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@@ -4,10 +4,12 @@
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||||
[](https://arxiv.org/abs/2502.03491)
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||||
[](https://github.com/scraed/LanPaintBench)
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||||
[](https://github.com/comfyanonymous/ComfyUI)
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||||
[](https://huggingface.co/charrywhite/LanPaint)
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||||
[](https://scraed.github.io/scraedBlog/)
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||||
[](https://github.com/scraed/LanPaint/stargazers)
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||||
</div>
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||||
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||||
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||||
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.
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||||
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).
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||||
@@ -24,6 +26,7 @@ Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/maste
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- [Quickstart](#quickstart)
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||||
- [How to Use Examples](#how-to-use-examples)
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||||
- [Examples](#examples)
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||||
- [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
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||||
- [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
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||||
- [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
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||||
- [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
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||||
@@ -78,8 +81,17 @@ Once installed, you'll find the LanPaint nodes under the "sampling" category in
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## Examples
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### Example Wan2.2: InPaint(LanPaint K Sampler, 5 steps of thinking)
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We are excited to announce that LanPaint now supports Wan2.2 text to image generation with Wan2.2 T2V model.
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[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_15)
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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.
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||||
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||||
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### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
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We are excited to announce that LanPaint now supports Qwen Image, providing powerful inpainting capabilities for image editing.
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[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_11)
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@@ -198,7 +210,7 @@ For challenging inpainting tasks:
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Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
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2️⃣ **Boost Speed**
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If you want better results but still need fewer steps, consider:
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Decrease **LanPaint_NumSteps** to accelerate generation! If you want better results but still need fewer steps, consider:
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- **Increasing LanPaint_StepSize** to speed up the thinking process.
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- **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster.
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||||
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||||
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"flags": {}
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"flags": {}
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"flags": {}
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"font_size": 24,
|
||||
"flags": {}
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"title": "Paste back the original to preserve it exactly, if you want",
|
||||
"bounding": [
|
||||
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||||
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],
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"config": {},
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||||
"extra": {
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||||
"ds": {
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"scale": 0.6209213230591556,
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||||
]
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||||
},
|
||||
"frontendVersion": "1.25.10",
|
||||
"node_versions": {
|
||||
"comfy-core": "0.3.18",
|
||||
"LanPaint": "0f509469ed2cd60c6032f739e282aad5dfc06166"
|
||||
}
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},
|
||||
"version": 0.4
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||||
}
|
||||
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|
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|
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|
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|
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|
After Width: | Height: | Size: 1.4 MiB |
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|
After Width: | Height: | Size: 1.6 MiB |
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|
After Width: | Height: | Size: 1.6 MiB |
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|
After Width: | Height: | Size: 301 KiB |
+1
-1
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "LanPaint"
|
||||
version = "1.3.0"
|
||||
version = "1.3.2"
|
||||
description = "Achieve seamless inpainting results without needing a specialized inpainting model."
|
||||
authors = [
|
||||
{name = "LanPaint", email = "czhengac@connect.ust.hk"}
|
||||
|
||||
@@ -75,8 +75,8 @@ class KSamplerX0Inpaint:
|
||||
def __init__(self, model, sigmas):
|
||||
self.inner_model = model
|
||||
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 )
|
||||
#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,**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
|
||||
|
||||
Reference in New Issue
Block a user