Merge branch 'master' of https://github.com/scraed/LanPaint

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

@@ -13,6 +13,12 @@
 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), accepted by TMLR. The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
+**🎬 NEW: LanPaint now supports inpainting and outpainting based on Z-Image!**
+
+| 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) |
+
 
 **🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
 
@@ -36,9 +42,12 @@ 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)
   - [Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint)
@@ -52,7 +61,8 @@ 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)
@@ -113,6 +123,10 @@ 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.
@@ -187,13 +201,21 @@ You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.com
 </table>
 
 <sub>**Test Platform**: All tests were conducted on an NVIDIA RTX Pro 6000.<br>
-**Model Used**: `wan2.2_t2v_low_noise_14B_fp18_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp18_scaled.safetensors`.<br>
+**Model Used**: `wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors` and `wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors`.<br>
 **Processing Steps**:  20 sampling steps x 2 (LanPaint steps of thinking).</sub>
 
-**Note:** To further reduce VRAM requirements, we recommend loading CLIP on CPU.
+**Note:** Vram is required by the model, not LanPaint. To further reduce VRAM requirements, we recommend generating less frames and loading CLIP on CPU.
 
 ## Image Examples
 
+### Example Hunyuan T2I: InPaint(LanPaint K Sampler, 5 steps of thinking)
+We are excited to announce that LanPaint now supports inpainting with Hunyuan text to image generation.
+
+[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_20)
+
+
+You need to follow the ComfyUI version of [Hunyuan workflow](https://docs.comfy.org/tutorials/video/hunyuan-video#hunyuan-text-to-video-workflow) to download and install the model.
+
 ### Example Wan2.2: InPaint(LanPaint K Sampler, 5 steps of thinking)
 We are excited to announce that LanPaint now supports Wan2.2 text to image generation with Wan2.2 T2V model.
 
@@ -203,6 +225,33 @@ 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)
+
+<details open>
+<summary>View Z-image Outpainting (Original / Masked / Outpainted)</summary>
+
+| Original | Masked | Outpainted |
+|:--------:|:------:|:----------:|
+| ![Original Z-image Outpaint](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/Original_No_Mask.png) | ![Masked Z-image Outpaint](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/Masked_Load_Me_in_Loader.png) | ![Outpainted Z-image](https://github.com/scraed/LanPaint/blob/master/examples/Example_22/InPainted_Drag_Me_to_ComfyUI.png) |
+
+</details>
+
+[View Outpaint Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_22)
+
+You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
+
 ### Example 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.
 
@@ -212,6 +261,7 @@ 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).
 
@@ -370,6 +420,9 @@ 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

example_workflows/Flux_Inpaint.json

@@ -0,0 +1,786 @@
+{
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+        }
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+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
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+      "title": "CLIP Text Encode (Positive Prompt)",
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+      ],
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+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
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+        "ver": "0.3.26",
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+      "color": "#322",
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+          "name": "conditioning",
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+        }
+      ],
+      "outputs": [
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+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
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+            207
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+          "name": "MODEL",
+          "type": "MODEL",
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+          "type": "LATENT",
+          "slot_index": 0,
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+        30,
+        1,
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+        1,
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+        "🖼️ Image Inpainting"
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+      196,
+      77,
+      1,
+      80,
+      0,
+      "CLIP"
+    ],
+    [
+      197,
+      77,
+      0,
+      73,
+      0,
+      "MODEL"
+    ],
+    [
+      200,
+      77,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      203,
+      77,
+      2,
+      65,
+      1,
+      "VAE"
+    ],
+    [
+      207,
+      79,
+      0,
+      73,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      210,
+      80,
+      0,
+      73,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      216,
+      65,
+      0,
+      66,
+      0,
+      "LATENT"
+    ],
+    [
+      217,
+      81,
+      0,
+      82,
+      0,
+      "IMAGE"
+    ],
+    [
+      218,
+      8,
+      0,
+      81,
+      1,
+      "IMAGE"
+    ],
+    [
+      219,
+      75,
+      0,
+      81,
+      0,
+      "IMAGE"
+    ],
+    [
+      220,
+      75,
+      1,
+      81,
+      2,
+      "MASK"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Mask image for inpainting.",
+      "bounding": [
+        36.04227828979492,
+        989.7313232421875,
+        278.89093017578125,
+        669.3414916992188
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "Convert Latents for LanPaint",
+      "bounding": [
+        286.0640563964844,
+        714.343505859375,
+        489.16796875,
+        197.81044006347656
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 3,
+      "title": "Load Model and Set Prompts",
+      "bounding": [
+        -78.9311294555664,
+        176.08712768554688,
+        1006.1721801757812,
+        514.258544921875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 4,
+      "title": "Inpaint with the LanPaint KSampler",
+      "bounding": [
+        960.8922729492188,
+        179.17588806152344,
+        474.8909606933594,
+        630.4742431640625
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 5,
+      "title": "LanPaint OutPut",
+      "bounding": [
+        1085.6029052734375,
+        994.0775756835938,
+        345.4561767578125,
+        669.4969482421875
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 11,
+      "title": "LanPaint",
+      "bounding": [
+        -262.59381103515625,
+        140.46656799316406,
+        1737.328857421875,
+        797.4443359375
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.35049389948139237,
+      "offset": [
+        348.866804381099,
+        308.65057628971834
+      ]
+    },
+    "frontendVersion": "1.27.10",
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "LanPaint": "0f509469ed2cd60c6032f739e282aad5dfc06166"
+    }
+  },
+  "version": 0.4
+}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "LanPaint"
-version = "1.4.0"
+version = "1.4.6"
 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)
@@ -38,8 +39,13 @@ class LanPaint():
         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
+        def scale_latent_inpaint(x, sigma, noise, latent_image):
+            return self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
+
+        x = x * (1 - latent_mask) +  scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
         
+
+
         if IS_FLUX or IS_FLOW:
             x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
         else:

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()
 
@@ -125,7 +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 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
@@ -297,7 +303,7 @@ 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="",Inpainting_mode="🖼️ Image Inpainting"):
 
-        model.LanPaint_StepSize = 0.15
+        model.LanPaint_StepSize = 0.2
         model.LanPaint_Lambda = 16.0
         model.LanPaint_Beta = 1.
         model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -335,7 +341,7 @@ class LanPaint_KSamplerAdvanced:
                     "return_with_leftover_noise": (["disable", "enable"], ),
                 "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
                 "LanPaint_Lambda": ("FLOAT", {"default": 16., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
-                "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "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_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
                 "LanPaint_Beta": ("FLOAT", {"default": 1., "min": 0.0001, "max": 5, "step": 0.1, "round": 0.1, "tooltip": "The step size ratio between masked / unmasked regions. Lower value can compensate high values of LanPaint_Lambda."}),
                 "LanPaint_Friction": ("FLOAT", {"default": 15, "min": 0., "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The friction parameter for fast langevin, lower values result in faster convergence but may be unstable."}),
                 "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: emphasis image quality, Prompt First: emphasis prompt following"}),
@@ -350,7 +356,7 @@ 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, LanPaint_NumSteps=5, LanPaint_Lambda=16.0, LanPaint_StepSize=0.15, LanPaint_Beta=1.0, LanPaint_Friction=15.0, LanPaint_PromptMode="Image First", LanPaint_EarlyStop=1, LanPaint_Info="", Inpainting_mode="🖼️ Image Inpainting"):
+    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, LanPaint_NumSteps=5, LanPaint_Lambda=16.0, LanPaint_StepSize=0.2, LanPaint_Beta=1.0, LanPaint_Friction=15.0, LanPaint_PromptMode="Image First", LanPaint_EarlyStop=1, LanPaint_Info="", Inpainting_mode="🖼️ Image Inpainting"):
         force_full_denoise = True
         if return_with_leftover_noise == "enable":
             force_full_denoise = False
@@ -403,8 +409,16 @@ class MaskBlend:
         # 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.")
-        
+            raise ValueError(
+                "Image size mismatch: Image1 and Image2 must have the same dimensions.\n"
+                "Additionally, ensure both images have width and height that are multiples of 8.\n"
+                "This is required because VAE decode always generates images with dimensions that are multiples of 8.\n"
+                "If your input images are not multiples of 8, a size mismatch will occur during the decoding process.\n"
+                "Please resize your images using an image resize node to ensure compatibility.\n"
+                "Current sizes - Image1: {}x{}, Image2: {}x{}".format(
+                    image1.shape[2], image1.shape[1], image2.shape[2], image2.shape[1]
+                )
+            )
         mask = mask.float()
         mask = torch.nn.functional.max_pool2d(mask, kernel_size=blend_overlap, stride=1, padding=blend_overlap//2)
         # apply Gaussian blur with kernel size blend_overlap
@@ -470,7 +484,7 @@ class LanPaint_SamplerCustom:
     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_StepSize = 0.2
         model.LanPaint_Lambda = 16.0
         model.LanPaint_Beta = 1.
         model.LanPaint_NumSteps = LanPaint_NumSteps
@@ -515,16 +529,13 @@ class LanPaint_SamplerCustomAdvanced:
     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"], ),
+                    "guider": ("GUIDER", ),
+                    "sampler": ("SAMPLER", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),
                      "LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "Number of steps for Langevin dynamics, representing turns of thinking per step."}),
                      "LanPaint_Lambda": ("FLOAT", {"default": 16.0, "min": 0.1, "max": 50.0, "step": 0.1, "tooltip": "Bidirectional guidance scale. Higher values align with known regions but may cause instability."}),
-                     "LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
+                     "LanPaint_StepSize": ("FLOAT", {"default": 0.2, "min": 0.0001, "max": 1.0, "step": 0.01, "tooltip": "Step size for Langevin dynamics. Higher values speed convergence but may be unstable."}),
                      "LanPaint_Beta": ("FLOAT", {"default": 1.0, "min": 0.0001, "max": 5.0, "step": 0.1, "tooltip": "Step size ratio between masked/unmasked regions. Lower values balance high Lambda."}),
                      "LanPaint_Friction": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Friction parameter for fast Langevin. Lower values speed convergence but may be unstable."}),
                      "LanPaint_PromptMode": (["Image First", "Prompt First"], {"tooltip": "Image First: prioritizes image quality; Prompt First: prioritizes prompt adherence."}),
@@ -533,17 +544,14 @@ class LanPaint_SamplerCustomAdvanced:
                     }
                }
 
-    RETURN_TYPES = ("LATENT", "LATENT")
+    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
+    def sample(self, noise, guider, sampler, sigmas, latent_image, LanPaint_NumSteps, LanPaint_Lambda, LanPaint_StepSize, LanPaint_Beta, LanPaint_Friction, LanPaint_PromptMode, LanPaint_EarlyStop, LanPaint_Info=""):
         model = guider.model_patcher
         model.LanPaint_StepSize = LanPaint_StepSize
         model.LanPaint_Lambda = LanPaint_Lambda
@@ -556,47 +564,33 @@ class LanPaint_SamplerCustomAdvanced:
         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
+            latent = latent_image
+            latent_image = latent["samples"]
+            latent = latent.copy()
+            latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+            latent["samples"] = latent_image
 
             noise_mask = None
             if "noise_mask" in latent:
                 noise_mask = latent["noise_mask"]
 
-            # 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)
+            callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
             disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
-
-            samples = guider.sample( noise.generate_noise(latent), latent_image_samples, sampler, sigmas, denoise_mask=noise_mask, callback=callback,disable_pbar=disable_pbar, seed=noise.seed
-            )
-
+            samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
             samples = samples.to(comfy.model_management.intermediate_device())
+
             out = latent.copy()
             out["samples"] = samples
             if "x0" in x0_output:
                 out_denoised = latent.copy()
-                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+                out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
             else:
                 out_denoised = out
             return (out, out_denoised)
 
+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {