update version

Updated the outer_sample and inner_sample method calls in CFGGuider_LanPaint to accept and forward arbitrary keyword arguments (**kwargs). This allows for greater flexibility and compatibility with future changes or additional parameters.

.gitignore

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

.vscode/settings.json

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

README.md

@@ -36,9 +36,11 @@ Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image
 - [How to Use Examples](#how-to-use-examples)
 - [Video Examples (Beta)](#video-examples-beta)
   - [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
+  - [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
   - [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
   - [Resource Consumption](#resource-consumption)
 - [Image Examples](#image-examples)
+  - [Z-image](#example-z-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
   - [Hunyuan T2I](#example-hunyuan-t2i-inpaintlanpaint-k-sampler-5-steps-of-thinking)
   - [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
   - [Wan 2.2 T2I with reference](#example-wan22-partial-inpaintlanpaint-k-sampler-5-steps-of-thinking)
@@ -53,7 +55,8 @@ Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image
   - [Basic Sampler](#basic-sampler)
   - [Advanced Sampler](#lanpaint-ksampler-advanced)
   - [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
-- [Community Showcase](#community-showcase-) 
+- [Community Showcase](#community-showcase-)
+- [FAQ](#faq)
 - [Updates](#updates)
 - [ToDo](#todo)
 - [Citation](#citation)
@@ -114,6 +117,10 @@ LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly
 
 You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
 
+### Wan 2.2 5B Video Inpainting 
+
+Similar to Wan 2.2 14B with slightly different workflow. [View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_17)
+
 ### Wan 2.2 Video Outpainting
 
 Extend your videos beyond their original boundaries with LanPaint's video outpainting capability based on Wan 2.2. This feature allows you to expand the canvas of your videos while maintaining coherent motion and context.
@@ -212,6 +219,22 @@ We are excited to announce that LanPaint now supports Wan2.2 text to image gener
 
 You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
 
+### Example Z-image: InPaint(LanPaint K Sampler, 5 steps of thinking)
+LanPaint also supports inpainting with the Z-image text-to-image model.
+
+<details open>
+<summary>View Original / Masked / Inpainted Comparison</summary>
+
+| Original | Masked | Inpainted |
+|:--------:|:------:|:---------:|
+| ![Original Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Original_No_Mask.png) | ![Masked Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/Masked_Load_Me_in_Loader.png) | ![Inpainted Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_21/InPainted_Drag_Me_to_ComfyUI.png) |
+
+</details>
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_21)
+
+You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
+
 ### Example Wan2.2: Partial InPaint(LanPaint K Sampler, 5 steps of thinking)
 Sometimes we don't want to inpaint completely new content, but rather let the inpainted image reference the original image. One option to achieve this is to inpaint with an edit model like Qwen Image Edit. Another option is to perform a partial inpaint: allowing the diffusion process to start at some middle steps rather than from 0.
 
@@ -221,6 +244,7 @@ Sometimes we don't want to inpaint completely new content, but rather let the in
 
 You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
 
+
 ### Example Qwen Edit 2509: InPaint
 Check our latest updated [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14) for Qwen Image Edit 2509. Download the model at [Qwen Image Edit 2509 Comfy](https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models).
 
@@ -379,6 +403,9 @@ Discover how the community is using LanPaint! Here are some user-created tutoria
 
 Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
 
+## FAQ
+[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
+
 ## Updates
 - 2025/08/08
     - Add Qwen image support

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "LanPaint"
-version = "1.4.1"
+version = "1.4.5"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
   {name = "LanPaint", email = "czhengac@connect.ust.hk"}

src/LanPaint/lanpaint.py

@@ -13,6 +13,7 @@ class LanPaint():
         self.friction = Friction
         self.chara_beta = Beta
         self.img_dim_size = None
+
     def add_none_dims(self, array):
         # Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
         index = (slice(None),) + (None,) * (self.img_dim_size-1)
@@ -42,6 +43,8 @@ class LanPaint():
             return self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
 
         x = x * (1 - latent_mask) +  scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
+        
+
 
         if IS_FLUX or IS_FLOW:
             x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )

src/LanPaint/nodes.py

@@ -11,7 +11,7 @@ from comfy.samplers import *
 from comfy.model_base import ModelType
 from .utils import *
 from .lanpaint import LanPaint
-
+from comfy.model_base import WAN22
 
 def reshape_mask(input_mask, output_shape,video_inpainting=False):
     dims = len(output_shape) - 2
@@ -80,11 +80,15 @@ def sampling_function_LanPaint(model, x, timestep, uncond, cond, cond_scale, con
 
 
 class CFGGuider_LanPaint:
-    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, **kwargs):
         print("CFGGuider outer_sample")
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
+        if isinstance(self.inner_model, WAN22):
+            print("WAN22 detected")
+            self.inner_model.extra_conds = super(WAN22, self.inner_model).extra_conds
+
         if denoise_mask is not None:
             video_inpainting = self.model_options.get("video_inpainting", False)
             denoise_mask = prepare_mask(denoise_mask, noise.shape, device, video_inpainting)
@@ -96,7 +100,7 @@ class CFGGuider_LanPaint:
 
         try:
             self.model_patcher.pre_run()
-            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, **kwargs)
         finally:
             self.model_patcher.cleanup()
 
@@ -125,8 +129,9 @@ class KSamplerX0Inpaint:
 
         IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX
         IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW
-        print("model type", self.inner_model.inner_model.model_type, "IS_FLUX", IS_FLUX, "IS_FLOW", IS_FLOW)
-        print("sigma", torch.mean(sigma).item(), torch.min(sigma).item(), torch.max(sigma).item())
+        #print("model class", type(self.inner_model.inner_model))
+        #print("model type", self.inner_model.inner_model.model_type, "IS_FLUX", IS_FLUX, "IS_FLOW", IS_FLOW)
+        #print("sigma", torch.mean(sigma).item(), torch.min(sigma).item(), torch.max(sigma).item())
         # unify the notations into variance exploding diffusion model
         if IS_FLUX or IS_FLOW:
             Flow_t = sigma