Update README.md

README.md

@@ -1,3 +1,82 @@
----
-license: openrail++
----
+---
+license: openrail++
+tags:
+- art
+- stable diffusion
+- ControlNet
+- SDXL
+- Diffusion-XL
+pipeline_tag: text-to-image
+---
+# MistoLine
+ 
+## Control Every Line!
+![Intro Image](assets/intro.png)
+
+MistoLine: A Versatile and Robust SDXL-ControlNet Model for Adaptable Line Art Conditioning.  
+
+MistoLine is an SDXL-ControlNet model that can adapt to any type of line art input, demonstrating high accuracy and excellent stability. It can generate  high-quality images (with a short side greater than 1024px) based on user-provided line art of various types, including hand-drawn sketches, different  ControlNet line preprocessors, and model-generated outlines. MistoLine eliminates the need to select different ControlNet models for different line  preprocessors, as it exhibits strong generalization capabilities across diverse line art conditions.  
+We developed MistoLine by employing a novel line preprocessing algorithm (Anyline) and retraining the ControlNet model based on the Unet of stabilityai/  stable-diffusion-xl-base-1.0, along with innovations in large model training engineering. MistoLine showcases superior performance across different types of
+line art inputs, surpassing existing ControlNet models in terms of detail restoration, prompt alignment, and stability, particularly in more complex  scenarios.  
+
+MistoLine maintains consistency with the ControlNet architecture released by lllyasviel, as illustrated in the following schematic diagram:
+![ControlNet architecture](assets/controlnet_1.png)  
+![ControlNet architecture](assets/controlnet_2.png)  
+*reference：https://github.com/lllyasviel/ControlNet*  
+
+More information about ControlNet can be found in the following references:  
+https://github.com/lllyasviel/ControlNet  
+https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl  
+
+The model is compatible with most SDXL models, except for PlaygroundV2.5 and CosXL. It can be used in conjunction with LCM and other ControlNet models. 
+We have open-sourced the corresponding model weight files for non-commercial use by individual users.
+
+## Apply with Different Line Preprocessors
+![preprocessors](assets/preprocessors.png)  
+
+## Compere with Other Controlnets
+![comparison](assets/comparison.png)  
+
+## Application Examples
+
+### Sketch Rendering
+![Sketch Rendering](assets/sketch_rendering.png)  
+
+### Model Rendering
+![Model Rendering](assets/model_rendering.png)
+
+## ComfyUI Recommended Parameters
+```
+sampler steps:30
+CFG:7.0
+sampler_name:dpmpp_2m_sde
+scheduler:karras
+denoise:0.93
+controlnet_strength:1.0
+stargt_percent:0.0
+end_percent:0.9
+```
+
+## Checkpoints
+* mistoLine_rank256.safetensors : General usage version, for ComfyUI and AUTOMATIC1111-WebUI.  
+* mistoLine_fp16.safetensors : FP16 weights, for ComfyUI and AUTOMATIC1111-WebUI.  
+
+## ComfyUI Usage
+![ComfyUI](assets/comfyui.png)
+
+## 中国（大陆地区）便捷下载地址： 
+链接：https://pan.baidu.com/s/1DbZWmGJ40Uzr3Iz9RNBG_w?pwd=8mzs  
+提取码：8mzs  
+
+## Citation
+```
+@misc{
+      title={Adding Conditional Control to Text-to-Image Diffusion Models}, 
+      author={Lvmin Zhang, Anyi Rao, Maneesh Agrawala},
+      year={2023},
+      eprint={2302.05543},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+