Add Qwen image support and new inpainting examples

Updated the README to announce Qwen image support and provide usage instructions. Added new example images and workflow files for Qwen inpainting in the examples directory.
Generalize dimension handling in LanPaint and fix mask reshape
2025-08-08 14:09:53 +08:00 · 2025-08-08 13:42:24 +08:00 · 2025-06-21 14:13:09 +08:00 · 2025-06-21 14:09:11 +08:00 · 2025-06-21 13:18:25 +08:00 · 2025-06-21 13:10:05 +08:00
42 changed files with 1019 additions and 321 deletions
@@ -7,14 +7,18 @@ on:
    paths:
      - "pyproject.toml"

+permissions:
+  issues: write
+
 jobs:
  publish-node:
    name: Publish Custom Node to registry
    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'scraed' }}
    steps:
      - name: Check out code
        uses: actions/checkout@v4
      - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
        with:
          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github Repository secrets and reference it here.
@@ -1,55 +1,35 @@
 # LanPaint (Thinking mode Inpaint)

-Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, aiming for seamless and accurate results. We encourage you to try it out and share your feedback through issues or discussions, as your input will help us enhance the algorithm's performance and stability.
+Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior quality.  
+![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
+
+This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). 

 ## Features

- 🎨 **Zero-Training Inpainting** - Works immediately with ANY SD model, even custom models you've trained yourself
- 🛠️ **Simple Integration** - Same workflow as standard ComfyUI KSampler
- 🚀 **Quality Enhancements** - High quality and seamless inpainting
+- **Universal Compatibility** – Works instantly with almost any model (SD 1.5, XL, 3.5, Flux, HiDream, 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. 

-## Example Results
-All examples use random seed 0 to ensure fair comparison.
-### Example 1: Basket to Basket Ball (LanPaint K Sampler, It is fast).
-![Inpainting Result 1](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_04.jpg)  
-[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 2: White Shirt to Blue Shirt (LanPaint K Sampler (Advanced), it is slower.)
-![Inpainting Result 2](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_05.jpg)  
-[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 3: Smile to Sad (LanPaint K Sampler (Advanced))
-![Inpainting Result 3](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_06.jpg)  
-[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 4: Damage Restoration (LanPaint K Sampler (Advanced))
-![Inpainting Result 3](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_07.jpg)  
-[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 5: Huge Damage Restoration (LanPaint K Sampler (Advanced))
-![Inpainting Result 3](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_08.jpg)  
-[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) (The key is increase LanPaint_stepsize to 0.5)
+**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).

-**How to Use These Examples:**  
-1. Navigate to the **example** folder (i.e example_1) by clicking **View Workflow & Masks**, download all pictures.  
-2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
-3. Download the required model from Civitai by clicking **Model Used in This Example**.  
-4. Load the model into the **"Load Checkpoint"** node.  
-5. Upload **Original_No_Mask.png** to the **"Load image"** node in the **"Original Image"** group (far left).  
-6. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left).  
-7. Queue the task, you will get inpainted results from three methods:  
-   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)** (middle),  
-   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)** (second from right),  
-   - **LanPaint** (far right).  
+## How It Works  
+LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results.

-Compare and explore the results from each method!
+LanPaint introduces "BIG score" that creates a **two-way alignment** between masked and unmasked areas. It continuously evaluates:  
+- *"Does the new content make sense with the existing elements?"*  
+- *"Do the existing elements support the new creation?"*  
+Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions.  
+  
+LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.

-![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG)  

 ## Quickstart

-1. **Install ComfyUI**: Follow the official [ComfyUI installation guide](https://docs.comfy.org/get_started) to set up ComfyUI on your system.  
+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.  
@@ -62,6 +42,120 @@ Compare and explore the results from each method!

 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!

+
+
+## Updates
+- 2025/08/08
+    - Add Qwen image support
+- 2025/06/21
+    - Update the algorithm with enhanced stability and outpaint performance.
+    - Add outpaint example
+    - Supports Sampler Custom (Thanks to [MINENEMA](https://github.com/MINENEMA))
+- 2025/06/04
+    - Add more sampler support.
+    - Add early stopping to advanced sampler.
+- 2025/05/28
+    - Major update on the Langevin solver. It is now much faster and more stable.
+    - Greatly simplified the parameters for advanced sampler.
+    - Fix performance issue on Flux and SD 3.5
+- 2025/04/16
+    - Added Primary HiDream support
+- 2025/03/22
+    - Added Primary Flux support
+    - Added Tease Mode
+- 2025/03/10
+    - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
+
+## Examples
+
+### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
+We are excited to announce that LanPaint now supports Qwen Image, providing powerful inpainting capabilities for image editing.
+
+![Inpainting Result 14](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_14.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_11)
+
+You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
+
+The following examples utilize a random seed of 0 to generate a batch of 4 images for variance demonstration and fair comparison. (Note: Generating 4 images may exceed your GPU memory; please adjust the batch size as necessary.)
+
+### Example HiDream: InPaint (LanPaint K Sampler, 5 steps of thinking)
+![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_11.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
+
+### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
+![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13(1).jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
+
+You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model. Thanks [Amazon90](https://github.com/Amazon90) for providing this example.
+
+### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
+![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_12.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
+
+You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki.com/en/tutorial/advanced/stable-diffusion-3-5-comfyui-workflow) to download and install the model.
+
+### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
+![Inpainting Result 7](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_10.jpg)  
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
+[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)
+![Inpainting Result 6](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_09.jpg)  
+[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).
+![Inpainting Result 1](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_04.jpg)  
+[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)
+![Inpainting Result 2](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_05.jpg)  
+[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)
+![Inpainting Result 3](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_06.jpg)  
+[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)
+![Inpainting Result 4](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_07.jpg)  
+[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)
+![Inpainting Result 5](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_08.jpg)  
+[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).
+
+
+## **How to Use These Examples:**  
+1. Navigate to the **example** folder (i.e example_1) by clicking **View Workflow & Masks**, download all pictures.  
+2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
+3. Download the required model from Civitai by clicking **Model Used in This Example**.  
+4. Load the model into the **"Load Checkpoint"** node.  
+5. Upload **Original_No_Mask.png** to the **"Load image"** node in the **"Original Image"** group (far left).  
+6. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left).  
+7. Queue the task, you will get inpainted results from three methods:  
+   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)** (middle),  
+   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)** (second from right),  
+   - **LanPaint** (far right).  
+8. You also get an output from "masked blend" node, which copy the original image and paste onto the unmasked part of output. It is useful if you want unmasked region to match original picture pixel perfectly.
+
+Compare and explore the results from each method!
+
+![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG)  
+
+
 ## Usage

 **Workflow Setup**  
@@ -71,53 +165,82 @@ Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes.
 - 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
+## Basic Sampler
 ![Samplers](https://github.com/scraed/LanPaint/blob/master/Nodes.JPG)  
-**LanPaint KSampler**  
+
+- 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: 50+ recommended
- LanPaint NumSteps: 1-10 (complexity of edits)
- Built-in parameter presets
+- 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)**  
+### LanPaint KSampler (Advanced)
 Full parameter control:
-## Key Parameters
+**Key Parameters**

 | Parameter | Range | Description |
 |-----------|-------|-------------|
-| `Steps` | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 50. |
-| `LanPaint_NumSteps` | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 1-2. Hard task: 5-10 |
-| `LanPaint_Lambda` | 0.1-50 | Content alignment strength (higher = stricter). Recommend 6.0 |
-| `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1 for most cases, 0.5 for some difficult cases. |
-| `LanPaint_cfg_BIG` | 0-20 | CFG scale used when aligning masked and unmasked region (higher = better alignment). Recommend 8 for seamless inpaint (i.e limbs, faces), 0-1 for character consistency (i.e multiple view) |
+| `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 **Primary Adjustments**:
- Increase **steps**, **LanPaint_StepSize**，**LanPaint_NumSteps** (thinking iterations), and **LanPaint_cfg_BIG** (guidance scale).
+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 **Secondary Tweaks**:  
- Boost **LanPaint_Lambda** (spatial constraint strength) or **LanPaint_StepSize** (denoising aggressiveness).
+2️⃣ **Boost Speed**
+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 **Balance Speed vs Stability**:  
- Reduce **LanPaint_Friction** to prioritize faster results with fewer "thinking" steps (*may risk instability*).  
- Increase **LanPaint_Tamed** (noise normalization onto a sphere) or **LanPaint_Alpha** (constraint the friction of underdamped Langevin dynamics) to suppress artifacts.
+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**:  
- Optimal parameters vary depending on the **model** and the **size of the inpainting area**.  
- For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting.  
+- 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.
+
+## ToDo
+- Try Implement Detailer
+- Provide inference code on without GUI.

 ## Contribute

 - 2025/03/06: Bug Fix for str not callable error and unpack error. Big thanks to [jamesWalker55](https://github.com/jamesWalker55) and [EricBCoding](https://github.com/EricBCoding).

-Help us improve LanPaint! 🚀 **Report bugs**, share **example cases**, or contribute your **personal parameter settings** to benefit the community. 
+
+## Citation
+
+```
+@misc{zheng2025lanpainttrainingfreediffusioninpainting,
+      title={Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference}, 
+      author={Candi Zheng and Yuan Lan and Yang Wang},
+      year={2025},
+      eprint={2502.03491},
+      archivePrefix={arXiv},
+      primaryClass={eess.IV},
+      url={https://arxiv.org/abs/2502.03491}, 
+}
+```
+



@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "LanPaint"
-version = "0.0.8"
+version = "1.2.0"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
  {name = "LanPaint", email = "czhengac@connect.ust.hk"}
@@ -0,0 +1,174 @@
+import torch
+from .utils import *
+from functools import partial
+
+class LanPaint():
+    def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False):
+        self.n_steps = NSteps
+        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
+    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 n_steps is None:
+            n_steps = self.n_steps
+        return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
+    def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW):
+        VE_Sigma, abt, Flow_t = current_times
+
+        
+        step_size = self.step_size * (1 - abt)
+        step_size = self.add_none_dims(step_size)
+        # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
+        # This is the replace step
+        x = x * (1 - latent_mask) +  self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
+        
+        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
+        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 )
+            x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)  
+        if IS_FLUX or IS_FLOW:
+            x = x_t / ( 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
+        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):
+        # 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 = self.x0_evalutation(x_t, score, sigma, args)
+            C = (abt**0.5 * x0  - x_t )/ (1-abt) + A * x_t
+            return C
+        def advance_time(x_t, v, dt, Gamma, A, C, D):
+            dtype = x_t.dtype
+            with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+                osc = StochasticHarmonicOscillator(Gamma, A, C, D )
+                x_t, v = osc.dynamics(x_t, v, dt )
+            x_t = x_t.to(dtype)
+            v = v.to(dtype)
+            return x_t, v
+        if args is None:
+            #v = torch.zeros_like(x_t)
+            v = None
+            C = Coef_C(x_t)
+            #print(torch.squeeze(dtx), torch.squeeze(dty))
+            x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
+        else:
+            v, C = args
+
+            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+
+            C_new = Coef_C(x_t)
+            v = v + Gamma**0.5 * ( C_new - C) *dt
+
+            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+
+            C = C_new
+  
+        return x_t, (v, C)
+
+    def 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
+
+
+
+    def x0_evalutation(self, x_t, score, sigma, args):
+        x0 = x_t + score(x_t)
+        return x0
@@ -8,15 +8,16 @@ import latent_preview
 from functools import partial
 from comfy.utils import repeat_to_batch_size
 from comfy.samplers import *
-# Monkey patch comfy.samplers module by importing with absolute package path
-#exec(inspect.getsource(comfy.samplers).replace("from .", "from comfy."))
+from comfy.model_base import ModelType
+from .utils import *
+from .lanpaint import LanPaint
+

 def reshape_mask(input_mask, output_shape):
    dims = len(output_shape) - 2

-
    scale_mode = "nearest-exact"
-    mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+    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])
@@ -76,10 +77,29 @@ class KSamplerX0Inpaint:
        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):
+    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
-        # print what is inside model_options
+        ### 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
+
+        # 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})
@@ -87,205 +107,31 @@ class KSamplerX0Inpaint:
            denoise_mask = (denoise_mask > 0.5).float()

            latent_mask = 1 - denoise_mask
+            current_times = (VE_Sigma, abt, Flow_t)

-            abt = 1/( 1+sigma**2 )
+            current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) )
+            total_steps = len(self.sigmas)-1

-            if self.step_time_schedule == "dual_shrink":
-                step_size = self.step_size * (1 - abt) ** 0.5 * abt ** 0.5
-            elif self.step_time_schedule == "follow_sampler":
-                time_ind = torch.argmin(torch.abs(self.sigmas - sigma))
-                times = torch.log( 1+ self.sigmas**2)
-                time_intervals = times[1:] - times[:-1]
-                time_intervals = time_intervals / time_intervals[0]
-                step_size = time_intervals[time_ind] * self.step_size 
+            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:
-                step_size = self.step_size * (1 - abt) ** 0.5
-
-            
-            current_times = (sigma, abt)
-
-            # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
-            x = x * (1 - latent_mask) +  self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
-            x_t = x #/ ( 1+sigma**2 )**0.5 # switch to variance perserving x_t values
-            # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
-            args = None
-            for i in range(self.n_steps):
-
-                if sigma > self.start_sigma or sigma < self.end_sigma:
-                    break
-
-                score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = abt, sigma = sigma, model_options = model_options, seed = seed )
-                if self.step_size_schedule == "linear":
-                    step_size_i = step_size * (1 - i/(self.n_steps) )
-                else:
-                    step_size_i = step_size 
-                x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size_i , current_times, sigma_x = self.sigma_x(abt), sigma_y = self.sigma_y(abt), args = args)  
-            x = x_t #* ( 1+sigma**2 )**0.5
-            # out is x_0
-            out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
-            out = out * denoise_mask + self.latent_image * latent_mask
+                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
-    def mid_times(self, current_times, step_size):
-        sigma, abt = current_times
-        tt = torch.log(1+sigma**2)
-        tt_mid = torch.max( tt - step_size, tt*0 )
-        sigma_mid = (torch.exp(tt_mid) - 1) ** 0.5
-        sigma_mid_prev = sigma_mid
-        # find the closest sigma to sigma_mid from self.sigmas
-        #sigma_mid = self.model_sigmas[torch.argmin(torch.abs(self.model_sigmas - sigma_mid))]

-        abt_mid = 1/(1+sigma_mid**2)
-        return sigma_mid, abt_mid
-    def score_model(self, x_t, y, mask, abt, sigma, model_options, seed):
-        
-        # the score function for the Langevin dynamics
-        lamb = self.chara_lamb
-        beta = self.chara_beta * (1-abt)**0.5
-
-        x_0, x_0_BIG = self.inner_model(x_t, sigma, model_options=model_options, seed=seed)
-        e_t = x_t / ((1 - abt) ** 0.5 * (1 + sigma**2) ** 0.5 )- (abt ** 0.5  / (1 - abt) ** 0.5) * x_0
-        e_t_BIG = x_t / ((1 - abt) ** 0.5 * (1 + sigma**2) ** 0.5 )- (abt ** 0.5  / (1 - abt) ** 0.5) * x_0_BIG
-        
-        score_x = -e_t
-        score_y = - (1 + lamb) * ( x_t/ ((1 + sigma**2) ** 0.5 *(1 - abt)**0.5) - abt**0.5 /(1 - abt)**0.5 * y )  + lamb *  e_t_BIG
-        return score_x * (1 - mask) + score_y * mask
-    def sigma_x(self, abt):
-        # the time scale for the x_t update
-        return 1
-    def sigma_y(self, abt):
-        # the time scale for the y_t update
-        if self.beta_scale == "shrink":
-            beta = self.chara_beta * (1-abt)**0.5
-        elif self.beta_scale == "dual_shrink":
-            beta = self.chara_beta * (1-abt)**0.5 * abt ** 0.5
-        elif self.beta_scale == "back_shrink":
-            beta = self.chara_beta * abt ** 0.5
-        else:
-            beta = self.chara_beta
-        return beta
-    def langevin_dynamics(self, x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None):
-        # -------------------------------------------------------------------------
-        # Unpack current times parameters (sigma and abt)
-        sigma, abt = current_times
-
-        # Compute time step (dtx, dty) for x and y branches.
-        dtx = 2 * step_size * sigma_x
-        dty = 2 * step_size * sigma_y
-
-        if self.step_time_schedule == "dual_shrink":
-            ref_dt = 0.1 * (1-abt)**0.5 * abt ** 0.5
-        else:
-            ref_dt = 0.1 * (1-abt)**0.5
-
-        # -------------------------------------------------------------------------
-        # Define friction parameter Gamma_hat for each branch.
-        # Using dtx**0 provides a tensor of the proper device/dtype.
-        Gamma_hat_x = self.friction * dtx / (1e-4+ 2 * sigma_x * ref_dt)
-        Gamma_hat_y = self.friction * dty / (1e-4+ 2 * sigma_y * ref_dt)
-
-        # Get mid time parameters (sigma_mid and abt_mid) for each branch.
-        sigma_mid_x, abt_mid_x = self.mid_times(current_times, dtx)
-        sigma_mid_y, abt_mid_y = self.mid_times(current_times, dty)
-
-        if sigma_mid_x >= sigma or sigma_mid_y >= sigma:
-            return x_t, args
-
-        # -------------------------------------------------------------------------
-        # A: Update epsilon (score estimate and noise initialization)
-        # -------------------------------------------------------------------------
-        # Compute the score-based epsilon (scaled as sqrt(1-abt))
-        score_model = score(x_t)
-        eps_model = -score_model 
-
-
-
-        # Initialize epsilon and Z if not provided in args.
-        if args is None:
-            eps = eps_model
-            Z = torch.randn_like(x_t)
-        else:
-            eps, Z = args
-
-        # -------------------------------------------------------------------------
-        # B: Update epsilon mean dynamics and compute the mid-point in z-space.
-        # -------------------------------------------------------------------------
-        # Compute the weighted combination term for epsilon mean update:
-        # term = (2/Γ_hat)*(1-exp(-0.5*Γ_hat))
-        term_x = 2.0 / (Gamma_hat_x + 1e-4) * (1 - torch.exp(-0.5 * Gamma_hat_x))
-        term_y = 2.0 / (Gamma_hat_y + 1e-4) * (1 - torch.exp(-0.5 * Gamma_hat_y))
-        eps_bar_x = term_x * eps + (1 - term_x) * eps_model
-        eps_bar_y = term_y * eps + (1 - term_y) * eps_model
-        # Combine branches according to mask.
-        eps_bar = eps_bar_x * (1 - mask) + eps_bar_y * mask
-
-        # Form the denoised epsilon using self.alpha (assumed to be 1/Ψ)
-        eps_denoise = self.alpha * eps_bar + (1 - self.alpha) * eps_model
-
-
-        # tamed
-        eps_model_x = eps_denoise* (1 - mask)
-        eps_model_x = eps_model_x* (torch.sum(1 - mask, dim = (1,2,3))/torch.sum(eps_model_x**2, dim = (1,2,3))) **0.5 ** torch.minimum(self.tamed*(dtx),sigma**0)#/( 1 + self.tamed*(sigma - sigma_mid_x) * (torch.sum(eps_model_x**2)/torch.sum((1 - mask)))**0.5 )
-        eps_model_y = eps_denoise* mask
-        eps_model_y = eps_model_y* (torch.sum(mask, dim = (1,2,3))/torch.sum(eps_model_y**2, dim = (1,2,3))) **0.5 ** torch.minimum(self.tamed*(dty),sigma**0)#/( 1 + self.tamed*(sigma - sigma_mid_y) * (torch.sum(eps_model_y**2)/torch.sum(mask))**0.5 )
-        eps_denoise = eps_model_x * (1 - mask) + eps_model_y * mask
-
-
-        # Update the mean epsilon for the next step:
-        eps_x = eps * torch.exp(-0.5 * Gamma_hat_x) + eps_model * (1 - torch.exp(-0.5 * Gamma_hat_x))
-        eps_y = eps * torch.exp(-0.5 * Gamma_hat_y) + eps_model * (1 - torch.exp(-0.5 * Gamma_hat_y))
-        eps = eps_x * (1 - mask) + eps_y * mask
-
-        # Transform x to z using z = x * sqrt(1+sigma^2). Here we have already set x to z to avoid floating point stability issue.
-        z_t = x_t #* (1 + sigma**2) ** 0.5
-
-        # Compute the mid-point update in z-space for each branch:
-        z_mid_x = z_t + eps_denoise * (sigma_mid_x - sigma)
-        z_mid_y = z_t + eps_denoise * (sigma_mid_y - sigma)
-        z_mid = z_mid_x * (1 - mask) + z_mid_y * mask
-
-        # -------------------------------------------------------------------------
-        # C: Update noise terms and finalize the x update.
-        # -------------------------------------------------------------------------
-        # Generate auxiliary noise terms.
-        Z_q     = torch.randn_like(x_t)
-        Z_q_avg = torch.randn_like(x_t)
-        Z_z     = torch.randn_like(x_t)
-
-        # Update Z for each branch:
-        Z_x = torch.exp(-0.5 * Gamma_hat_x) * Z + (1 - torch.exp(-Gamma_hat_x)) ** 0.5 * Z_q
-        Z_y = torch.exp(-0.5 * Gamma_hat_y) * Z + (1 - torch.exp(-Gamma_hat_y)) ** 0.5 * Z_q
-        Z_next = Z_x * (1 - mask) + Z_y * mask
-
-        # Compute the combined noise update following the scheme:
-        Z_comb_x = (
-            (1 - torch.exp(-Gamma_hat_x / 2)) / torch.sqrt(Gamma_hat_x + 1e-4) *
-            (Z + torch.sqrt(torch.tanh(Gamma_hat_x / 4)) * Z_q)
-            + torch.sqrt(1 - (4 / (Gamma_hat_x + 1e-4)) * torch.tanh(Gamma_hat_x / 4)) * Z_q_avg
-        )
-        Z_comb_y = (
-            (1 - torch.exp(-Gamma_hat_y / 2)) / torch.sqrt(Gamma_hat_y + 1e-4) *
-            (Z + torch.sqrt(torch.tanh(Gamma_hat_y / 4)) * Z_q)
-            + torch.sqrt(1 - (4 / (Gamma_hat_y + 1e-4)) * torch.tanh(Gamma_hat_y / 4)) * Z_q_avg
-        )
-        Z_comb = Z_comb_x * (1 - mask) + Z_comb_y * mask
-
-        # Combine with an additional noise term using self.alpha.
-        Z_comb = self.alpha ** 0.5 * Z_comb + (1 - self.alpha) ** 0.5 * Z_z
-
-        # Compute the change in sigma (dsigma = sqrt(sigma^2 - sigma_mid^2)).
-        dsigma_x = sigma * torch.sqrt(1 - (sigma_mid_x / sigma) ** 2)
-        dsigma_y = sigma * torch.sqrt(1 - (sigma_mid_y / sigma) ** 2)
-        dsigma = dsigma_x * (1 - mask) + dsigma_y * mask
-
-        # Final z update.
-        z_final = z_mid + Z_comb * dsigma
-
-        # Transform back to x-space: x = z / sqrt(1+sigma^2)
-        x_t = z_final #/ (1 + sigma**2) ** 0.5
-
-        return x_t, (eps, Z_next)
 # 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):
@@ -299,30 +145,38 @@ class KSAMPLER(comfy.samplers.KSAMPLER):
            model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
        else:
            model_k.noise = noise
-        model_wrap.cfg_BIG = model_wrap.model_patcher.LanPaint_cfg_BIG
-        model_k.step_size = model_wrap.model_patcher.LanPaint_StepSize
-        model_k.chara_lamb = model_wrap.model_patcher.LanPaint_Lambda
-        model_k.chara_beta = model_wrap.model_patcher.LanPaint_Beta
-        model_k.n_steps = model_wrap.model_patcher.LanPaint_NumSteps
-        model_k.friction = model_wrap.model_patcher.LanPaint_Friction
-        model_k.alpha = model_wrap.model_patcher.LanPaint_Alpha
-        model_k.tamed = model_wrap.model_patcher.LanPaint_Tamed
-        model_k.beta_scale = model_wrap.model_patcher.LanPaint_BetaScale
-        model_k.step_size_schedule = model_wrap.model_patcher.LanPaint_StepSizeSchedule
-        model_k.step_time_schedule = model_wrap.model_patcher.LanPaint_StepTimeSchedule
-        model_k.start_sigma = model_wrap.model_patcher.LanPaint_StartSigma
-        model_k.end_sigma = model_wrap.model_patcher.LanPaint_EndSigma
+
+        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)
+        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)
+        #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")
+        #print("LanPaint KSampler end sampler_function")
        samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
        return samples

@@ -345,9 +199,33 @@ def override_sample_function():
        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"


-KSAMPLER_NAMES = ["euler", "dpmpp_2m", "uni_pc"]
+    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
@@ -356,16 +234,17 @@ class LanPaint_KSampler():
            "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": 50, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
+                "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, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
+                "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": 10, "min": 0, "max": 20, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),  
-                "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler. Recommend steps 50 ( increase steps boosts performance ), LanPaint NumSteps 1-10 depending on the difficulty of task. For more information, visit https://github.com/scraed/LanPaint", "multiline": True}),
+                "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}),
                  }
        }

@@ -376,20 +255,18 @@ class LanPaint_KSampler():
    CATEGORY = "sampling"
    DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."

-    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, LanPaint_StepSize=0.05, LanPaint_NumSteps=5, LanPaint_Info=""):
-        model.LanPaint_StepSize = 0.3
-        model.LanPaint_Lambda = 6.0
-        model.LanPaint_Beta = 0.6
+    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=""):
+
+        model.LanPaint_StepSize = 0.15
+        model.LanPaint_Lambda = 16.0
+        model.LanPaint_Beta = 1.
        model.LanPaint_NumSteps = LanPaint_NumSteps
-        model.LanPaint_Friction = 10.
-        model.LanPaint_Alpha = 0.5
-        model.LanPaint_Tamed = 0.1
-        model.LanPaint_BetaScale = "shrink"
-        model.LanPaint_StepSizeSchedule = "linear"
-        model.LanPaint_StepTimeSchedule = "shrink"
-        model.LanPaint_StartSigma = 20.
-        model.LanPaint_EndSigma = 1.
-        model.LanPaint_cfg_BIG = cfg
+        model.LanPaint_Friction = 15.
+        model.LanPaint_EarlyStop = 1
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = cfg
+        else:
+            model.LanPaint_cfg_BIG = 0*cfg - 0.5
        with override_sample_function():
            return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
 class LanPaint_KSamplerAdvanced:
@@ -399,7 +276,7 @@ class LanPaint_KSamplerAdvanced:
                    {"model": ("MODEL",),
                    "add_noise": (["enable", "disable"], ),
                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                    "steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
+                    "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, ),
@@ -409,20 +286,14 @@ class LanPaint_KSamplerAdvanced:
                    "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": 10, "min": 0, "max": 20, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
-                "LanPaint_Lambda": ("FLOAT", {"default": 6., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The lambda parameter for the bidirectional guidance. Higher values align with known regions more closely, but may result in instability."}),
-                "LanPaint_StepSize": ("FLOAT", {"default": 0.3, "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": 0.6, "min": 0.0001, "max": 5, "step": 0.1, "round": 0.1, "tooltip": "The beta parameter for the bidirectional guidance. Scale the step size for the known region independently for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
-                "LanPaint_Friction": ("FLOAT", {"default": 10., "min": 1., "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The friction parameter for the underdamped Langevin dynamics, higher values result in faster convergence but may be unstable."}),
-                "LanPaint_Alpha": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1., "step": 0.1, "round": 0.1, "tooltip": "The (rescaled) alpha parameter for the HFHR langevin dynamics, mixes Langevin dynamics and underdamped Langevin dynamics with a friction term. 0 corresponds to Langevin dynamics, 1 corresponds to underdamped Langevin dynamics."}),
-                "LanPaint_Tamed": ("FLOAT", {"default": 1., "min": 0.000, "max": 20., "step": 0.1, "round": 0.1, "tooltip": "The tame strength for the noise, normalize and projects the noise onto unit sphere to enhance stability."}),
-                "LanPaint_BetaScale": (["shrink", "fixed", "dual_shrink", "back_shrink"], {"default": "shrink", "tooltip": "The beta scale, determines how the beta parameter changes over time. Shrink: beta = beta * (1 - alpha bar) ** 0.5; Fixed: beta = beta; Dual_shrink: beta = beta * (1 - alpha bar) ** 0.5 *  alpha bar ** 0.5; Back_shrink: beta = beta *  alpha bar ** 0.5; Alpha bar: the alpha cumprod."}),
-                "LanPaint_StepSizeSchedule": (["const", "linear"], {"default": "linear", "tooltip": "The step size schedule for the Langevin dynamics, const: constant step size, linear: linearly decreasing step size."}),
-                "LanPaint_StepTimeSchedule": (["shrink", "dual_shrink", "follow_sampler"], {"default": "shrink", "tooltip": "The step size schedule for the first step of Langevin dynamics during diffusion sampling, shrink: step size = step size * (1 - alpha bar) ** 0.5; Dual_shrink: step size = step size * (1 - alpha bar) ** 0.5 *  alpha bar ** 0.5; Follow_sampler: scale with the sampler step size."}),
-                "LanPaint_StartSigma": ("FLOAT", {"default": 20., "min": 0.0001, "max": 20.0, "step": 0.1, "round": 0.1, "tooltip": "Start 'thinking' with Langevin dynamics at this sigma value."}),
-                "LanPaint_EndSigma": ("FLOAT", {"default": 1., "min": 0.000, "max": 20.0, "step": 0.1, "round": 0.1, "tooltip": "Stop 'thinking' with Langevin dynamics at this sigma value."}),
-                "LanPaint_cfg_BIG": ("FLOAT", {"default": 8., "min": 0., "max": 20.0, "step": 0.1, "round": 0.1, "tooltip": "The CFG scale used in the bidirectional guidance (for the known region only). Higher value results in more closely matching the known region."}),
-                "LanPaint_Info": ("STRING", {"default": "LanPaint KSampler Advanced. For difficult tasks, first try increasing steps, LanPaint_NumSteps, and LanPaint_cfg_BIG. Then try increase LanPaint_Lambda or LanPaint_StepSize. Decrease LanPaint_Friction if you want to obtain good results with fewer turns of thinking (LanPaint_NumSteps) at the risk of irregular behavior. Increase LanPaint_Tamed or LanPaint_Alpha can suppress irregular behavior. For more information, visit https://github.com/scraed/LanPaint", "multiline": True}),
+                "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
+                "LanPaint_Lambda": ("FLOAT", {"default": 16., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
+                "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
+                "LanPaint_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}),
                     },
                }

@@ -431,7 +302,7 @@ class LanPaint_KSamplerAdvanced:

    CATEGORY = "sampling"

-    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, LanPaint_StepSize=0.05, LanPaint_Lambda=5, LanPaint_Beta=1, LanPaint_NumSteps=5, LanPaint_Friction=5, LanPaint_Alpha=1, LanPaint_Tamed=0., LanPaint_BetaScale="fixed", LanPaint_StepSizeSchedule = "const", LanPaint_StepTimeSchedule = "shrink",  LanPaint_StartSigma=20, LanPaint_EndSigma=0, LanPaint_cfg_BIG = 5., LanPaint_Info=""):
+    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, LanPaint_StepSize=0.05, LanPaint_Lambda=5, LanPaint_Beta=1, LanPaint_NumSteps=5, LanPaint_Friction=5, LanPaint_PromptMode = "Image First", LanPaint_EarlyStop = 1, LanPaint_Info=""):
        force_full_denoise = True
        if return_with_leftover_noise == "enable":
            force_full_denoise = False
@@ -443,27 +314,252 @@ class LanPaint_KSamplerAdvanced:
        model.LanPaint_Beta = LanPaint_Beta
        model.LanPaint_NumSteps = LanPaint_NumSteps
        model.LanPaint_Friction = LanPaint_Friction
-        model.LanPaint_Alpha = LanPaint_Alpha
-        model.LanPaint_Tamed = LanPaint_Tamed
-        model.LanPaint_BetaScale = LanPaint_BetaScale
-        model.LanPaint_StepSizeSchedule = LanPaint_StepSizeSchedule
-        model.LanPaint_StepTimeSchedule = LanPaint_StepTimeSchedule
-        model.LanPaint_StartSigma = LanPaint_StartSigma
-        model.LanPaint_EndSigma = LanPaint_EndSigma
-        model.LanPaint_cfg_BIG = LanPaint_cfg_BIG
+        model.LanPaint_EarlyStop = LanPaint_EarlyStop
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = cfg
+        else:
+            model.LanPaint_cfg_BIG = 0*cfg - 0.5
+
        with override_sample_function():
            return nodes.common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)


+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("Make sure your image size is a multiple of 8. Otherwise the mask will not be aligned with the output image.")
+        
+        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 = 0.15
+        model.LanPaint_Lambda = 16.0
+        model.LanPaint_Beta = 1.
+        model.LanPaint_NumSteps = LanPaint_NumSteps
+        model.LanPaint_Friction = 15.
+        model.LanPaint_EarlyStop = 1
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = cfg
+        else:
+            model.LanPaint_cfg_BIG = 0 * cfg - 0.5
+        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",),
+                     "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": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
+                     "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
+                     "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+                     "LanPaint_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}),
+                    }
+               }
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("output", "denoised_output")
+    FUNCTION = "sample"
+    CATEGORY = "sampling/custom_sampling"
+
+    def sample(self, noise, guider, sampler, sigmas, latent_image, start_at_step, end_at_step, return_with_leftover_noise, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
+        force_full_denoise = True
+        if end_at_step <= start_at_step:
+            raise ValueError('end_at_step must be larger than start_at_step')
+        if return_with_leftover_noise == "enable":
+            force_full_denoise = False
+        model = guider.model_patcher
+        model.LanPaint_StepSize = LanPaint_StepSize
+        model.LanPaint_Lambda = LanPaint_Lambda
+        model.LanPaint_Beta = LanPaint_Beta
+        model.LanPaint_NumSteps = LanPaint_NumSteps
+        model.LanPaint_Friction = LanPaint_Friction
+        model.LanPaint_EarlyStop = LanPaint_EarlyStop
+        if LanPaint_PromptMode == "Image First":
+            model.LanPaint_cfg_BIG = guider.cfg
+        else:
+            model.LanPaint_cfg_BIG = 0 * guider.cfg - 0.5
+        with override_sample_function():
+            latent = latent_image.copy()
+            latent_image_samples = latent["samples"]
+            latent_image_samples = comfy.sample.fix_empty_latent_channels(model, latent_image_samples)
+            latent["samples"] = latent_image_samples
+
+            noise_mask = None
+            if "noise_mask" in latent:
+                noise_mask = latent["noise_mask"]
+
+            # From base comfy samplers.py
+            if end_at_step is not None and end_at_step < (len(sigmas) - 1):
+                sigmas = sigmas[:end_at_step + 1]
+                if force_full_denoise:
+                    sigmas[-1] = 0
+
+            if start_at_step is not None:
+                if start_at_step < (len(sigmas) - 1):
+                    sigmas = sigmas[start_at_step:]
+                else:
+                    if latent_image is not None:
+                        return latent_image
+                    else:
+                        return torch.zeros_like(noise)
+
+            x0_output = {}
+            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+            disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+            samples = guider.sample( noise.generate_noise(latent), latent_image_samples, sampler, sigmas, denoise_mask=noise_mask, callback=callback,disable_pbar=disable_pbar, seed=noise.seed
+            )
+
+            samples = samples.to(comfy.model_management.intermediate_device())
+            out = latent.copy()
+            out["samples"] = samples
+            if "x0" in x0_output:
+                out_denoised = latent.copy()
+                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+            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_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"
 }
@@ -0,0 +1,301 @@
+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
+    denominator_pos = gamma_t_sqrt_delta
+    result_pos = numerator_pos / gamma_t_sqrt_delta
+    result_pos = torch.where(torch.isfinite(result_pos), result_pos, torch.zeros_like(result_pos))
+
+    # 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]
Author	SHA1	Message	Date
scraed	7aeb6e535f	Add Qwen image support and new inpainting examples Updated the README to announce Qwen image support and provide usage instructions. Added new example images and workflow files for Qwen inpainting in the examples directory.	2025-08-08 14:09:53 +08:00
scraed	eda0f19944	Generalize dimension handling in LanPaint and fix mask reshape Refactored LanPaint to dynamically handle tensors with varying numbers of dimensions by introducing add_none_dims and remove_none_dims utility methods. Updated all relevant tensor broadcasting to use these methods, improving flexibility. Also fixed reshape_mask in nodes.py to use the last two dimensions for resizing, ensuring correct mask shape.	2025-08-08 13:42:24 +08:00
scraed	e20c8f20ce	update version	2025-06-21 14:13:09 +08:00
scraed	89b9010b35	Fix indentation error in sample method Corrected an indentation issue in the sample method of LanPaint_SamplerCustomAdvanced to ensure proper execution of the end_at_step check.	2025-06-21 14:09:11 +08:00
scraed	ee0c65656e	simplify custom sampler	2025-06-21 13:18:25 +08:00
scraed	48b1dd4be6	Merge branch 'master' into pr/33	2025-06-21 13:10:05 +08:00
scraed	9d304cd5a3	update readme	2025-06-21 13:03:30 +08:00
scraed	61c19ac31d	update algorithm with better outpaint	2025-06-21 12:56:36 +08:00
Bagier	1ca6e53090	adjust info names	2025-06-20 08:31:51 +07:00
Bagier	520932cee0	add invalid value check	2025-06-19 20:49:20 +07:00
Bagier	37612bb399	Logic fix	2025-06-19 20:12:50 +07:00
Bagier	0e6cb45081	re-add lanpaint info	2025-06-19 19:25:59 +07:00
Bagier	0a5e36d55e	add advanced custom sampler	2025-06-19 19:15:58 +07:00
Bagier	b319f772cb	fix parameter update error	2025-06-19 19:00:41 +07:00
Bagier	340376ad18	Add custom sampler variation	2025-06-19 16:14:26 +07:00
scraed	dbfc1585fc	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-06-16 20:03:31 +08:00
scraed	c3ae2c644d	update coefficients	2025-06-16 20:03:25 +08:00
scraed	c7017373c9	Update pyproject.toml	2025-06-08 17:59:04 +08:00
scraed	850f707eb6	Remove redundant comment	2025-06-08 17:58:40 +08:00
scraed	62870f060a	Update README.md	2025-06-06 13:05:50 +08:00
scraed	a91cefacf0	Update README.md	2025-06-06 13:05:16 +08:00
scraed	4265f71a85	Update README.md	2025-06-05 18:15:21 +08:00
scraed	4189ba80c2	add error message	2025-06-05 17:10:07 +08:00
scraed	23ad6e47fd	add new masked blend node	2025-06-05 16:21:46 +08:00
scraed	4d3d5d17f0	Update README.md	2025-06-05 01:22:09 +08:00
scraed	b86f3b7112	update version	2025-06-05 01:14:57 +08:00
scraed	96b7f3eecd	fix sigma batch error	2025-06-05 01:11:04 +08:00
scraed	e472574783	update readme	2025-06-04 21:33:58 +08:00
scraed	0b799be8ec	support more sampler	2025-06-04 21:28:51 +08:00
scraed	82da0b4142	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-06-04 20:04:58 +08:00
scraed	c66155e783	add early stop	2025-06-04 20:04:53 +08:00
scraed	d802f2f2b0	Update README.md fix typo	2025-06-04 14:34:10 +08:00
scraed	6153a450c9	Update README.md	2025-06-04 14:33:07 +08:00
scraed	493658d23a	Update README.md	2025-06-04 14:28:11 +08:00
scraed	7de1054b39	Update README.md	2025-06-04 09:06:39 +08:00
scraed	574d9906ec	Update README.md	2025-06-03 20:54:12 +08:00
scraed	61d4ecef75	Update README.md	2025-06-03 18:41:24 +08:00
scraed	5eb2d8ef48	Update README.md	2025-06-03 17:19:52 +08:00
scraed	b668bcc42c	Update README.md	2025-06-03 13:11:47 +08:00
scraed	05386f98d3	replace outdated flux picture	2025-06-03 11:49:40 +08:00
scraed	ad5704d25f	correct tricks for consistency	2025-06-03 11:06:35 +08:00
scraed	2ab72851b9	update summary img	2025-06-03 10:29:54 +08:00
scraed	14f7907c70	update version	2025-06-03 10:19:41 +08:00
scraed	5adce4ecdd	update readme	2025-05-28 18:11:30 +08:00
scraed	2fd945e69b	update readme and pictures	2025-05-28 18:04:52 +08:00
scraed	1bd6932cae	change to ve notation and update examples	2025-05-28 17:22:15 +08:00
scraed	59b31303c8	update examples	2025-05-27 10:44:15 +08:00
scraed	f906bd9cc9	reduce parameters	2025-05-22 15:01:36 +08:00
scraed	31e4909438	switch to sampler alg	2025-05-22 10:14:58 +08:00
scraed	6775e6ac37	1-a schedule and shared A	2025-05-17 19:06:43 +08:00
scraed	49b35bcb28	fix Zcoef asymp bug	2025-05-14 15:55:18 +08:00
scraed	13bd3182bf	switch to separate file	2025-05-13 19:14:46 +08:00
scraed	bb3e0078d4	switch to new new alg	2025-05-13 18:33:08 +08:00
scraed	5fc4cf2092	remove y time truncate	2025-05-12 22:34:44 +08:00
scraed	3f8e2b833c	change y update alg	2025-05-12 22:27:56 +08:00
scraed	0f546f09b6	add truncate time step	2025-05-12 16:38:35 +08:00
scraed	99ac3ee06d	Create utils.py	2025-05-12 11:40:05 +08:00
scraed	505fbfb7fc	fix tamed bug	2025-05-12 09:49:12 +08:00
scraed	aa541edaea	change default lambda schedule to const	2025-05-03 23:16:07 +08:00
scraed	5382fbba21	remove redundant time step schedule	2025-05-03 22:31:59 +08:00
scraed	82a6798e29	change parameter range	2025-05-03 22:28:25 +08:00
scraed	3b7c79f431	add lamb schedule	2025-05-03 01:01:02 +08:00
scraed	ae31ac7e70	complete migration to new alg formula	2025-05-02 22:31:37 +08:00
scraed	2a67dd353f	update epxm1Dx	2025-05-02 21:14:54 +08:00
scraed	2d0f458695	update eps	2025-05-02 13:50:19 +08:00
scraed	6f2deeda51	separate ld	2025-05-02 00:38:58 +08:00
scraed	76cd0a5c1d	sort tamed	2025-05-01 21:08:45 +08:00
scraed	f90636d4c0	create hidream img	2025-04-23 22:05:02 +08:00
scraed	dc4ef3aed4	Update README.md	2025-04-22 08:55:51 +08:00
scraed	eca581ee79	Update README.md	2025-04-17 15:38:32 +08:00
scraed	75a91710f2	Update pyproject.toml	2025-04-16 23:50:17 +08:00
scraed	f8eea61264	Update README.md	2025-04-16 23:48:58 +08:00
scraed	e42d3b0426	Update README.md	2025-04-16 23:32:46 +08:00
scraed	ee567b14e8	Update Example for Hidream	2025-04-16 23:21:55 +08:00
scraed	10a790974c	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-04-16 23:12:18 +08:00
scraed	00d2eb9885	Update hidream example	2025-04-16 23:12:13 +08:00
scraed	b1139f285a	Update Citationn	2025-04-16 22:58:40 +08:00
scraed	eb69a21cf2	Update README.md for hidream support	2025-04-16 22:26:05 +08:00
scraed	c863ade839	update version	2025-04-16 22:25:05 +08:00
scraed	b07ed256c7	Primary support for Hidream	2025-04-16 22:24:18 +08:00
scraed	612cfb51f2	Merge pull request #19 from ComfyNodePRs/update-publish-yaml Update Github Action for Publishing to Comfy Registry	2025-04-11 20:42:47 +08:00
scraed	aa9319f7cf	Update README.md	2025-03-22 21:26:08 +08:00
scraed	a92d19d4f8	Updates dates	2025-03-22 21:23:12 +08:00
scraed	478b077c69	Update pyproject.toml	2025-03-22 21:18:12 +08:00
scraed	3015d6f0ef	Update README.md	2025-03-22 21:17:17 +08:00
scraed	55cd84be93	Update README.md for flux and tease mode	2025-03-22 21:16:40 +08:00
scraed	758eae62c3	add flux example	2025-03-22 21:08:23 +08:00
scraed	58f8d01acf	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-03-22 20:14:27 +08:00
scraed	1684b616f1	Merge pull request #16 from charrywhite/master Add preview hood in KSamplerX0Inpaint	2025-03-22 20:10:07 +08:00
scraed	65f98f7c48	disable CFG BIG for Flux	2025-03-22 20:05:30 +08:00
charrywhite	5d8e40a669	Add preview hood in KSamplerX0Inpaint	2025-03-22 19:57:59 +08:00
scraed	ff1fb62d1a	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-03-22 19:46:42 +08:00
scraed	b7d37ef121	flux support and parameter change add flux support and eliminate parameters a and b	2025-03-22 19:46:40 +08:00
scraed	29c9d877be	Update ToDo	2025-03-19 23:10:21 +08:00
scraed	6c1375c35f	update control net support in readme	2025-03-13 23:10:49 +08:00
scraed	8c04ba410b	More intuitive how it works	2025-03-13 22:29:46 +08:00
scraed	a5c08aa0a4	Update how it works	2025-03-13 17:45:32 +08:00
scraed	cefc2c43d3	add version requirement in README.md	2025-03-13 10:15:36 +08:00
scraed	62c5934a1a	Update README.md	2025-03-12 17:10:47 +08:00
scraed	9ae04abb14	Update README.md	2025-03-12 10:15:22 +08:00
scraed	8446a1a735	Update README.md	2025-03-12 09:55:46 +08:00
scraed	439cc8371a	Update tuning guide	2025-03-12 08:58:42 +08:00
scraed	0b6adec240	Update README.md	2025-03-11 23:46:52 +08:00
scraed	32dd4f23ff	Update README.md	2025-03-11 22:49:04 +08:00
scraed	ef927e6a9a	Update README.md	2025-03-11 12:14:01 +08:00
scraed	142595b8ac	Update README.md	2025-03-11 12:13:20 +08:00
scraed	26dd8dcf8e	Update README.md	2025-03-11 12:08:09 +08:00
scraed	c4e8ca9fb9	Update README.md	2025-03-11 10:54:51 +08:00
scraed	6073fb9846	Update README.md	2025-03-11 10:53:10 +08:00
scraed	de01bc07d7	Update README.md	2025-03-11 10:49:02 +08:00
scraed	492114f14c	Update README.md	2025-03-11 09:39:26 +08:00
charrywhite	47f4fea033	Update README.md	2025-03-11 09:24:02 +08:00
scraed	3dd2c28db3	Update README.md	2025-03-11 01:43:51 +08:00
scraed	f58cfeb0ca	Update README.md	2025-03-11 01:39:21 +08:00
scraed	df43f9f078	Update README.md	2025-03-11 01:13:51 +08:00
scraed	3eebbf9ffd	Update pyproject.toml	2025-03-11 01:09:45 +08:00
scraed	3fcf3e11ac	Update nodes.py	2025-03-11 01:08:40 +08:00
scraed	a9aff269a7	Update README.md	2025-03-11 01:06:16 +08:00
scraed	22cce34f79	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-03-11 01:00:36 +08:00
scraed	95e566ca23	update nodes image	2025-03-11 01:00:09 +08:00
scraed	bd318e4666	Update README.md	2025-03-11 00:52:47 +08:00
scraed	733cbc704f	Update README.md	2025-03-11 00:49:56 +08:00
charrywhite	96ef5e4926	Update README.md add more description for lanpaint	2025-03-11 00:46:04 +08:00
scraed	bf554c1170	Merge branch 'master' of https://github.com/scraed/LanPaint	2025-03-11 00:45:26 +08:00
scraed	3dc4adeade	update example 6 picture	2025-03-11 00:45:06 +08:00
scraed	dd41680dd7	Update README.md	2025-03-11 00:40:21 +08:00
scraed	3fb85ab403	major update node functions and examples	2025-03-11 00:36:46 +08:00
snomiao	34d36e3c93	chore(publish): update workflow for node publishing - Add permissions for issue writing in the workflow - Set condition to run job only for 'scraed' repository owner - Update action version from 'main' to 'v1' for stability and consistency	2025-03-04 04:12:56 +00:00