Update pyproject.toml

Update Github Action for Publishing to Comfy Registry

.github/workflows/publish_action.yml

@@ -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.

README.md

@@ -21,6 +21,8 @@ LanPaint introduces **two-way alignment** between masked and unmasked areas. It
 Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions.  
   
 ## Updates
+- 2025/04/16
+    - Added Primary HiDream support
 - 2025/03/22
     - Added Primary Flux support
     - Added Tease Mode
@@ -30,6 +32,19 @@ Based on this evaluation, LanPaint iteratively updates the noise in both the mas
 ## Example Results
 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)
+![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 install [ComfyUI GGUF](https://github.com/city96/ComfyUI-GGUF) in order to load the models. Make sure you have the latest (nightly at 2025/04/16) comfyui installed. The following models are needed for Hidream:
+- [clip_g_hidream.safetensors](https://huggingface.co/Comfy-Org/HiDream-I1_ComfyUI/blob/main/split_files/text_encoders/clip_g_hidream.safetensors)
+- [clip_l_hidream.safetensors](https://huggingface.co/Comfy-Org/HiDream-I1_ComfyUI/blob/main/split_files/text_encoders/clip_l_hidream.safetensors)
+- [T5 GGUF](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf/tree/main)
+- [Llama 3.1](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/tree/main)
+- [Flux VAE](https://huggingface.co/StableDiffusionVN/Flux/blob/main/Vae/flux_vae.safetensors)
+
+
 ### Example 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) 
@@ -67,7 +82,10 @@ All examples use a random seed 0 to generate batch of 4 images for fair comparis
 [Model Used in This Example](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors) 
 (Note: Use CFG scale 1.0 for Flux as it don't use CFG. LanPaint_cfg_BIG is also disabled on Flux)
 
-**How to Use These Examples:**  
+
+
+
+## **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**.  
@@ -166,6 +184,19 @@ For challenging inpainting tasks:
 
 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}, 
+}
+```
 
 
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "LanPaint"
-version = "0.2.0"
+version = "0.2.2"
 description = "Achieve seamless inpainting results without needing a specialized inpainting model."
 authors = [
   {name = "LanPaint", email = "czhengac@connect.ust.hk"}

src/LanPaint/nodes.py

@@ -85,9 +85,10 @@ class KSamplerX0Inpaint:
         # 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:
+        if IS_FLUX or IS_FLOW:
             LanPaint_Sigma = sigma / ( torch.maximum( 1 - sigma , sigma*0 + 5e-2  ))
             self.LanPaint_Sigmas = self.sigmas / ( torch.maximum( 1 - self.sigmas , self.sigmas*0 + 5e-2  ))
         else:
@@ -130,7 +131,7 @@ class KSamplerX0Inpaint:
             # 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
             
-            if IS_FLUX:
+            if IS_FLUX or IS_FLOW:
                 x_t = x * ( 1 + LanPaint_Sigma[:, None,None,None])
             else:
                 x_t = x #/ ( 1+sigma**2 )**0.5 # switch to variance perserving x_t values
@@ -149,7 +150,7 @@ class KSamplerX0Inpaint:
                 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)[:, None,None,None], sigma_y = self.sigma_y(abt)[:, None,None,None], args = args)  
-            if IS_FLUX:
+            if IS_FLUX or IS_FLOW:
                 x = x_t / ( 1 + LanPaint_Sigma[:, None,None,None] )
             else:
                 x = x_t #/ ( 1+sigma**2 )**0.5 # switch to variance perserving x_t values
@@ -190,7 +191,8 @@ class KSamplerX0Inpaint:
         beta = self.chara_beta * (1-abt)**0.5
 
         IS_FLUX = self.inner_model.inner_model.model_type == ModelType.FLUX
-        if IS_FLUX:
+        IS_FLOW = self.inner_model.inner_model.model_type == ModelType.FLOW
+        if IS_FLUX or IS_FLOW:
             x_0, x_0_BIG = self.inner_model(x_t / ( 1 + sigma ), sigma[:, 0,0,0] / ( 1 + sigma[:, 0,0,0] ), model_options=model_options, seed=seed)
         else:
             x_0, x_0_BIG = self.inner_model(x_t, sigma[:, 0,0,0], model_options=model_options, seed=seed)