！庆祝高允贞出道6周年！T8即将直播？当AI绘画学会深度思考？！万能修复神器LanPaint，万物皆可修！-T8 Comfyui教程](https://www.youtube.com/watch?v=Z4DSTv3UPJo)
+- [Ai绘画进阶151-真相了！T8竟是个AI？！LanPaint进阶（二），人物一致性，多视角实验性测试，新参数讲解，工作流分享-T8 Comfyui教程](https://www.youtube.com/watch?v=landiRhvF3k)
+- [重绘和三视图角色一致性解决新方案！LanPaint节点尝试](https://www.youtube.com/watch?v=X0WbXdm6FA0)
+- [ComfyUI: HiDream with Perturbation Upscale, LanPaint Inpainting (Workflow Tutorial)](https://www.youtube.com/watch?v=2-mGe4QVIIw&t=2785s)
+- [ComfyUI必备LanPaint插件超详细使用教程](https://plugin.aix.ink/archives/lanpaint)
+
+Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
+
+## FAQ
+[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
+
+## Updates
+- 2026/03/02
+ - `v1.5.0`: Fixed a hidden bug that hurt performance and caused image blur (especially on `z-image-base`), and improved overall LanPaint performance on other models too.
+- 2026/01/30
+ - Add Z-image-base documentation and Example_25 workflow images.
+- 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.~~ Check our local Python benchmark code [LanPaintBench](https://github.com/scraed/LanPaintBench).
+
+
+## Citation
+
+```
+@article{
+zheng2025lanpaint,
+title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
+author={Candi Zheng and Yuan Lan and Yang Wang},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=JPC8JyOUSW},
+note={}
+}
+```
+
+
+
+
+
diff --git a/LanPaint/src/LanPaint.egg-info/SOURCES.txt b/LanPaint/src/LanPaint.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e8e2e4493578f3f9a36c237868d38b826a945b0
--- /dev/null
+++ b/LanPaint/src/LanPaint.egg-info/SOURCES.txt
@@ -0,0 +1,19 @@
+LICENSE
+MANIFEST.in
+README.md
+pyproject.toml
+src/LanPaint/__init__.py
+src/LanPaint/earlystop.py
+src/LanPaint/lanpaint.py
+src/LanPaint/nodes.py
+src/LanPaint/types.py
+src/LanPaint/utils.py
+src/LanPaint.egg-info/PKG-INFO
+src/LanPaint.egg-info/SOURCES.txt
+src/LanPaint.egg-info/dependency_links.txt
+src/LanPaint.egg-info/requires.txt
+src/LanPaint.egg-info/top_level.txt
+tests/test_LanPaint.py
+tests/test_lanpaint_semantic_stop.py
+tests/test_reshape_mask.py
+tests/test_sho_regression.py
\ No newline at end of file
diff --git a/LanPaint/src/LanPaint.egg-info/dependency_links.txt b/LanPaint/src/LanPaint.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/LanPaint/src/LanPaint.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/LanPaint/src/LanPaint.egg-info/requires.txt b/LanPaint/src/LanPaint.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e2cf126e59e1387d6c898f5d834cc9a4db891f08
--- /dev/null
+++ b/LanPaint/src/LanPaint.egg-info/requires.txt
@@ -0,0 +1,8 @@
+
+[dev]
+bump-my-version
+coverage
+mypy
+pre-commit
+pytest
+ruff
diff --git a/LanPaint/src/LanPaint.egg-info/top_level.txt b/LanPaint/src/LanPaint.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0593c4b03a6b33715a94b32729cbb1a3abc2a774
--- /dev/null
+++ b/LanPaint/src/LanPaint.egg-info/top_level.txt
@@ -0,0 +1 @@
+LanPaint
diff --git a/LanPaint/src/LanPaint/__init__.py b/LanPaint/src/LanPaint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/LanPaint/src/LanPaint/__pycache__/__init__.cpython-312.pyc b/LanPaint/src/LanPaint/__pycache__/__init__.cpython-312.pyc
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diff --git a/LanPaint/src/LanPaint/earlystop.py b/LanPaint/src/LanPaint/earlystop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1dc244d5b865d60686ce481467af6f471b2226c
--- /dev/null
+++ b/LanPaint/src/LanPaint/earlystop.py
@@ -0,0 +1,337 @@
+"""
+Early Stop Logic Contributed by `https://github.com/godnight10061`.
+"""
+
+import inspect
+from typing import Any, Callable, Optional
+
+import torch
+
+from .types import LangevinState
+
+
+def _clamp01(val: float) -> float:
+ if val <= 0.0:
+ return 0.0
+ if val >= 1.0:
+ return 1.0
+ return val
+
+
+def _abt_scale(abt_val: float) -> float:
+ """
+ Smooth, parameter-free scale based on outer-step noise level.
+
+ - 0 at abt=0/1 (disable at extreme noise / extreme tail)
+ - 1 at abt=0.5 (mid-schedule)
+ """
+ abt_val = _clamp01(abt_val)
+ return _clamp01(4.0 * abt_val * (1.0 - abt_val))
+
+
+def _boundary_weight(latent_mask: torch.Tensor, inpaint_weight: torch.Tensor) -> Optional[torch.Tensor]:
+ """
+ Return a 4-neighbor boundary weight: unknown pixels adjacent to known pixels.
+
+ This replaces the previous dilation-based "ring" (kernel/padding) and has no tunable hyperparameters.
+ """
+ if latent_mask.dim() != 4:
+ return None
+
+ known = latent_mask > 0.5
+ neighbor_known = torch.zeros_like(known)
+ neighbor_known[:, :, 1:, :] |= known[:, :, :-1, :]
+ neighbor_known[:, :, :-1, :] |= known[:, :, 1:, :]
+ neighbor_known[:, :, :, 1:] |= known[:, :, :, :-1]
+ neighbor_known[:, :, :, :-1] |= known[:, :, :, 1:]
+
+ boundary = (~known) & neighbor_known
+ return boundary.to(dtype=torch.float32) * inpaint_weight
+
+
+def _weighted_mse(t1: torch.Tensor, t2: torch.Tensor, weight: torch.Tensor) -> float:
+ diff_sq = (t1.to(dtype=torch.float32) - t2.to(dtype=torch.float32)) ** 2
+ denom = torch.sum(weight) + 1e-12
+ return float((torch.sum(diff_sq * weight) / denom).item())
+
+
+class LanPaintEarlyStopper:
+ """
+ Per-step early-stop logic for LanPaint inner (Langevin) iterations.
+ """
+
+ @classmethod
+ def from_options(
+ cls,
+ *,
+ model_options: Optional[dict],
+ latent_mask: torch.Tensor,
+ abt: torch.Tensor,
+ default_threshold: float,
+ default_patience: int,
+ default_distance_fn: Optional[Callable[..., Any]],
+ ) -> Optional["LanPaintEarlyStopper"]:
+ semantic_stop = model_options.get("lanpaint_semantic_stop") if isinstance(model_options, dict) else None
+
+ threshold = float(default_threshold)
+ patience = int(default_patience)
+ distance_fn = default_distance_fn
+ # distance_fn contract: return None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor)
+
+ if isinstance(semantic_stop, dict):
+ threshold = float(semantic_stop.get("threshold", threshold))
+ patience = int(semantic_stop.get("patience", patience))
+ distance_fn = semantic_stop.get("distance_fn", distance_fn)
+
+ # Backward compatibility: map legacy 'min_steps' to a patience floor so it is not an independent knob.
+ if patience > 0:
+ min_steps = semantic_stop.get("min_steps")
+ if min_steps is not None:
+ try:
+ min_steps_int = int(min_steps)
+ except (TypeError, ValueError):
+ min_steps_int = 0
+ if min_steps_int > 1:
+ patience = max(patience, min_steps_int - 1)
+
+ enabled_early_stop = (threshold > 0.0) and (patience > 0)
+ # Require N+1 consecutive stable checks:
+ # - the first stable step sets patience_counter to 1
+ # - `patience=1` therefore stops after 2 stable steps
+ patience_eff = max(1, patience) + 1
+ threshold_eff = threshold
+ inpaint_weight = ring_weight = trace = abt_val = None
+
+ if enabled_early_stop:
+ try:
+ abt_val = float(torch.mean(abt).item())
+ except (TypeError, ValueError):
+ abt_val = 0.0
+
+ threshold_eff = threshold * _abt_scale(abt_val)
+ if threshold_eff <= 0.0:
+ enabled_early_stop = False
+ else:
+ inpaint_weight = (1 - latent_mask).to(dtype=torch.float32)
+ if float(torch.sum(inpaint_weight).item()) < 1e-6:
+ enabled_early_stop = False
+ else:
+ ring_weight = _boundary_weight(latent_mask, inpaint_weight)
+ if isinstance(model_options, dict):
+ trace = model_options.get("lanpaint_semantic_trace")
+
+ if not enabled_early_stop:
+ return None
+
+ # Pre-fetch trace keys to avoid repeated dict lookups
+ bench_case_id = bench_outer_step = bench_timestep = None
+ if isinstance(trace, list) and isinstance(model_options, dict):
+ bench_case_id = model_options.get("bench_case_id")
+ bench_outer_step = model_options.get("bench_outer_step")
+ bench_timestep = model_options.get("bench_timestep")
+
+ return cls(
+ enabled=enabled_early_stop,
+ threshold=threshold,
+ threshold_eff=threshold_eff,
+ patience_eff=patience_eff,
+ inpaint_weight=inpaint_weight,
+ ring_weight=ring_weight,
+ distance_fn=distance_fn,
+ trace=trace,
+ bench_case_id=bench_case_id,
+ bench_outer_step=bench_outer_step,
+ bench_timestep=bench_timestep,
+ abt_val=abt_val,
+ )
+
+ def __init__(
+ self,
+ *,
+ enabled: bool,
+ threshold: float,
+ threshold_eff: float,
+ patience_eff: int,
+ inpaint_weight: Optional[torch.Tensor],
+ ring_weight: Optional[torch.Tensor],
+ distance_fn: Optional[Callable[..., Any]] = None,
+ trace: Optional[list] = None,
+ bench_case_id: Any = None,
+ bench_outer_step: Any = None,
+ bench_timestep: Any = None,
+ abt_val: Optional[float] = None,
+ ) -> None:
+ self.enabled = bool(enabled)
+ self.threshold = float(threshold)
+ self.threshold_eff = float(threshold_eff)
+ self.patience_eff = int(patience_eff)
+
+ self.inpaint_weight = inpaint_weight
+ self.ring_weight = ring_weight
+
+ self.trace = trace
+ self.bench_case_id = bench_case_id
+ self.bench_outer_step = bench_outer_step
+ self.bench_timestep = bench_timestep
+ self.abt_val = abt_val
+
+ self.patience_counter = 0
+ self.x0_anchor = None
+
+ self._dist_wrapper = self._wrap_distance_fn(distance_fn) if self.enabled else None
+
+ @property
+ def has_custom_distance_fn(self) -> bool:
+ return self._dist_wrapper is not None
+
+ @staticmethod
+ def _wrap_distance_fn(distance_fn: Optional[Callable[..., Any]]):
+ """
+ Wrap a user-provided `distance_fn` into a normalized callable: fn(prev, cur, ctx) -> dist|None.
+
+ Supported signatures:
+ - 3+ positional (or *args): `distance_fn(prev, cur, ctx)`
+ - explicit / **kwargs ctx: `distance_fn(prev, cur, ctx=ctx)`
+ - default 2-arg: `distance_fn(cur, prev)`
+
+ Return contract: None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor).
+ """
+ if not callable(distance_fn):
+ return None
+
+ try:
+ sig = inspect.signature(distance_fn)
+ params = list(sig.parameters.values())
+
+ has_ctx_param = "ctx" in sig.parameters
+ has_var_kw = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params)
+ has_var_pos = any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params)
+
+ pos_params = [
+ p
+ for p in params
+ if p.kind in (inspect.Parameter.POSITIONAL_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD)
+ ]
+
+ if len(pos_params) >= 3 or has_var_pos:
+ # 3-arg positional: fn(prev, cur, ctx)
+ return lambda p, c, ctx: distance_fn(p, c, ctx)
+ if has_ctx_param or has_var_kw:
+ # keyword ctx: fn(prev, cur, ctx=ctx)
+ return lambda p, c, ctx: distance_fn(p, c, ctx=ctx)
+
+ # Default 2-arg: fn(cur, prev)
+ return lambda p, c, ctx: distance_fn(c, p)
+ except (ValueError, TypeError):
+ # Fallback for built-ins or complex callables.
+ def fallback_wrapper(p, c, ctx):
+ try:
+ return distance_fn(p, c, ctx)
+ except TypeError as e:
+ tb = e.__traceback__
+ if tb is not None and tb.tb_frame.f_code is not fallback_wrapper.__code__:
+ raise
+ return distance_fn(c, p)
+
+ return fallback_wrapper
+
+ def step(
+ self,
+ *,
+ i: int,
+ n_steps: int,
+ x_t_before: torch.Tensor,
+ x_t_after: torch.Tensor,
+ x_t_prev_for_custom: Optional[torch.Tensor],
+ prev_args: Any,
+ args: Any,
+ ctx: dict,
+ ) -> bool:
+ if not self.enabled:
+ return False
+
+ # 'inpaint_weight' is guaranteed to be set when enabled is True in the caller.
+ inpaint = self.inpaint_weight
+ if inpaint is None:
+ return False
+
+ dist = None
+ custom_dist = False
+ dist_inpaint = dist_ring = dist_drift = x0_prev = x0_cur = None
+
+ if self._dist_wrapper is not None:
+ dist = self._dist_wrapper(x_t_prev_for_custom, x_t_after, ctx)
+ if dist is not None:
+ if isinstance(dist, torch.Tensor):
+ if dist.numel() != 1:
+ raise TypeError("distance_fn must return None or a scalar / 0-d (1-element) tensor")
+ dist = float(dist.item())
+ else:
+ dist = float(dist)
+ custom_dist = dist is not None
+
+ if dist is None:
+ def _get_x0(arg: Any) -> Optional[torch.Tensor]:
+ if isinstance(arg, LangevinState):
+ return arg.x0
+ if isinstance(arg, tuple) and len(arg) >= 3:
+ return arg[2]
+ return None
+
+ x0_prev = _get_x0(prev_args)
+ x0_cur = _get_x0(args)
+
+ if x0_prev is not None and x0_cur is not None:
+ dist_inpaint = _weighted_mse(x0_cur, x0_prev, inpaint)
+ dist_ring = _weighted_mse(x0_cur, x0_prev, self.ring_weight) if self.ring_weight is not None else None
+ dist = dist_inpaint if dist_ring is None else max(dist_inpaint, dist_ring)
+ else:
+ dist_inpaint = _weighted_mse(x_t_after, x_t_before, inpaint)
+ dist = dist_inpaint
+
+ threshold_used = self.threshold if custom_dist else self.threshold_eff
+
+ # Drift guard (only for default metric with x0_cur).
+ if x0_cur is not None and not custom_dist:
+ if dist <= threshold_used:
+ if self.x0_anchor is None:
+ self.x0_anchor = x0_cur.detach()
+ else:
+ drift_inpaint = _weighted_mse(x0_cur, self.x0_anchor, inpaint)
+ drift_ring = _weighted_mse(x0_cur, self.x0_anchor, self.ring_weight) if self.ring_weight is not None else None
+ dist_drift = drift_inpaint if drift_ring is None else max(drift_inpaint, drift_ring)
+ dist = max(dist, dist_drift)
+ else:
+ self.x0_anchor = None
+
+ if dist <= threshold_used:
+ self.patience_counter += 1
+ else:
+ self.patience_counter = 0
+ self.x0_anchor = None
+
+ should_stop = self.patience_counter >= self.patience_eff
+
+ if isinstance(self.trace, list):
+ self.trace.append(
+ {
+ "case_id": self.bench_case_id,
+ "outer_step": self.bench_outer_step,
+ "bench_timestep": self.bench_timestep,
+ "inner_step": i + 1,
+ "dist": dist,
+ "dist_inpaint": None if dist_inpaint is None else float(dist_inpaint),
+ "dist_ring": None if dist_ring is None else float(dist_ring),
+ "dist_drift": None if dist_drift is None else float(dist_drift),
+ "threshold": float(threshold_used),
+ "threshold_eff": float(self.threshold_eff),
+ "patience_counter": int(self.patience_counter),
+ "patience_eff": int(self.patience_eff),
+ "abt": None if self.abt_val is None else float(self.abt_val),
+ "custom_dist": bool(custom_dist),
+ "stopped": bool(should_stop),
+ }
+ )
+
+ return bool(should_stop)
+
diff --git a/LanPaint/src/LanPaint/lanpaint.py b/LanPaint/src/LanPaint/lanpaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..70b38d54abda0771d0f4c13a645b55f28fefe126
--- /dev/null
+++ b/LanPaint/src/LanPaint/lanpaint.py
@@ -0,0 +1,272 @@
+import torch
+from .utils import StochasticHarmonicOscillator
+from functools import partial
+from .earlystop import LanPaintEarlyStopper
+from .types import LangevinState
+
+class LanPaint():
+ def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False, EarlyStopThreshold = 0.0, EarlyStopPatience = 1, EarlyStopHook = None):
+ self.n_steps = NSteps
+ self.chara_lamb = Lambda
+ self.IS_FLUX = IS_FLUX
+ self.IS_FLOW = IS_FLOW
+ self.step_size = StepSize
+ self.inner_model = Model
+ self.friction = Friction
+ self.chara_beta = Beta
+ self.img_dim_size = None
+ self.early_stop_threshold = EarlyStopThreshold
+ self.early_stop_patience = EarlyStopPatience
+ self.early_stop_hook = EarlyStopHook
+
+ def add_none_dims(self, array):
+ # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+ index = (slice(None),) + (None,) * (self.img_dim_size-1)
+ return array[index]
+ def remove_none_dims(self, array):
+ # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+ index = (slice(None),) + (0,) * (self.img_dim_size-1)
+ return array[index]
+ def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None):
+ self.img_dim_size = len(x.shape)
+ self.latent_image = latent_image
+ self.noise = noise
+ if torch.mean(torch.abs(self.noise)) < 1e-8:
+ self.noise = torch.randn_like(self.noise)
+ if n_steps is None:
+ n_steps = self.n_steps
+ return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
+ def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW):
+ input_x = x
+ VE_Sigma, abt, Flow_t = current_times
+
+ step_size = self.step_size * (1 - abt)
+ step_size = self.add_none_dims(step_size)
+ # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
+ # This is the replace step
+ 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:
+ x_t = x / ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
+
+ ############ LanPaint Iterations Start ###############
+ # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
+ args = None
+ stopper = LanPaintEarlyStopper.from_options(
+ model_options=model_options if isinstance(model_options, dict) else None,
+ latent_mask=latent_mask,
+ abt=abt,
+ default_threshold=self.early_stop_threshold,
+ default_patience=self.early_stop_patience,
+ default_distance_fn=self.early_stop_hook,
+ )
+
+ for i in range(n_steps):
+ score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = self.add_none_dims(abt), sigma = self.add_none_dims(VE_Sigma), tflow = self.add_none_dims(Flow_t), model_options = model_options, seed = seed )
+
+ prev_args = args
+ x_t_prev = x_t.detach() if (stopper is not None and stopper.has_custom_distance_fn) else None
+ x_t_before = x_t if (stopper is not None and stopper.enabled) else None
+
+ x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)
+
+ if stopper is not None:
+ ctx = {
+ "step": i,
+ "steps_done": i + 1,
+ "n_steps": n_steps,
+ "mask": latent_mask,
+ "latent_image": self.latent_image,
+ "current_times": current_times,
+ "seed": seed,
+ }
+ if stopper.step(
+ i=i,
+ n_steps=n_steps,
+ x_t_before=x_t_before,
+ x_t_after=x_t,
+ x_t_prev_for_custom=x_t_prev,
+ prev_args=prev_args,
+ args=args,
+ ctx=ctx,
+ ):
+ break
+
+ if IS_FLUX or IS_FLOW:
+ x = x_t / ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
+ else:
+ x = x_t * ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
+ ############ LanPaint Iterations End ###############
+ # out is x_0
+
+ out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+ out = out * (1-latent_mask) + self.latent_image * latent_mask
+
+ input_x.copy_(x)
+ return out
+
+ def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):
+ lamb = self.chara_lamb
+ if self.IS_FLUX or self.IS_FLOW:
+ # compute t for flow model, with a small epsilon compensating for numerical error.
+ x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching
+ x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
+ else:
+ x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding
+ x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)
+
+ score_x = -(x_t - x_0)
+ score_y = - (1 + lamb) * ( x_t - y ) + lamb * (x_t - x_0_BIG)
+ return score_x * (1 - mask) + score_y * mask
+ def sigma_x(self, abt):
+ # the time scale for the x_t update
+ return abt**0
+ def sigma_y(self, abt):
+ beta = self.chara_beta * abt ** 0
+ return beta
+
+ def langevin_dynamics(self, x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None):
+ if args is not None and not isinstance(args, LangevinState):
+ if isinstance(args, tuple):
+ if len(args) == 2:
+ # Backwards compat: older state was (v, C) without x0.
+ args = LangevinState(args[0], args[1], None)
+ elif len(args) >= 3:
+ args = LangevinState(args[0], args[1], args[2])
+ # prepare the step size and time parameters
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ step_sizes = self.prepare_step_size(current_times, step_size, sigma_x, sigma_y)
+ sigma, abt, dtx, dty, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y = step_sizes
+ # print('mask',mask.device)
+ if torch.mean(dtx) <= 0.:
+ return x_t, args
+ # -------------------------------------------------------------------------
+ # 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)
+ A = A_x * (1-mask) + A_y * mask
+ D = D_x * (1-mask) + D_y * mask
+ dt = dtx * (1-mask) + dty * mask
+ Gamma = Gamma_x * (1-mask) + Gamma_y * mask
+
+ def Coef_C(x_t):
+ x0 = x_t + score(x_t)
+ C = (abt**0.5 * x0 - x_t )/ (1-abt) + A * x_t
+ return C, x0
+ def advance_time(x_t, v, dt, Gamma, A, C, D):
+ dtype = x_t.dtype
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ osc = StochasticHarmonicOscillator(Gamma, A, C, D )
+ x_t, v = osc.dynamics(x_t, v, dt )
+ x_t = x_t.to(dtype)
+ v = v.to(dtype)
+ return x_t, v
+
+ def advance_time_overdamped(x_t, dt, A, C, D):
+ """
+ Overdamped (Gamma -> infinity) limit:
+ dx = -A x dt + C dt + D dW_t
+ with C treated as constant over this substep.
+ """
+ dtype = x_t.dtype
+ with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+ A_dt = A * dt
+ exp_neg = torch.exp(-A_dt)
+
+ eps = 1e-8
+ abs_A = torch.abs(A)
+ # k = (1 - exp(-A dt)) / A -> dt when A -> 0
+ k = torch.where(abs_A < eps, dt, (-torch.expm1(-A_dt)) / A)
+ # k2 = (1 - exp(-2 A dt)) / (2 A) -> dt when A -> 0
+ k2 = torch.where(abs_A < eps, dt, (-torch.expm1(-2 * A_dt)) / (2 * A))
+
+ mean = exp_neg * x_t + k * C
+ var = (D ** 2) * k2
+ noise = torch.randn_like(x_t) * torch.sqrt(torch.clamp(var, min=0.0))
+ x_t = mean + noise
+ return x_t.to(dtype)
+
+ def run_damped(x_t, args):
+ if args is None:
+ v = None
+ C, x0 = Coef_C(x_t)
+ x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
+ else:
+ v = args.v
+ C = args.C
+ x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+ C_new, x0 = Coef_C(x_t)
+ v = v + Gamma**0.5 * ( C_new - C) *dt
+ x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+ C = C_new
+ # args is (v, C, x0) for the next inner step.
+ return x_t, LangevinState(v, C, x0)
+
+ def run_overdamped(x_t, args):
+ if args is None:
+ C, x0 = Coef_C(x_t)
+ x_t = advance_time_overdamped(x_t, dt, A, C, D)
+ else:
+ C = args.C
+ x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+ C_new, x0 = Coef_C(x_t)
+ x_t = x_t + (C_new - C) * dt
+ x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
+ C = C_new
+ # args is (v, C, x0); v is None in the overdamped fallback.
+ return x_t, LangevinState(None, C, x0)
+
+ try:
+ x_t_next, state = run_damped(x_t, args)
+
+ v_next = state.v
+ if torch.isnan(x_t_next).any() or (v_next is not None and torch.isnan(v_next).any()):
+ raise ValueError("NaN detected")
+
+ x_t = x_t_next
+ except Exception:
+ x_t, state = run_overdamped(x_t, args)
+
+ # args is (v, C, x0); v can be None if we fell back to the overdamped update.
+ return x_t, state
+
+ def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
+ # -------------------------------------------------------------------------
+ # Unpack current times parameters (sigma and abt)
+ sigma, abt, flow_t = current_times
+ sigma = self.add_none_dims(sigma)
+ abt = self.add_none_dims(abt)
+ # Compute time step (dtx, dty) for x and y branches.
+ dtx = 2 * step_size * sigma_x
+ dty = 2 * step_size * sigma_y
+
+ # -------------------------------------------------------------------------
+ # Define friction parameter Gamma_hat for each branch.
+ # Using dtx**0 provides a tensor of the proper device/dtype.
+
+ Gamma_hat_x = self.friction **2 * self.step_size * sigma_x / 0.1 * sigma**0
+ Gamma_hat_y = self.friction **2 * self.step_size * sigma_y / 0.1 * sigma**0
+ #print("Gamma_hat_x", torch.mean(Gamma_hat_x).item(), "Gamma_hat_y", torch.mean(Gamma_hat_y).item())
+ # adjust dt to match denoise-addnoise steps sizes
+ Gamma_hat_x /= 2.
+ Gamma_hat_y /= 2.
+ A_t_x = (1) / ( 1 - abt ) * dtx / 2
+ A_t_y = (1+self.chara_lamb) / ( 1 - abt ) * dty / 2
+
+
+ A_x = A_t_x / (dtx/2)
+ A_y = A_t_y / (dty/2)
+ Gamma_x = Gamma_hat_x / (dtx/2)
+ Gamma_y = Gamma_hat_y / (dty/2)
+
+ #D_x = (2 * (1 + sigma**2) )**0.5
+ #D_y = (2 * (1 + sigma**2) )**0.5
+ D_x = (2 * abt**0 )**0.5
+ D_y = (2 * abt**0 )**0.5
+ return sigma, abt, dtx/2, dty/2, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y
diff --git a/LanPaint/src/LanPaint/nodes.py b/LanPaint/src/LanPaint/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c7064605b6540ebb8f2b5c5f43d4aa3a9048bd3
--- /dev/null
+++ b/LanPaint/src/LanPaint/nodes.py
@@ -0,0 +1,648 @@
+from contextlib import contextmanager
+import math
+# import nodes.py
+import comfy
+import nodes
+import latent_preview
+import torch
+from comfy.utils import repeat_to_batch_size
+from comfy.samplers import *
+from comfy.model_base import ModelType
+from .lanpaint import LanPaint
+from comfy.model_base import WAN22
+import comfyui_version
+
+def _version_tuple(value):
+ return tuple(int(part) if part.isdigit() else 0 for part in value.split("."))
+
+COMFYUI_VERSION_060_OR_NEWER = _version_tuple(comfyui_version.__version__) >= (0, 6, 0)
+
+def reshape_mask(input_mask, output_shape,video_inpainting=False):
+ dims = len(output_shape) - 2
+ print('output shape',output_shape)
+ scale_mode = "nearest-exact"
+ print('input mask',input_mask.shape,type(input_mask),torch.max(input_mask),torch.min(input_mask))
+ print('target output_shape',output_shape)
+ print('input_mask.ndim:', input_mask.ndim, 'output_shape len:', len(output_shape))
+
+ # Handle input mask dimensions
+ if input_mask.ndim == 2:
+ input_mask = input_mask.unsqueeze(0).unsqueeze(0)
+ elif input_mask.ndim == 3:
+ input_mask = input_mask.unsqueeze(1)
+
+ # Handle 5D output shape (B, C, F, H, W) by ensuring input is 5D
+ if len(output_shape) == 5 and input_mask.ndim == 4:
+ if COMFYUI_VERSION_060_OR_NEWER:
+ input_mask = input_mask.unsqueeze(2) # (B, C, 1, H, W)
+
+ # Handle video case with temporal dimension
+ if video_inpainting: # Video case: (batch, channels, frames, height, width)
+ target_frames = output_shape[2]
+ target_height, target_width = output_shape[-2:]
+
+ print('Video case - input_mask initial shape:', input_mask.shape)
+
+ # First reshape input_mask to have proper dimensions for video processing
+ # Assume input is (frames, channels, height, width) -> (1, channels, frames, height, width)
+ ## if comfy version < 0.6.0
+ if not COMFYUI_VERSION_060_OR_NEWER:
+ input_mask = input_mask.permute(1, 0, 2, 3).unsqueeze(0)
+ print('Video case - input_mask after reshaping:', input_mask.shape)
+ # Ensure we have the correct 5D shape: (batch, channels, frames, height, width)
+ batch_size, channels, frames, height, width = input_mask.shape
+ print('Video case - dimensions: batch_size={}, channels={}, frames={}, height={}, width={}'.format(batch_size, channels, frames, height, width))
+ print('Video case - target size:', (target_frames, target_height, target_width))
+
+ # 3D nearest-exact interpolation: (batch, channels, frames, height, width) -> (batch, channels, target_frames, target_height, target_width)
+ temp_mask = torch.nn.functional.interpolate(
+ input_mask,
+ size=(target_frames, target_height, target_width),
+ mode=scale_mode,
+ )
+
+ # temp_mask is already 5D: (batch, channels, target_frames, target_height, target_width)
+ mask = temp_mask
+ print('after mask',mask.shape)
+ # Handle channel dimension expansion if needed
+ if mask.shape[1] < output_shape[1]:
+ mask = mask.repeat(1, output_shape[1], 1, 1, 1)[:, :output_shape[1]]
+ # Handle batch dimension
+ mask = repeat_to_batch_size(mask, output_shape[0])
+ else: # Original 2D image case
+ if not COMFYUI_VERSION_060_OR_NEWER:
+ mask = torch.nn.functional.interpolate(input_mask, size=output_shape[-2:], mode=scale_mode)
+ else:
+ mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+ if mask.shape[1] < output_shape[1]:
+ mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+ mask = repeat_to_batch_size(mask, output_shape[0])
+
+
+ return mask
+def prepare_mask(noise_mask, shape, device,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
+ else:
+ uncond_ = uncond
+
+ conds = [cond, uncond_]
+ out = calc_cond_batch(model, conds, x, timestep, model_options)
+
+ for fn in model_options.get("sampler_pre_cfg_function", []):
+ args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
+ "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+ out = fn(args)
+
+ return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_), cfg_function(model, out[0], out[1], cond_scale_BIG, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
+
+
+class CFGGuider_LanPaint:
+ 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:
+ 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)
+ sigmas = sigmas.to(device)
+ cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
+
+ try:
+ self.model_patcher.pre_run()
+ output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs)
+ finally:
+ self.model_patcher.cleanup()
+
+ comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+ del self.inner_model
+ del self.loaded_models
+ return output
+ def predict_noise(self, x, timestep, model_options={}, seed=None):
+ return sampling_function_LanPaint(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, self.cfg_BIG, model_options=model_options, seed=seed)
+
+#CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample
+#CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise
+
+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 )
+ 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
+ # sigma is the noise level
+ ### For flux model
+ # x is rectified flow x_t = sigma * noise + (1.0 - sigma) * x_0
+
+ IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX
+ IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW
+ #print("model class", type(self.inner_model.inner_model))
+ #print("model type", self.inner_model.inner_model.model_type, "IS_FLUX", IS_FLUX, "IS_FLOW", IS_FLOW)
+ #print("sigma", torch.mean(sigma).item(), torch.min(sigma).item(), torch.max(sigma).item())
+ # unify the notations into variance exploding diffusion model
+ if IS_FLUX or IS_FLOW:
+ Flow_t = sigma
+ abt = (1 - Flow_t)**2 / ((1 - Flow_t)**2 + Flow_t**2 )
+ VE_Sigma = Flow_t / (1 - Flow_t)
+ #print("t", torch.mean( sigma ).item(), "VE_Sigma", torch.mean( VE_Sigma ).item())
+
+
+ else:
+ VE_Sigma = sigma
+ abt = 1/( 1+VE_Sigma**2 )
+ Flow_t = (1-abt)**0.5 / ( (1-abt)**0.5 + abt**0.5 )
+
+ if denoise_mask is not None:
+ if "denoise_mask_function" in model_options:
+ denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
+
+ denoise_mask = (denoise_mask > 0.5).float()
+
+ latent_mask = 1 - denoise_mask
+ current_times = (VE_Sigma, abt, Flow_t)
+
+ current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) )
+ total_steps = len(self.sigmas)-1
+
+ if total_steps - current_step <= self.LanPaint_early_stop:
+ out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed, n_steps=0)
+ else:
+ out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed)
+ else:
+ out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+
+ # Add TAESD preview support - directly use the latent_preview module
+ current_step = model_options.get("i", kwargs.get("i", 0))
+ total_steps = model_options.get("total_steps", 0)
+
+ # Only show preview every few steps to improve performance
+ if current_step % 2 == 0:
+ # Directly call the preview callback if it exists
+ callback = model_options.get("callback", None)
+ if callback is not None:
+ callback({"i": current_step, "denoised": out, "x": x})
+
+ return out
+
+# Custom sampler class extending ComfyUI's KSAMPLER for LanPaint
+class KSAMPLER(comfy.samplers.KSAMPLER):
+ def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+ #noise here is a randn noise from comfy.sample.prepare_noise
+ #latent_image is the latent image as input of the KSampler node. For inpainting, it is the masked latent image. Otherwise it is zero tensor.
+ extra_args["denoise_mask"] = denoise_mask
+ model_k = KSamplerX0Inpaint(model_wrap, sigmas)
+ model_k.latent_image = latent_image
+ if self.inpaint_options.get("random", False): #TODO: Should this be the default?
+ generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
+ model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
+ else:
+ model_k.noise = noise
+
+ IS_FLUX = model_wrap.inner_model.model_type == ModelType.FLUX
+ IS_FLOW = model_wrap.inner_model.model_type == ModelType.FLOW
+ # unify the notations into variance exploding diffusion model
+ if IS_FLUX:
+ model_wrap.cfg_BIG = 1.0
+ else:
+ model_wrap.cfg_BIG = model_wrap.model_patcher.LanPaint_cfg_BIG
+ noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
+
+ model_k.PaintMethod = LanPaint(model_k.inner_model,
+ model_wrap.model_patcher.LanPaint_NumSteps,
+ model_wrap.model_patcher.LanPaint_Friction,
+ model_wrap.model_patcher.LanPaint_Lambda,
+ model_wrap.model_patcher.LanPaint_Beta,
+ model_wrap.model_patcher.LanPaint_StepSize,
+ IS_FLUX = IS_FLUX,
+ IS_FLOW = IS_FLOW,
+ EarlyStopThreshold = getattr(model_wrap.model_patcher, "LanPaint_InnerThreshold", 0.0),
+ EarlyStopPatience = getattr(model_wrap.model_patcher, "LanPaint_InnerPatience", 1),
+ EarlyStopHook = extra_args.get("model_options", {}).get("lanpaint_semantic_hook", None))
+ model_k.LanPaint_early_stop = model_wrap.model_patcher.LanPaint_EarlyStop
+ #if not inpainting, after noise_scaling, noise = noise * sigma, which is the noise added to the clean latent image in the variance exploding diffusion model notation.
+ #if inpainting, after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
+ k_callback = None
+ total_steps = len(sigmas) - 1
+ if callback is not None:
+ k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
+ #print("LanPaint KSampler call sampler_function", self.sampler_function)
+ # The main loop!
+ #print("##########")
+ #print("Sampling with ", self.sampler_function)
+ #print("##########")
+ samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
+ #print("LanPaint KSampler end sampler_function")
+ samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
+ return samples
+
+@contextmanager
+def override_sample_function():
+ original_outer_sample = comfy.samplers.CFGGuider.outer_sample
+ comfy.samplers.CFGGuider.outer_sample = CFGGuider_LanPaint.outer_sample
+
+ original_predict_noise = comfy.samplers.CFGGuider.predict_noise
+ comfy.samplers.CFGGuider.predict_noise = CFGGuider_LanPaint.predict_noise
+
+ original_sample = comfy.samplers.KSAMPLER.sample
+ comfy.samplers.KSAMPLER.sample = KSAMPLER.sample
+
+ try:
+ yield
+ finally:
+ comfy.samplers.KSAMPLER.sample = original_sample
+ comfy.samplers.CFGGuider.predict_noise = original_predict_noise
+ comfy.samplers.CFGGuider.outer_sample = original_outer_sample
+
+
+class LanPaint_UpSale_LatentNoiseMask:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "samples": ("LATENT",),
+ "scale": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1}),
+ }}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "set_mask"
+
+
+ CATEGORY = "latent/inpaint"
+
+ def set_mask(self, samples, scale):
+ s = samples.copy()
+ samples = s['samples']
+ # generate a mask with every scaleth pixel set to 1
+ mask = torch.zeros(samples.shape[0], 1, samples.shape[2], samples.shape[3], device=samples.device) + 1
+ mask[:, :, ::scale, ::scale] = 0
+ s["noise_mask"] = mask
+ return (s,)
+
+#KSAMPLER_NAMES = ["euler", "dpmpp_2m", "uni_pc"]
+KSAMPLER_NAMES = ["euler","euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+ "dpm_fast", "dpmpp_sde", "dpmpp_sde_gpu",
+ "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm",
+ "deis", "res_multistep", "res_multistep_ancestral",
+ "gradient_estimation", "er_sde", "seeds_2", "seeds_3"]
+
+class LanPaint_KSampler():
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}),
+ "steps": ("INT", {"default": 30, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
+ "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
+ "sampler_name": (KSAMPLER_NAMES, {"tooltip": "Recommended: euler."}),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras", "tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
+ "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
+ "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
+ "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
+ "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"}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ OUTPUT_TOOLTIPS = ("The denoised latent.",)
+ FUNCTION = "sample"
+
+ 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="",Inpainting_mode="🖼️ Image Inpainting"):
+
+ model.LanPaint_StepSize = 0.2
+ model.LanPaint_Lambda = 16.0
+ model.LanPaint_Beta = 1.
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = 15.
+ model.LanPaint_EarlyStop = 1
+ model.LanPaint_InnerThreshold = 0.0
+ model.LanPaint_InnerPatience = 1
+ if LanPaint_PromptMode == "Image First":
+ 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:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": (["enable", "disable"], ),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "sampler_name": (KSAMPLER_NAMES, ),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "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"], ),
+ "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": 0.2, "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"}),
+ "LanPaint_InnerThreshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001, "round": 0.0001, "tooltip": "Early stop threshold for Langevin iterations based on semantic distance. 0.0 to disable. (Contributed by godnight10061)"}),
+ "LanPaint_InnerPatience": ("INT", {"default": 1, "min": 1, "max": 100, "tooltip": "Number of consecutive steps below threshold required to stop. (Contributed by godnight10061)"}),
+ },
+ }
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "sample"
+
+ 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, 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", LanPaint_InnerThreshold=0.0, LanPaint_InnerPatience=1):
+ force_full_denoise = True
+ if return_with_leftover_noise == "enable":
+ force_full_denoise = False
+ disable_noise = False
+ if add_noise == "disable":
+ disable_noise = True
+ model.LanPaint_StepSize = LanPaint_StepSize
+ model.LanPaint_Lambda = LanPaint_Lambda
+ model.LanPaint_Beta = LanPaint_Beta
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = LanPaint_Friction
+ model.LanPaint_EarlyStop = LanPaint_EarlyStop
+ model.LanPaint_InnerThreshold = LanPaint_InnerThreshold
+ model.LanPaint_InnerPatience = LanPaint_InnerPatience
+ if LanPaint_PromptMode == "Image First":
+ 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=1.0, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
+
+
+class MaskBlend:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image1": ("IMAGE", {"tooltip": "Image before inpaint"}),
+ "image2": ("IMAGE", {"tooltip": "Image after inpaint"}),
+ "mask": ("MASK",),
+ "blend_overlap": ("INT", {"default": 1, "min": 1, "max": 51, "step": 2, "tooltip": "The number of pixels to blend between the two images."})
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "blend_images"
+
+ CATEGORY = "image/postprocessing"
+
+ def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, mask: torch.Tensor, blend_overlap: int):
+ # smooth the binary 01 mask, keep 1 still 1, but smooth the transition from 1 to 0
+ # for each mask pixel, find out the nearest 1 pixel, and set the mask value to the distance between the two pixels
+ # check the size of mask and image1, image2, if not the same, assert error
+ if image1.shape[1] != image2.shape[1] or image1.shape[2] != image2.shape[2]:
+ raise ValueError(
+ "Image size mismatch: Image1 and Image2 must have the same dimensions.\n"
+ "Additionally, ensure both images have width and height that are multiples of 8.\n"
+ "This is required because VAE decode always generates images with dimensions that are multiples of 8.\n"
+ "If your input images are not multiples of 8, a size mismatch will occur during the decoding process.\n"
+ "Please resize your images using an image resize node to ensure compatibility.\n"
+ "Current sizes - Image1: {}x{}, Image2: {}x{}".format(
+ image1.shape[2], image1.shape[1], image2.shape[2], image2.shape[1]
+ )
+ )
+ mask = mask.float()
+ mask = torch.nn.functional.max_pool2d(mask, kernel_size=blend_overlap, stride=1, padding=blend_overlap//2)
+ # apply Gaussian blur with kernel size blend_overlap
+ kernel = self.gaussian_kernel(blend_overlap)
+ kernel = kernel.to(image1.device)
+ kernel = kernel[None, None, ...]
+
+ mask = torch.nn.functional.conv2d(mask[:,None,:,:], kernel, padding=blend_overlap//2)[:,0,:,:]
+
+
+ blended_image = image1 * (1 - mask[...,None]) + image2 * mask[...,None]
+ return (blended_image,)
+ def gaussian_kernel(self,kernel_size):
+ """
+ Creates a 2D Gaussian kernel with the given size and standard deviation (sigma).
+ """
+ sigma = (kernel_size - 1)/4
+ # Create a grid of (x, y) coordinates
+ x = torch.arange(kernel_size).float() - kernel_size // 2
+ y = torch.arange(kernel_size).float() - kernel_size // 2
+ x_grid, y_grid = torch.meshgrid(x, y, indexing='ij')
+
+ # Compute the Gaussian function
+ kernel = torch.exp(-(x_grid ** 2 + y_grid ** 2) / (2 * sigma ** 2))
+ kernel = kernel / kernel.sum() # Normalize the kernel
+
+ return kernel
+
+class Noise_EmptyNoise:
+ def generate_noise(self, latent):
+ return torch.zeros_like(latent["samples"])
+
+class Noise_RandomNoise:
+ def __init__(self, seed):
+ self.seed = seed
+ def generate_noise(self, latent):
+ torch.manual_seed(self.seed)
+ return torch.randn_like(latent["samples"])
+
+# Custom sampler implementation mimmicking base comfy nodes_custom_sampler.py
+class LanPaint_SamplerCustom:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": ("BOOLEAN", {"default": True}),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+ "positive": ("CONDITIONING",),
+ "negative": ("CONDITIONING",),
+ "sampler": ("SAMPLER",),
+ "sigmas": ("SIGMAS",),
+ "latent_image": ("LATENT",),
+ "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
+ "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
+ "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT", "LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+ FUNCTION = "sample"
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(self, model, sampler, sigmas, add_noise, noise_seed, cfg, positive, negative, latent_image, LanPaint_NumSteps, LanPaint_PromptMode, LanPaint_Info=""):
+ model.LanPaint_StepSize = 0.2
+ model.LanPaint_Lambda = 16.0
+ model.LanPaint_Beta = 1.
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = 15.
+ model.LanPaint_EarlyStop = 1
+ model.LanPaint_InnerThreshold = 0.0
+ model.LanPaint_InnerPatience = 1
+ if LanPaint_PromptMode == "Image First":
+ model.LanPaint_cfg_BIG = cfg
+ else:
+ model.LanPaint_cfg_BIG = 0 * cfg - 0.5
+ with override_sample_function():
+ latent = latent_image.copy()
+ latent_image = latent["samples"]
+ latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+ latent["samples"] = latent_image
+
+ if not add_noise:
+ noise = Noise_EmptyNoise().generate_noise(latent)
+ else:
+ noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+ noise_mask = None
+ if "noise_mask" in latent:
+ noise_mask = latent["noise_mask"]
+
+ x0_output = {}
+ callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+ disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+ samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+ out = latent.copy()
+ out["samples"] = samples
+ if "x0" in x0_output:
+ out_denoised = latent.copy()
+ out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+ else:
+ out_denoised = out
+ return (out, out_denoised)
+
+class LanPaint_SamplerCustomAdvanced:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"noise": ("NOISE",),
+ "guider": ("GUIDER", ),
+ "sampler": ("SAMPLER", ),
+ "sigmas": ("SIGMAS", ),
+ "latent_image": ("LATENT", ),
+ "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
+ "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
+ "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+ "LanPaint_Beta": ("FLOAT", {"default": 1.0, "min": 0.0001, "max": 5.0, "step": 0.1, "tooltip": "Step size ratio between masked/unmasked regions. Lower values balance high Lambda."}),
+ "LanPaint_Friction": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Friction parameter for fast Langevin. Lower values speed convergence but may be unstable."}),
+ "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
+ "LanPaint_EarlyStop": ("INT", {"default": 1, "min": 0, "max": 10000, "tooltip": "Steps to stop LanPaint early, preventing irregular patterns."}),
+ "LanPaint_Info": ("STRING", {"default": "LanPaint Custom Sampler Adv. For more info, visit https://github.com/scraed/LanPaint. If you find it useful, please give a star ⭐️!", "multiline": True}),
+ "LanPaint_InnerThreshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001, "round": 0.0001, "tooltip": "Early stop threshold for Langevin iterations based on semantic distance. 0.0 to disable. (Contributed by godnight10061)"}),
+ "LanPaint_InnerPatience": ("INT", {"default": 1, "min": 1, "max": 100, "tooltip": "Number of consecutive steps below threshold required to stop. (Contributed by godnight10061)"}),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT","LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+
+ FUNCTION = "sample"
+
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(self, noise, guider, sampler, sigmas, latent_image, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info="", LanPaint_InnerThreshold=0.0, LanPaint_InnerPatience=1):
+ model = guider.model_patcher
+ model.LanPaint_StepSize = LanPaint_StepSize
+ model.LanPaint_Lambda = LanPaint_Lambda
+ model.LanPaint_Beta = LanPaint_Beta
+ model.LanPaint_NumSteps = LanPaint_NumSteps
+ model.LanPaint_Friction = LanPaint_Friction
+ model.LanPaint_EarlyStop = LanPaint_EarlyStop
+ model.LanPaint_InnerThreshold = LanPaint_InnerThreshold
+ model.LanPaint_InnerPatience = LanPaint_InnerPatience
+ if LanPaint_PromptMode == "Image First":
+ model.LanPaint_cfg_BIG = guider.cfg
+ else:
+ model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
+ with override_sample_function():
+ 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"]
+
+ x0_output = {}
+ callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
+ disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+ samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+
+ out = latent.copy()
+ out["samples"] = samples
+ if "x0" in x0_output:
+ out_denoised = latent.copy()
+ out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+ else:
+ out_denoised = out
+ 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 = {
+ "LanPaint_KSampler": LanPaint_KSampler,
+ "LanPaint_KSamplerAdvanced": LanPaint_KSamplerAdvanced,
+ "LanPaint_SamplerCustom" : LanPaint_SamplerCustom,
+ "LanPaint_SamplerCustomAdvanced" : LanPaint_SamplerCustomAdvanced,
+ "LanPaint_MaskBlend": MaskBlend,
+# "LanPaint_UpSale_LatentNoiseMask": LanPaint_UpSale_LatentNoiseMask,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "LanPaint_KSampler": "LanPaint KSampler",
+ "LanPaint_KSamplerAdvanced": "LanPaint KSampler (Advanced)",
+ "LanPaint_SamplerCustom" : "LanPaint Sampler Custom",
+ "LanPaint_SamplerCustomAdvanced" : "LanPaint Sampler Custom (Advanced)",
+ "LanPaint_MaskBlend": "LanPaint Mask Blend",
+# "LanPaint_UpSale_LatentNoiseMask": "LanPaint UpSale Latent Noise Mask"
+}
diff --git a/LanPaint/src/LanPaint/types.py b/LanPaint/src/LanPaint/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a738a857d08623d64fe153aed731c56440bd9cb0
--- /dev/null
+++ b/LanPaint/src/LanPaint/types.py
@@ -0,0 +1,10 @@
+from typing import NamedTuple, Optional
+
+import torch
+
+
+class LangevinState(NamedTuple):
+ v: Optional[torch.Tensor]
+ C: Optional[torch.Tensor]
+ x0: Optional[torch.Tensor]
+
diff --git a/LanPaint/src/LanPaint/utils.py b/LanPaint/src/LanPaint/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..774ad4c7bd4980a27e6d892f7a5ed9ae70c03e25
--- /dev/null
+++ b/LanPaint/src/LanPaint/utils.py
@@ -0,0 +1,300 @@
+import torch
+def epxm1_x(x):
+ # Compute the (exp(x) - 1) / x term with a small value to avoid division by zero.
+ result = torch.special.expm1(x) / x
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x) < 1e-2
+ result = torch.where(mask, 1 + x/2. + x**2 / 6., result)
+ return result
+def epxm1mx_x2(x):
+ # Compute the (exp(x) - 1 - x) / x**2 term with a small value to avoid division by zero.
+ result = (torch.special.expm1(x) - x) / x**2
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x**2) < 1e-2
+ result = torch.where(mask, 1/2. + x/6 + x**2 / 24 + x**3 / 120, result)
+ return result
+
+def expm1mxmhx2_x3(x):
+ # Compute the (exp(x) - 1 - x - x**2 / 2) / x**3 term with a small value to avoid division by zero.
+ result = (torch.special.expm1(x) - x - x**2 / 2) / x**3
+ # replace NaN or inf values with 0
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ mask = torch.abs(x**3) < 1e-2
+ result = torch.where(mask, 1/6 + x/24 + x**2 / 120 + x**3 / 720 + x**4 / 5040, result)
+ return result
+
+def exp_1mcosh_GD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ # Main computation
+ is_positive = delta > 0
+ sqrt_abs_delta = torch.sqrt(torch.abs(delta))
+ gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta
+ numerator_pos = torch.exp(-gamma_t) - (torch.exp(gamma_t * (sqrt_abs_delta - 1)) + torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2
+ numerator_neg = torch.exp(-gamma_t) * ( 1 - torch.cos(gamma_t * sqrt_abs_delta ) )
+ numerator = torch.where(is_positive, numerator_pos, numerator_neg)
+ result = numerator / (delta * gamma_t**2 )
+ # Handle NaN/inf cases
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+ # Handle numerical instability for small delta
+ mask = torch.abs(gamma_t_sqrt_delta**2) < 5e-2
+ taylor = ( -0.5 - gamma_t**2 / 24 * delta - gamma_t**4 / 720 * delta**2 ) * torch.exp(-gamma_t)
+ result = torch.where(mask, taylor, result)
+ return result
+
+def exp_sinh_GsqrtD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ)
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ # Main computation
+ is_positive = delta > 0
+ sqrt_abs_delta = torch.sqrt(torch.abs(delta))
+ gamma_t_sqrt_delta = gamma_t * sqrt_abs_delta
+ numerator_pos = (torch.exp(gamma_t * (sqrt_abs_delta - 1)) - torch.exp(gamma_t * (-sqrt_abs_delta - 1))) / 2
+ result_pos = numerator_pos / gamma_t_sqrt_delta
+ result_pos = torch.where(torch.isfinite(result_pos), result_pos, torch.zeros_like(result_pos))
+
+ # Taylor expansion for small gamma_t_sqrt_delta
+ mask = torch.abs(gamma_t_sqrt_delta) < 1e-2
+ taylor = ( 1 + gamma_t**2 / 6 * delta + gamma_t**4 / 120 * delta**2 ) * torch.exp(-gamma_t)
+ result_pos = torch.where(mask, taylor, result_pos)
+
+ # Handle negative delta
+ result_neg = torch.exp(-gamma_t) * torch.special.sinc(gamma_t_sqrt_delta/torch.pi)
+ result = torch.where(is_positive, result_pos, result_neg)
+ return result
+
+def exp_cosh(gamma_t, delta):
+ """
+ Compute e^(-Γt) * cosh(Γt√Δ)
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_1mcosh_GD_result = exp_1mcosh_GD(gamma_t, delta) # e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+ result = torch.exp(-gamma_t) - gamma_t**2 * delta * exp_1mcosh_GD_result
+ return result
+def exp_sinh_sqrtD(gamma_t, delta):
+ """
+ Compute e^(-Γt) * sinh(Γt√Δ) / √Δ
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_sinh_GsqrtD_result = exp_sinh_GsqrtD(gamma_t, delta) # e^(-Γt) * sinh(Γt√Δ) / (Γt√Δ)
+ result = gamma_t * exp_sinh_GsqrtD_result
+ return result
+
+
+
+def zeta1(gamma_t, delta):
+ # Compute hyperbolic terms and exponential
+ half_gamma_t = gamma_t / 2
+ exp_cosh_term = exp_cosh(half_gamma_t, delta)
+ exp_sinh_term = exp_sinh_sqrtD(half_gamma_t, delta)
+
+
+ # Main computation
+ numerator = 1 - (exp_cosh_term + exp_sinh_term)
+ denominator = gamma_t * (1 - delta) / 4
+ result = 1 - numerator / denominator
+
+ # Handle numerical instability
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+
+ # Taylor expansion for small x (similar to your epxm1Dx approach)
+ mask = torch.abs(denominator) < 5e-3
+ term1 = epxm1_x(-gamma_t)
+ term2 = epxm1mx_x2(-gamma_t)
+ term3 = expm1mxmhx2_x3(-gamma_t)
+ taylor = term1 + (1/2.+ term1-3*term2)*denominator + (-1/6. + term1/2 - 4 * term2 + 10 * term3) * denominator**2
+ result = torch.where(mask, taylor, result)
+
+ return result
+
+def exp_cosh_minus_terms(gamma_t, delta):
+ """
+ Compute E^(-tΓ) * (Cosh[tΓ] - 1 - (Cosh[tΓ√Δ] - 1)/Δ) / (tΓ(1 - Δ))
+
+ Parameters:
+ gamma_t: Γ*t term (could be a scalar or tensor)
+ delta: Δ term (could be a scalar or tensor)
+
+ Returns:
+ Result of the computation with numerical stability handling
+ """
+ exp_term = torch.exp(-gamma_t)
+ # Compute individual terms
+ exp_cosh_term = exp_cosh(gamma_t, gamma_t**0) - exp_term # E^(-tΓ) (Cosh[tΓ] - 1) term
+ exp_cosh_delta_term = - gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) # E^(-tΓ) (Cosh[tΓ√Δ] - 1)/Δ term
+
+ #exp_1mcosh_GD e^(-Γt) * (1 - cosh(Γt√Δ))/ ( (Γt)**2 Δ )
+ # Main computation
+ numerator = exp_cosh_term - exp_cosh_delta_term
+ denominator = gamma_t * (1 - delta)
+
+ result = numerator / denominator
+
+ # Handle numerical instability
+ result = torch.where(torch.isfinite(result), result, torch.zeros_like(result))
+
+ # Taylor expansion for small gamma_t and delta near 1
+ mask = (torch.abs(denominator) < 1e-1)
+ exp_1mcosh_GD_term = exp_1mcosh_GD(gamma_t, delta**0)
+ taylor = (
+ gamma_t*exp_1mcosh_GD_term + 0.5 * gamma_t * exp_sinh_GsqrtD(gamma_t, delta**0)
+ - denominator / 4 * ( 0.5 * exp_cosh(gamma_t, delta**0) - 4 * exp_1mcosh_GD_term - 5 /2 * exp_sinh_GsqrtD(gamma_t, delta**0) )
+ )
+ result = torch.where(mask, taylor, result)
+
+ return result
+
+
+def zeta2(gamma_t, delta):
+ half_gamma_t = gamma_t / 2
+ return exp_sinh_GsqrtD(half_gamma_t, delta)
+
+def sig11(gamma_t, delta):
+ return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta)
+
+
+def Zcoefs(gamma_t, delta):
+ Zeta1 = zeta1(gamma_t, delta)
+ Zeta2 = zeta2(gamma_t, delta)
+
+ sq_total = 1 - Zeta1 + gamma_t * (delta - 1) * (Zeta1 - 1)**2 / 8
+ amplitude = torch.sqrt(sq_total)
+ Zcoef1 = ( gamma_t**0.5 * Zeta2 / 2 **0.5 ) / amplitude
+ Zcoef2 = Zcoef1 * gamma_t *( - 2 * exp_1mcosh_GD(gamma_t, delta) / sig11(gamma_t, delta) ) ** 0.5
+ #cterm = exp_cosh_minus_terms(gamma_t, delta)
+ #sterm = exp_sinh_sqrtD(gamma_t, delta**0) + exp_sinh_sqrtD(gamma_t, delta)
+ #Zcoef3 = 2 * torch.sqrt( cterm / ( gamma_t * (1 - delta) * cterm + sterm ) )
+ Zcoef3 = torch.sqrt( torch.maximum(1 - Zcoef1**2 - Zcoef2**2, sq_total.new_zeros(sq_total.shape)) )
+
+ return Zcoef1 * amplitude, Zcoef2 * amplitude, Zcoef3 * amplitude, amplitude
+
+def Zcoefs_asymp(gamma_t, delta):
+ A_t = (gamma_t * (1 - delta) )/4
+ return epxm1_x(- 2 * A_t)
+
+class StochasticHarmonicOscillator:
+ """
+ Simulates a stochastic harmonic oscillator governed by the equations:
+ dy(t) = q(t) dt
+ dq(t) = -Γ A y(t) dt + Γ C dt + Γ D dw(t) - Γ q(t) dt
+
+ Also define v(t) = q(t) / √Γ, which is numerically more stable.
+
+ Where:
+ y(t) - Position variable
+ q(t) - Velocity variable
+ Γ - Damping coefficient
+ A - Harmonic potential strength
+ C - Constant force term
+ D - Noise amplitude
+ dw(t) - Wiener process (Brownian motion)
+ """
+ def __init__(self, Gamma, A, C, D):
+ self.Gamma = Gamma
+ self.A = A
+ self.C = C
+ self.D = D
+ self.Delta = 1 - 4 * A / Gamma
+ def sig11(self, gamma_t, delta):
+ return 1 - torch.exp(-gamma_t) + gamma_t**2 * exp_1mcosh_GD(gamma_t, delta) + exp_sinh_sqrtD(gamma_t, delta)
+ def sig22(self, gamma_t, delta):
+ return 1- zeta1(2*gamma_t, delta) + 2 * gamma_t * exp_1mcosh_GD(gamma_t, delta)
+ def dynamics(self, y0, v0, t):
+ """
+ Calculates the position and velocity variables at time t.
+
+ Parameters:
+ y0 (float): Initial position
+ v0 (float): Initial velocity v(0) = q(0) / √Γ
+ t (float): Time at which to evaluate the dynamics
+ Returns:
+ tuple: (y(t), v(t))
+ """
+
+ dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0
+ Delta = self.Delta + dummyzero
+ Gamma_hat = self.Gamma * t + dummyzero
+ A = self.A + dummyzero
+ C = self.C + dummyzero
+ D = self.D + dummyzero
+ Gamma = self.Gamma + dummyzero
+ zeta_1 = zeta1( Gamma_hat, Delta)
+ zeta_2 = zeta2( Gamma_hat, Delta)
+ EE = 1 - Gamma_hat * zeta_2
+
+ if v0 is None:
+ v0 = torch.randn_like(y0) * D / 2 ** 0.5
+ #v0 = (C - A * y0)/Gamma**0.5
+
+ # Calculate mean position and velocity
+ term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t
+ y_mean = term1 + y0
+ v_mean = (1 - EE)*(C - A * y0) / (Gamma ** 0.5) + (EE - A * t * (1 - zeta_1)) * v0
+
+ cov_yy = D**2 * t * self.sig22(Gamma_hat, Delta)
+ cov_vv = D**2 * self.sig11(Gamma_hat, Delta) / 2
+ cov_yv = (zeta2(Gamma_hat, Delta) * Gamma_hat * D ) **2 / 2 / (Gamma ** 0.5)
+
+ # sample new position and velocity with multivariate normal distribution
+
+ batch_shape = y0.shape
+ cov_matrix = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
+ cov_matrix[..., 0, 0] = cov_yy
+ cov_matrix[..., 0, 1] = cov_yv
+ cov_matrix[..., 1, 0] = cov_yv # symmetric
+ cov_matrix[..., 1, 1] = cov_vv
+
+
+
+ # Compute the Cholesky decomposition to get scale_tril
+ #scale_tril = torch.linalg.cholesky(cov_matrix)
+ scale_tril = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
+ tol = 1e-8
+ cov_yy = torch.clamp( cov_yy, min = tol )
+ sd_yy = torch.sqrt( cov_yy )
+ inv_sd_yy = 1/(sd_yy)
+
+ scale_tril[..., 0, 0] = sd_yy
+ scale_tril[..., 0, 1] = 0.
+ scale_tril[..., 1, 0] = cov_yv * inv_sd_yy
+ scale_tril[..., 1, 1] = torch.clamp( cov_vv - cov_yv**2 / cov_yy, min = tol ) ** 0.5
+ # check if it matches torch.linalg.
+ #assert torch.allclose(torch.linalg.cholesky(cov_matrix), scale_tril, atol = 1e-4, rtol = 1e-4 )
+ # Sample correlated noise from multivariate normal
+ mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype)
+ mean[..., 0] = y_mean
+ mean[..., 1] = v_mean
+ new_yv = torch.distributions.MultivariateNormal(
+ loc=mean,
+ scale_tril=scale_tril
+ ).sample()
+
+ return new_yv[...,0], new_yv[...,1]
diff --git a/LanPaint/tests/__init__.py b/LanPaint/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..717f0f7eee9420b5025564f75e883f0722b3f23f
--- /dev/null
+++ b/LanPaint/tests/__init__.py
@@ -0,0 +1 @@
+"""Unit test package for LanPaint."""
diff --git a/LanPaint/tests/conftest.py b/LanPaint/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..310609c5ef6b8aff1813f92aca3b954c3b93e9cb
--- /dev/null
+++ b/LanPaint/tests/conftest.py
@@ -0,0 +1,6 @@
+import os
+import sys
+
+# Add the project root directory to Python path
+# This allows the tests to import the project
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
diff --git a/LanPaint/tests/pytest.ini b/LanPaint/tests/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..e2884105999d8b98a256f564e7eaf393c8b1f52e
--- /dev/null
+++ b/LanPaint/tests/pytest.ini
@@ -0,0 +1,5 @@
+[pytest]
+# Keep settings value-only; pytest does not treat inline `# ...` as comments.
+testpaths = .
+python_files = test_*.py
+norecursedirs = ..
diff --git a/LanPaint/tests/test_LanPaint.py b/LanPaint/tests/test_LanPaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..355dddf06da0e24e154afe942f6767a6e711d3ae
--- /dev/null
+++ b/LanPaint/tests/test_LanPaint.py
@@ -0,0 +1,13 @@
+"""Basic import tests for LanPaint.
+
+The ComfyUI runtime dependencies (e.g. `comfy`) are intentionally optional for unit tests.
+"""
+
+
+def test_package_imports_without_comfy() -> None:
+ import LanPaint
+
+ assert isinstance(LanPaint.NODE_CLASS_MAPPINGS, dict)
+ assert isinstance(LanPaint.NODE_DISPLAY_NAME_MAPPINGS, dict)
+ assert "LanPaint_KSampler" in LanPaint.NODE_CLASS_MAPPINGS
+ assert LanPaint.WEB_DIRECTORY == "./web"
diff --git a/LanPaint/tests/test_lanpaint_semantic_stop.py b/LanPaint/tests/test_lanpaint_semantic_stop.py
new file mode 100644
index 0000000000000000000000000000000000000000..e96acbe47e94968878d819b3af8ec0e83dd91bf8
--- /dev/null
+++ b/LanPaint/tests/test_lanpaint_semantic_stop.py
@@ -0,0 +1,104 @@
+import torch
+
+from src.LanPaint.lanpaint import LanPaint as LanPaintEngine
+
+
+class _DummySampling:
+ def noise_scaling(self, sigma, noise, latent_image): # type: ignore[no-untyped-def]
+ return latent_image + noise * sigma
+
+
+class _DummyModel:
+ def __init__(self) -> None:
+ self.inner_model = self
+ self.model_sampling = _DummySampling()
+
+ def __call__(self, x, sigma, model_options=None, seed=None): # type: ignore[no-untyped-def]
+ return x, x
+
+
+def _inputs(): # type: ignore[no-untyped-def]
+ x = torch.zeros((1, 4, 8, 8))
+ latent_image = torch.zeros_like(x)
+ noise = torch.ones_like(x)
+ sigma = torch.tensor([1.0])
+
+ latent_mask = torch.zeros_like(x)
+ current_times = (sigma, torch.tensor([0.5]), torch.tensor([0.0]))
+ return x, latent_image, noise, sigma, latent_mask, current_times
+
+
+def test_default_semantic_stop_triggers_at_patience_without_custom_distance_fn() -> None:
+ engine = LanPaintEngine(
+ _DummyModel(),
+ NSteps=10,
+ Friction=15.0,
+ Lambda=1.0,
+ Beta=1.0,
+ StepSize=0.2,
+ )
+
+ calls = {"langevin": 0, "with_score": 0, "without_score": 0}
+
+ def fake_langevin(x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None): # type: ignore[no-untyped-def]
+ calls["langevin"] += 1
+ if score is None:
+ calls["without_score"] += 1
+ else:
+ calls["with_score"] += 1
+ return x_t, args
+
+ engine.langevin_dynamics = fake_langevin # type: ignore[method-assign]
+
+ model_options = {
+ "lanpaint_semantic_stop": {
+ "threshold": 1e-6,
+ "patience": 2,
+ }
+ }
+
+ x, latent_image, noise, sigma, latent_mask, current_times = _inputs()
+ engine(x, latent_image, noise, sigma, latent_mask, current_times, model_options=model_options, seed=0, n_steps=10)
+
+ assert calls["langevin"] == 3
+ assert calls["with_score"] == 3
+ assert calls["without_score"] == 0
+
+
+def test_semantic_stop_is_disabled_when_no_inpaint_region() -> None:
+ engine = LanPaintEngine(
+ _DummyModel(),
+ NSteps=10,
+ Friction=15.0,
+ Lambda=1.0,
+ Beta=1.0,
+ StepSize=0.2,
+ )
+
+ calls = {"langevin": 0, "with_score": 0, "without_score": 0}
+
+ def fake_langevin(x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None): # type: ignore[no-untyped-def]
+ calls["langevin"] += 1
+ if score is None:
+ calls["without_score"] += 1
+ else:
+ calls["with_score"] += 1
+ return x_t, args
+
+ engine.langevin_dynamics = fake_langevin # type: ignore[method-assign]
+
+ model_options = {
+ "lanpaint_semantic_stop": {
+ "threshold": 1e-6,
+ "patience": 1,
+ }
+ }
+
+ x, latent_image, noise, sigma, latent_mask, _ = _inputs()
+ current_times = (sigma, torch.tensor([0.5]), torch.tensor([0.0]))
+ no_inpaint_mask = torch.ones_like(latent_mask)
+ engine(x, latent_image, noise, sigma, no_inpaint_mask, current_times, model_options=model_options, seed=0, n_steps=10)
+
+ assert calls["langevin"] == 10
+ assert calls["with_score"] == 10
+ assert calls["without_score"] == 0
diff --git a/LanPaint/tests/test_reshape_mask.py b/LanPaint/tests/test_reshape_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..4de8fff31708bea6c5272fb7d49fdff90c74dd20
--- /dev/null
+++ b/LanPaint/tests/test_reshape_mask.py
@@ -0,0 +1,74 @@
+import importlib
+import sys
+import types
+
+import pytest
+import torch
+
+
+def _repeat_to_batch_size(tensor: torch.Tensor, batch_size: int) -> torch.Tensor:
+ if tensor.shape[0] == batch_size:
+ return tensor
+ if tensor.shape[0] == 1:
+ return tensor.repeat((batch_size,) + (1,) * (tensor.ndim - 1))
+ repeats = (batch_size + tensor.shape[0] - 1) // tensor.shape[0]
+ return tensor.repeat((repeats,) + (1,) * (tensor.ndim - 1))[:batch_size]
+
+
+def _import_nodes(monkeypatch, comfyui_version: str):
+ comfy_mod = types.ModuleType("comfy")
+ comfy_mod.__path__ = []
+
+ comfy_utils_mod = types.ModuleType("comfy.utils")
+ comfy_utils_mod.repeat_to_batch_size = _repeat_to_batch_size
+
+ comfy_samplers_mod = types.ModuleType("comfy.samplers")
+ class DummyKSAMPLER: ...
+ comfy_samplers_mod.KSAMPLER = DummyKSAMPLER
+
+ comfy_model_base_mod = types.ModuleType("comfy.model_base")
+ class ModelType:
+ FLUX = "FLUX"
+ FLOW = "FLOW"
+
+ class WAN22: ...
+ comfy_model_base_mod.ModelType = ModelType
+ comfy_model_base_mod.WAN22 = WAN22
+
+ comfyui_version_mod = types.ModuleType("comfyui_version")
+ comfyui_version_mod.__version__ = comfyui_version
+
+ comfy_mod.utils = comfy_utils_mod
+ comfy_mod.samplers = comfy_samplers_mod
+ comfy_mod.model_base = comfy_model_base_mod
+
+ monkeypatch.setitem(sys.modules, "comfy", comfy_mod)
+ monkeypatch.setitem(sys.modules, "comfy.utils", comfy_utils_mod)
+ monkeypatch.setitem(sys.modules, "comfy.samplers", comfy_samplers_mod)
+ monkeypatch.setitem(sys.modules, "comfy.model_base", comfy_model_base_mod)
+ monkeypatch.setitem(sys.modules, "nodes", types.ModuleType("nodes"))
+ monkeypatch.setitem(sys.modules, "latent_preview", types.ModuleType("latent_preview"))
+ monkeypatch.setitem(sys.modules, "comfyui_version", comfyui_version_mod)
+
+ sys.modules.pop("src.LanPaint.nodes", None)
+ return importlib.import_module("src.LanPaint.nodes")
+
+
+@pytest.mark.parametrize("comfyui_version", ["0.5.0", "0.6.0"])
+def test_reshape_mask_accepts_bhw_and_5d_output_shape(monkeypatch, comfyui_version: str) -> None:
+ lanpaint_nodes = _import_nodes(monkeypatch, comfyui_version)
+ input_mask = torch.zeros((1, 4, 4))
+ output_shape = (1, 16, 1, 8, 8)
+
+ out = lanpaint_nodes.reshape_mask(input_mask, output_shape, video_inpainting=False)
+ assert tuple(out.shape) == output_shape
+
+
+def test_prepare_mask_accepts_hw_and_moves_device(monkeypatch) -> None:
+ lanpaint_nodes = _import_nodes(monkeypatch, "0.5.0")
+ input_mask = torch.zeros((4, 4))
+ output_shape = (2, 3, 8, 8)
+
+ out = lanpaint_nodes.prepare_mask(input_mask, output_shape, device=torch.device("cpu"), video_inpainting=False)
+ assert tuple(out.shape) == output_shape
+ assert out.device.type == "cpu"
diff --git a/LanPaint/tests/test_sho_regression.py b/LanPaint/tests/test_sho_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf46119a096288f1e67c6e2fb4fd19e18c991219
--- /dev/null
+++ b/LanPaint/tests/test_sho_regression.py
@@ -0,0 +1,45 @@
+import torch
+from unittest.mock import MagicMock, patch
+from src.LanPaint.lanpaint import LanPaint
+
+
+def test_langevin_dynamics_fallback_on_nan() -> None:
+ """Test that langevin_dynamics falls back to overdamped dynamics if damped dynamics produces NaNs."""
+ torch.manual_seed(0)
+ # Setup minimal LanPaint instance
+ lp = LanPaint(Model=MagicMock(), NSteps=10, Friction=1.0, Lambda=1.0, Beta=1.0, StepSize=0.1)
+ # Dummy inputs
+ # Shape: (Batch, Channel, Height, Width)
+ x_t = torch.randn(1, 4, 8, 8)
+ lp.img_dim_size = 4
+ mask = torch.zeros_like(x_t)
+ # Simple score function
+ def score(x):
+ return torch.zeros_like(x)
+ step_size = torch.tensor([0.1])
+ # (sigma, abt, flow_t)
+ current_times = (torch.tensor([0.5]), torch.tensor([0.5]), torch.tensor([0.5]))
+ # Mock StochasticHarmonicOscillator to return NaNs
+ # We patch it where it is used (imported) in lanpaint.py
+ with patch("src.LanPaint.lanpaint.StochasticHarmonicOscillator") as MockSHO:
+ mock_instance = MockSHO.return_value
+ # Configure dynamics to return NaNs
+ nan_tensor = torch.full_like(x_t, float('nan'))
+ mock_instance.dynamics.return_value = (nan_tensor, nan_tensor)
+ # Execute langevin_dynamics
+ # This should try run_damped -> get NaNs -> raise ValueError -> catch -> run_overdamped
+ x_out, args_out = lp.langevin_dynamics(x_t, score, mask, step_size, current_times, sigma_y=1.0)
+ assert hasattr(args_out, "v")
+ assert hasattr(args_out, "C")
+ assert hasattr(args_out, "x0")
+ assert args_out[0] is args_out.v
+ assert args_out[1] is args_out.C
+ assert args_out[2] is args_out.x0
+ v_out = args_out[0]
+ # Verify that SHO was initialized and dynamics called
+ MockSHO.assert_called()
+ mock_instance.dynamics.assert_called()
+ # Verify result is finite (indicating fallback to overdamped logic was successful)
+ assert torch.isfinite(x_out).all(), "Output contains NaNs, fallback failed"
+ assert v_out is None or torch.isfinite(v_out).all()
+
diff --git a/LanPaint/web/js/example.js b/LanPaint/web/js/example.js
new file mode 100644
index 0000000000000000000000000000000000000000..b2ebc8a1fe71086e51f1542119760d03127ccd8c
--- /dev/null
+++ b/LanPaint/web/js/example.js
@@ -0,0 +1 @@
+console.log(app);
diff --git a/VibeVoice-ComfyUI/LICENSE b/VibeVoice-ComfyUI/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9edf0e7689772227f8d9889b78ea6ead1a81d140
--- /dev/null
+++ b/VibeVoice-ComfyUI/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Fabio Sarracino - enemyx.net
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/VibeVoice-ComfyUI/README.md b/VibeVoice-ComfyUI/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a61fc4f03302bc0272fc34ecb88ff80f1a2d7be9
--- /dev/null
+++ b/VibeVoice-ComfyUI/README.md
@@ -0,0 +1,770 @@
+# VibeVoice ComfyUI Nodes
+
+A comprehensive ComfyUI integration for Microsoft's VibeVoice text-to-speech model, enabling high-quality single and multi-speaker voice synthesis directly within your ComfyUI workflows.
+
+## ✨ Features
+
+### Core Functionality
+- 🎤 **Single Speaker TTS**: Generate natural speech with optional voice cloning
+- 👥 **Multi-Speaker Conversations**: Support for up to 4 distinct speakers
+- 🎯 **Voice Cloning**: Clone voices from audio samples
+- 🎨 **LoRA Support**: Fine-tune voices with custom LoRA adapters (v1.4.0+)
+- 🎚️ **Voice Speed Control**: Adjust speech rate by modifying reference voice speed (v1.5.0+)
+- 📝 **Text File Loading**: Load scripts from text files
+- 📚 **Automatic Text Chunking**: Handles long texts seamlessly with configurable chunk size
+- ⏸️ **Custom Pause Tags**: Insert silences with `[pause]` and `[pause:ms]` tags (wrapper feature)
+- 🔄 **Node Chaining**: Connect multiple VibeVoice nodes for complex workflows
+- ⏹️ **Interruption Support**: Cancel operations before or between generations
+- 🔧 **Flexible Configuration**: Control temperature, sampling, and guidance scale
+
+### Performance & Optimization
+- ⚡ **Attention Mechanisms**: Choose between auto, eager, sdpa, flash_attention_2 or sage
+- 🎛️ **Diffusion Steps**: Adjustable quality vs speed trade-off (default: 20)
+- 💾 **Memory Management**: Toggle automatic VRAM cleanup after generation
+- 🧹 **Free Memory Node**: Manual memory control for complex workflows
+- 🍎 **Apple Silicon Support**: Native GPU acceleration on M1/M2/M3 Macs via MPS
+- 🔢 **8-Bit Quantization**: Perfect audio quality with high VRAM reduction
+- 🔢 **4-Bit Quantization**: Maximum VRAM savings with minimal quality loss
+
+### Compatibility & Installation
+- 📦 **Self-Contained**: Embedded VibeVoice code, no external dependencies
+- 🔄 **Universal Compatibility**: Adaptive support for transformers v4.51.3+
+- 🖥️ **Cross-Platform**: Works on Windows, Linux, and macOS
+- 🎮 **Multi-Backend**: Supports CUDA, CPU, and MPS (Apple Silicon)
+
+## 🎥 Video Demo
+View Original / Masked / Inpainted Comparison
+ +| Original | Masked | Inpainted | +|:--------:|:------:|:---------:| +|  |  |  | + +
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+ Click to watch the demo video
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