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@@ -1,29 +1,57 @@
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||||
# LanPaint (Thinking mode Inpaint)
|
||||
<div align="center">
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||||
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||||
Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, aiming for seamless and accurate results.
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||||
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||||
# LanPaint: Universal Inpainting Sampler with "Think Mode"
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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)
|
||||
[](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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||||
|
||||
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.
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||||
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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).
|
||||
|
||||
|
||||
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.
|
||||
|
||||
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||||
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||||
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)
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||||
- [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [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)
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||||
- [Basic Sampler](#basic-sampler)
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||||
- [Advanced Sampler](#lanpaint-ksampler-advanced)
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||||
- [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.
|
||||
|
||||
- **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 +69,50 @@ 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 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.
|
||||
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_15)
|
||||
|
||||
|
||||
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 Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
|
||||
|
||||
[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)
|
||||
|
||||
[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)
|
||||
.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)
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
|
||||
@@ -115,23 +161,6 @@ 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!
|
||||
|
||||
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
**Workflow Setup**
|
||||
@@ -166,20 +195,22 @@ 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:
|
||||
Decrease **LanPaint_NumSteps** to accelerate generation! If you want better results but still need fewer steps, consider:
|
||||
- **Increasing LanPaint_StepSize** to speed up the thinking process.
|
||||
- **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster.
|
||||
|
||||
@@ -192,14 +223,45 @@ 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
|
||||
|
||||
|
||||
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|
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@@ -0,0 +1,806 @@
|
||||
{
|
||||
"id": "11cce4ab-536b-4f42-a95c-0be437d04ace",
|
||||
"revision": 0,
|
||||
"last_node_id": 128,
|
||||
"last_link_id": 338,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 74,
|
||||
"type": "LanPaint_KSampler",
|
||||
"pos": [
|
||||
276.219970703125,
|
||||
179.55892944335938
|
||||
],
|
||||
"size": [
|
||||
388.97625732421875,
|
||||
572
|
||||
],
|
||||
"flags": {},
|
||||
"order": 10,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 325
|
||||
},
|
||||
{
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"link": 323
|
||||
},
|
||||
{
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
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"bounding": [
|
||||
1491.586181640625,
|
||||
984.3547973632812,
|
||||
749.66455078125,
|
||||
637.360595703125
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
"flags": {}
|
||||
}
|
||||
],
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 0.6209213230591556,
|
||||
"offset": [
|
||||
850.0803535605011,
|
||||
86.6432096141053
|
||||
]
|
||||
},
|
||||
"frontendVersion": "1.25.10",
|
||||
"node_versions": {
|
||||
"comfy-core": "0.3.18",
|
||||
"LanPaint": "0f509469ed2cd60c6032f739e282aad5dfc06166"
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
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+1
-1
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "LanPaint"
|
||||
version = "1.0.1"
|
||||
version = "1.3.2"
|
||||
description = "Achieve seamless inpainting results without needing a specialized inpainting model."
|
||||
authors = [
|
||||
{name = "LanPaint", email = "czhengac@connect.ust.hk"}
|
||||
|
||||
+51
-23
@@ -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)
|
||||
|
||||
+234
-11
@@ -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])
|
||||
@@ -77,8 +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
|
||||
@@ -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"
|
||||
}
|
||||
|
||||
+20
-7
@@ -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]
|
||||
Reference in New Issue
Block a user