Bump version to 1.3.0 in pyproject.toml

Updated the project version from 1.2.0 to 1.3.0 to reflect new changes or improvements in the LanPaint package.

README.md

@@ -1,29 +1,54 @@
-# LanPaint (Thinking mode Inpaint)
+<div align="center">
 
-Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, aiming for seamless and accurate results. 
-![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
+# LanPaint: Universal Inpainting Sampler with "Think Mode"
+[![arXiv](https://img.shields.io/badge/Arxiv-2502.03491-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2502.03491)
+[![Python Benchmark](https://img.shields.io/badge/🐍-Python_Benchmark-3776AB?logo=python)](https://github.com/scraed/LanPaintBench)
+[![ComfyUI Extension](https://img.shields.io/badge/ComfyUI-Extension-7B5DFF)](https://github.com/comfyanonymous/ComfyUI)
+[![Blog](https://img.shields.io/badge/📝-Blog-9cf)](https://scraed.github.io/scraedBlog/)
+[![GitHub stars](https://img.shields.io/github/stars/scraed/LanPaint)](https://github.com/scraed/LanPaint/stargazers)
+</div>
 
-This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). 
+Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.  
+
+This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+
+![Qwen Result 2](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_03.jpg) 
+Check [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14). You need to follow the ComfyUI version of [Qwen Image Edit workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) to download and install the model.
+
+![Qwen Result 1](https://github.com/scraed/LanPaint/blob/master/examples/LanPaintQwen_01.jpg) 
+
+Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_13) and [Qwen Outpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_12). You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
+
+## Table of Contents
+- [Features](#features)
+- [Quickstart](#quickstart)
+- [How to Use Examples](#how-to-use-examples)
+- [Examples](#examples)
+  - [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
+  - [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [Flux](#example-flux-inpaintlanpaint-k-sampler-5-steps-of-thinking)
+  - [SDXL Examples](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
+- [Usage](#usage)
+  - [Basic Sampler](#basic-sampler)
+  - [Advanced Sampler](#lanpaint-ksampler-advanced)
+  - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
+- [Community Showcase](#community-showcase-) 
+- [Updates](#updates)
+- [ToDo](#todo)
+- [Citation](#citation)
 
 ## Features
 
-- **Universal Compatibility** – Works instantly with almost any model (SD 1.5, XL, 3.5, Flux, HiDream, or custom LoRAs) and ControlNet.  
+- **Universal Compatibility** – Works instantly with almost any model (**SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image or custom LoRAs**) and ControlNet.  
+![Inpainting Result 13](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13.jpg) 
 - **No Training Needed** – Works out of the box with your existing model.  
 - **Easy to Use** – Same workflow as standard ComfyUI KSampler.  
 - **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.  
 - **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.  
 - **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters. 
 
-## How It Works  
-LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results.
-
-LanPaint introduces "BIG score" that creates a **two-way alignment** between masked and unmasked areas. It continuously evaluates:  
-- *"Does the new content make sense with the existing elements?"*  
-- *"Do the existing elements support the new creation?"*  
-Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions.  
-  
-LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.
-
+**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
 
 ## Quickstart
 
@@ -41,32 +66,41 @@ LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.
 Once installed, you'll find the LanPaint nodes under the "sampling" category in ComfyUI. Use them just like the default KSampler for high-quality inpainting!
 
 
-
-## Updates
-- 2025/06/04
-    - Add more sampler support.
-    - Add early stopping to advanced sampler.
-- 2025/05/28
-    - Major update on the Langevin solver. It is now much faster and more stable.
-    - Greatly simplified the parameters for advanced sampler.
-    - Fix performance issue on Flux and SD 3.5
-- 2025/04/16
-    - Added Primary HiDream support
-- 2025/03/22
-    - Added Primary Flux support
-    - Added Tease Mode
-- 2025/03/10
-    - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
+## **How to Use Examples:**  
+1. Navigate to the **example** folder (i.e example_1), download all pictures.  
+2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
+3. Download the required model (i.e clicking **Model Used in This Example**).  
+4. Load the model in ComfyUI.
+5. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left), or the **Prepare Image** node.  
+7. Queue the task, you will get inpainted results from LanPaint. Some example also gives you inpainted results from the following methods for comparison:
+   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)**
+   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)**
 
 ## Examples
-All examples use a random seed 0 to generate batch of 4 images for fair comparison. (Warning: Generating 4 images may exceed your GPU memory; adjust batch size as needed.)
 
-### Example HiDream: InPaint(LanPaint K Sampler, 5 steps of thinking)
+### 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)
@@ -115,23 +149,6 @@ You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki
 Check more for use cases like inpaint on [fine tuned models](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938662021) and [face swapping](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938723501), thanks to [Amazon90](https://github.com/Amazon90).
 
 
-## **How to Use These Examples:**  
-1. Navigate to the **example** folder (i.e example_1) by clicking **View Workflow & Masks**, download all pictures.  
-2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.  
-3. Download the required model from Civitai by clicking **Model Used in This Example**.  
-4. Load the model into the **"Load Checkpoint"** node.  
-5. Upload **Original_No_Mask.png** to the **"Load image"** node in the **"Original Image"** group (far left).  
-6. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left).  
-7. Queue the task, you will get inpainted results from three methods:  
-   - **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)** (middle),  
-   - **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)** (second from right),  
-   - **LanPaint** (far right).  
-
-Compare and explore the results from each method!
-
-![WorkFlow](https://github.com/scraed/LanPaint/blob/master/Example.JPG)  
-
-
 ## Usage
 
 **Workflow Setup**  
@@ -166,17 +183,19 @@ Full parameter control:
 | `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. |
 | `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 |
 | `LanPaint_Friction` | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0|
+| `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5|
 | `LanPaint_PromptMode` | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. |
 
 For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.
 
-
+### LanPaint Mask Blend
+This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly.
 
 ## LanPaint KSampler (Advanced) Tuning Guide
 For challenging inpainting tasks:  
 
 1️⃣ **Boost Quality**
-Increase total number of sampling steps, **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
+Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
   
 2️⃣ **Boost Speed**
 If you want better results but still need fewer steps, consider:
@@ -192,14 +211,45 @@ If you find the results have wired texture, try
 ⚠️ **Notes**:  
 - For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting.  Better to do it with a batche of images to avoid overfitting on a single image.
 
+## Community Showcase [](#community-showcase-)
+
+Discover how the community is using LanPaint! Here are some user-created tutorials:
+
+- [Ai绘画进阶148-三大王炸！庆祝高允贞出道6周年！T8即将直播？当AI绘画学会深度思考？！万能修复神器LanPaint，万物皆可修！-T8 Comfyui教程](https://www.youtube.com/watch?v=Z4DSTv3UPJo)
+- [Ai绘画进阶151-真相了！T8竟是个AI？！LanPaint进阶（二），人物一致性，多视角实验性测试，新参数讲解，工作流分享-T8 Comfyui教程](https://www.youtube.com/watch?v=landiRhvF3k)
+- [重绘和三视图角色一致性解决新方案！LanPaint节点尝试](https://www.youtube.com/watch?v=X0WbXdm6FA0)
+- [ComfyUI: HiDream with Perturbation Upscale, LanPaint Inpainting (Workflow Tutorial)](https://www.youtube.com/watch?v=2-mGe4QVIIw&t=2785s)
+- [ComfyUI必备LanPaint插件超详细使用教程](https://plugin.aix.ink/archives/lanpaint)
+
+Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
+
+## Updates
+- 2025/08/08
+    - Add Qwen image support
+- 2025/06/21
+    - Update the algorithm with enhanced stability and outpaint performance.
+    - Add outpaint example
+    - Supports Sampler Custom (Thanks to [MINENEMA](https://github.com/MINENEMA))
+- 2025/06/04
+    - Add more sampler support.
+    - Add early stopping to advanced sampler.
+- 2025/05/28
+    - Major update on the Langevin solver. It is now much faster and more stable.
+    - Greatly simplified the parameters for advanced sampler.
+    - Fix performance issue on Flux and SD 3.5
+- 2025/04/16
+    - Added Primary HiDream support
+- 2025/03/22
+    - Added Primary Flux support
+    - Added Tease Mode
+- 2025/03/10
+    - LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
+- 2025/03/06:
+    - Bug Fix for str not callable error and unpack error. Big thanks to [jamesWalker55](https://github.com/jamesWalker55) and [EricBCoding](https://github.com/EricBCoding).
+
 ## ToDo
 - Try Implement Detailer
-- Provide inference code on without GUI.
-
-## Contribute
-
-- 2025/03/06: Bug Fix for str not callable error and unpack error. Big thanks to [jamesWalker55](https://github.com/jamesWalker55) and [EricBCoding](https://github.com/EricBCoding).
-
+- ~~Provide inference code on without GUI.~~ Check our local Python benchmark code [LanPaintBench](https://github.com/scraed/LanPaintBench).
 
 ## Citation
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "LanPaint"
-version = "1.0.1"
+version = "1.3.0"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
   {name = "LanPaint", email = "czhengac@connect.ust.hk"}

src/LanPaint/lanpaint.py

@@ -1,6 +1,7 @@
 import torch
 from .utils import *
 from functools import partial
+
 class LanPaint():
     def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False):
         self.n_steps = NSteps
@@ -11,8 +12,17 @@ class LanPaint():
         self.inner_model = Model
         self.friction = Friction
         self.chara_beta = Beta
-        
+        self.img_dim_size = None
+    def add_none_dims(self, array):
+        # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+        index = (slice(None),) + (None,) * (self.img_dim_size-1)
+        return array[index]
+    def remove_none_dims(self, array):
+        # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
+        index = (slice(None),) + (0,) * (self.img_dim_size-1)
+        return array[index]
     def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None):
+        self.img_dim_size = len(x.shape)
         self.latent_image = latent_image
         self.noise = noise
         if n_steps is None:
@@ -23,26 +33,26 @@ class LanPaint():
 
         
         step_size = self.step_size * (1 - abt)
-        step_size = step_size[:, None, None, None]
+        step_size = self.add_none_dims(step_size)
         # self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
         # This is the replace step
         x = x * (1 - latent_mask) +  self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
         
         if IS_FLUX or IS_FLOW:
-            x_t = x * ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 )
+            x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
         else:
-            x_t = x / ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values
+            x_t = x / ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
 
         ############ LanPaint Iterations Start ###############
         # after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
         args = None
         for i in range(n_steps):
-            score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = abt[:, None,None,None], sigma = VE_Sigma[:, None,None,None], tflow = Flow_t[:, None,None,None], model_options = model_options, seed = seed )
-            x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.sigma_x(abt)[:, None,None,None], sigma_y = self.sigma_y(abt)[:, None,None,None], args = args)  
+            score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = self.add_none_dims(abt), sigma = self.add_none_dims(VE_Sigma), tflow = self.add_none_dims(Flow_t), model_options = model_options, seed = seed )
+            x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)  
         if IS_FLUX or IS_FLOW:
-            x = x_t / ( abt[:, None,None,None]**0.5 + (1-abt[:, None,None,None])**0.5 )
+            x = x_t / ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
         else:
-            x = x_t * ( 1+VE_Sigma[:, None,None,None]**2 )**0.5 # switch to variance perserving x_t values
+            x = x_t * ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
         ############ LanPaint Iterations End ###############
         # out is x_0
         out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
@@ -52,14 +62,13 @@ class LanPaint():
     def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):
         
         lamb = self.chara_lamb
-
         if self.IS_FLUX or self.IS_FLOW:
             # compute t for flow model, with a small epsilon compensating for numerical error.
             x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching
-            x_0, x_0_BIG = self.inner_model(x, tflow[:, 0,0,0], model_options=model_options, seed=seed)
+            x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
         else:
             x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding
-            x_0, x_0_BIG = self.inner_model(x, sigma[:, 0,0,0], model_options=model_options, seed=seed)
+            x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)
 
         score_x = -(x_t - x_0)
         score_y =  - (1 + lamb) * ( x_t - y )  + lamb * (x_t - x_0_BIG)  
@@ -82,33 +91,52 @@ class LanPaint():
         # -------------------------------------------------------------------------
         # Compute the Langevin dynamics update in variance perserving notation
         # -------------------------------------------------------------------------
-        x0 = self.x0_evalutation(x_t, score, sigma, args)
-        C = abt**0.5 * x0 / (1-abt)
+        #x0 = self.x0_evalutation(x_t, score, sigma, args)
+        #C = abt**0.5 * x0 / (1-abt)
         A = A_x * (1-mask) + A_y * mask
         D = D_x * (1-mask) + D_y * mask
         dt = dtx * (1-mask) + dty * mask
         Gamma = Gamma_x * (1-mask) + Gamma_y * mask
 
 
-
+        def Coef_C(x_t):
+            x0 = self.x0_evalutation(x_t, score, sigma, args)
+            C = (abt**0.5 * x0  - x_t )/ (1-abt) + A * x_t
+            return C
+        def advance_time(x_t, v, dt, Gamma, A, C, D):
+            dtype = x_t.dtype
+            with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
+                osc = StochasticHarmonicOscillator(Gamma, A, C, D )
+                x_t, v = osc.dynamics(x_t, v, dt )
+            x_t = x_t.to(dtype)
+            v = v.to(dtype)
+            return x_t, v
         if args is None:
             #v = torch.zeros_like(x_t)
             v = None
+            C = Coef_C(x_t)
+            #print(torch.squeeze(dtx), torch.squeeze(dty))
+            x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
         else:
-            v, = args
+            v, C = args
 
-        with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
-            osc = StochasticHarmonicOscillator(Gamma, A, C, D )
-            x_t, v = osc.dynamics(x_t, v, dt )
+            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+
+            C_new = Coef_C(x_t)
+            v = v + Gamma**0.5 * ( C_new - C) *dt
+
+            x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
+
+            C = C_new
   
-        return x_t, (v,)
+        return x_t, (v, C)
 
     def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
         # -------------------------------------------------------------------------
         # Unpack current times parameters (sigma and abt)
         sigma, abt, flow_t = current_times
-        sigma = sigma[:, None,None,None]
-        abt = abt[:, None,None,None]
+        sigma = self.add_none_dims(sigma)
+        abt = self.add_none_dims(abt)
         # Compute time step (dtx, dty) for x and y branches.
         dtx = 2 * step_size * sigma_x
         dty = 2 * step_size * sigma_y
@@ -123,9 +151,9 @@ class LanPaint():
         # adjust dt to match denoise-addnoise steps sizes
         Gamma_hat_x /= 2.
         Gamma_hat_y /= 2.
-
         A_t_x = (1) / ( 1 - abt ) * dtx / 2
-        A_t_y =  (1) / ( 1 - abt ) * dty / 2
+        A_t_y =  (1+self.chara_lamb) / ( 1 - abt ) * dty / 2
+
 
         A_x = A_t_x / (dtx/2)
         A_y = A_t_y / (dty/2)

src/LanPaint/nodes.py

@@ -11,15 +11,13 @@ from comfy.samplers import *
 from comfy.model_base import ModelType
 from .utils import *
 from .lanpaint import LanPaint
-# Monkey patch comfy.samplers module by importing with absolute package path
-#exec(inspect.getsource(comfy.samplers).replace("from .", "from comfy."))
+
 
 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])
@@ -111,10 +109,10 @@ class KSamplerX0Inpaint:
             latent_mask = 1 - denoise_mask
             current_times = (VE_Sigma, abt, Flow_t)
 
-            current_step = torch.argmin( torch.abs( self.sigmas - sigma ) )
+            current_step = torch.argmin( torch.abs( self.sigmas - torch.mean(sigma) ) )
             total_steps = len(self.sigmas)-1
 
-            if total_steps - current_step < self.LanPaint_early_stop:
+            if total_steps - current_step <= self.LanPaint_early_stop:
                 out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed, n_steps=0)
             else:
                 out = self.PaintMethod(x, self.latent_image, self.noise, sigma, latent_mask, current_times, model_options, seed)
@@ -260,8 +258,8 @@ class LanPaint_KSampler():
     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 = 8.0
-        model.LanPaint_Beta = 1.0
+        model.LanPaint_Lambda = 16.0
+        model.LanPaint_Beta = 1.
         model.LanPaint_NumSteps = LanPaint_NumSteps
         model.LanPaint_Friction = 15.
         model.LanPaint_EarlyStop = 1
@@ -289,7 +287,7 @@ class LanPaint_KSamplerAdvanced:
                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                     "return_with_leftover_noise": (["disable", "enable"], ),
                 "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
-                "LanPaint_Lambda": ("FLOAT", {"default": 8., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
+                "LanPaint_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."}),
@@ -326,11 +324,233 @@ class LanPaint_KSamplerAdvanced:
             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,
 }
 
@@ -338,5 +558,8 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "LanPaint_KSampler": "LanPaint KSampler",
     "LanPaint_KSamplerAdvanced": "LanPaint KSampler (Advanced)",
+    "LanPaint_SamplerCustom" : "LanPaint Sampler Custom",
+    "LanPaint_SamplerCustomAdvanced" : "LanPaint Sampler Custom (Advanced)",
+    "LanPaint_MaskBlend": "LanPaint Mask Blend",
 #    "LanPaint_UpSale_LatentNoiseMask": "LanPaint UpSale Latent Noise Mask"
 }

src/LanPaint/utils.py

@@ -240,7 +240,6 @@ class StochasticHarmonicOscillator:
             tuple: (y(t), v(t))
         """
         
-
         dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0
         Delta = self.Delta + dummyzero
         Gamma_hat = self.Gamma * t + dummyzero
@@ -253,8 +252,8 @@ class StochasticHarmonicOscillator:
         EE = 1 - Gamma_hat * zeta_2
 
         if v0 is None:
-            #v0 = torch.randn_like(y0) * D / 2 ** 0.5
-            v0 = (C - A * y0)/Gamma**0.5
+            v0 = torch.randn_like(y0) * D / 2 ** 0.5
+            #v0 = (C - A * y0)/Gamma**0.5
         
         # Calculate mean position and velocity
         term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t
@@ -273,16 +272,30 @@ class StochasticHarmonicOscillator:
         cov_matrix[..., 0, 1] = cov_yv
         cov_matrix[..., 1, 0] = cov_yv  # symmetric
         cov_matrix[..., 1, 1] = cov_vv
+
+
         
+        # Compute the Cholesky decomposition to get scale_tril
+        #scale_tril = torch.linalg.cholesky(cov_matrix)
+        scale_tril = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
+        tol = 1e-8
+        cov_yy = torch.clamp( cov_yy, min = tol )
+        sd_yy = torch.sqrt( cov_yy )
+        inv_sd_yy = 1/(sd_yy)
+
+        scale_tril[..., 0, 0] = sd_yy
+        scale_tril[..., 0, 1] = 0.
+        scale_tril[..., 1, 0] = cov_yv * inv_sd_yy
+        scale_tril[..., 1, 1] = torch.clamp( cov_vv - cov_yv**2 / cov_yy, min = tol ) ** 0.5
+        # check if it matches torch.linalg.
+        #assert torch.allclose(torch.linalg.cholesky(cov_matrix), scale_tril, atol = 1e-4, rtol = 1e-4 )
         # Sample correlated noise from multivariate normal
         mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype)
         mean[..., 0] = y_mean
         mean[..., 1] = v_mean
         new_yv = torch.distributions.MultivariateNormal(
             loc=mean,
-            covariance_matrix=cov_matrix
+            scale_tril=scale_tril
         ).sample()
 
-        return new_yv[...,0], new_yv[...,1]
-
-
+        return new_yv[...,0], new_yv[...,1]