Upload README.md

README.md

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+---
+license: openrail++
+tags:
+- art
+- stable diffusion
+- ControlNet
+- SDXL
+- Diffusion-XL
+pipeline_tag: text-to-image
+---
+# MistoLine
+## Control Every Line!
+
+![Intro Image](assets/intro.png)  
+[GitHub Repo](https://github.com/TheMistoAI/MistoLine)
+
+## NEWS!!!!! Anyline-preprocessor is released!!!!
+[Anyline Repo](https://github.com/TheMistoAI/ComfyUI-Anyline)
+
+**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](https://github.com/TheMistoAI/ComfyUI-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, CosXL, and SDXL-Lightning(maybe). It can be used in conjunction with LCM and other ControlNet models.  
+
+The following usage of this model is not allowed:  
+* Violating laws and regulations
+* Harming or exploiting minors
+* Creating and spreading false information
+* Infringing on others' privacy
+* Defaming or harassing others
+* Automated decision-making that harms others' legal rights
+* Discrimination based on social behavior or personal characteristics
+* Exploiting the vulnerabilities of specific groups to mislead their behavior
+* Discrimination based on legally protected characteristics
+* Providing medical advice and diagnostic results
+* Improperly generating and using information for purposes such as law enforcement and immigration
+
+If you use or distribute this model for commercial purposes, you must comply with the following conditions:  
+1. Clearly acknowledge the contribution of TheMisto.ai to this model in the documentation, website, or other prominent and visible locations of your product.
+   Example: "This product uses the MistoLine-SDXL-ControlNet developed by TheMisto.ai."
+2. If your product includes about screens, readme files, or other similar display areas, you must include the above attribution information in those areas.
+3. If your product does not have the aforementioned areas, you must include the attribution information in other reasonable locations within the product to ensure that end-users can notice it.
+4. You must not imply in any way that TheMisto.ai endorses or promotes your product. The use of the attribution information is solely to indicate the origin of this model.  
+If you have any questions about how to provide attribution in specific cases, please contact info@themisto.ai.  
+
+署名条款  
+如果您在商业用途中使用或分发本模型,您必须满足以下条件:  
+1. 在产品的文档,网站,或其他主要可见位置,明确提及 TheMisto.ai 对本软件的贡献。
+   示例: "本产品使用了 TheMisto.ai 开发的 MistoLine-SDXL-ControlNet。"
+2. 如果您的产品包含有关屏幕,说明文件,或其他类似的显示区域,您必须在这些区域中包含上述署名信息。
+3. 如果您的产品没有上述区域,您必须在产品的其他合理位置包含署名信息,以确保最终用户能够注意到。
+4. 您不得以任何方式暗示 TheMisto.ai 为您的产品背书或促销。署名信息的使用仅用于表明本模型的来源。  
+如果您对如何在特定情况下提供署名有任何疑问,请联系info@themisto.ai。
+
+The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk.
+
+## Apply with Different Line Preprocessors
+![preprocessors](assets/preprocessors.png)  
+
+## Compere with Other Controlnets
+![comparison](assets/comparison.png)  
+
+## Application Examples
+
+### Sketch Rendering
+*The following case only utilized MistoLine as the controlnet：*
+![Sketch Rendering](assets/sketch_rendering.png)  
+
+### Model Rendering
+*The following case only utilized Anyline as the preprocessor and MistoLine as the controlnet.*
+![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
+```
+## Diffusers pipeline
+Make sure to first install the libraries:
+```
+pip install accelerate transformers safetensors opencv-python diffusers
+```
+And then we're ready to go:
+```
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+
+prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+negative_prompt = 'low quality, bad quality, sketches'
+
+image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
+
+controlnet_conditioning_scale = 0.5
+
+controlnet = ControlNetModel.from_pretrained(
+    "TheMistoAI/MistoLine",
+    torch_dtype=torch.float16,
+    variant="fp16",
+)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+image = np.array(image)
+image = cv2.Canny(image, 100, 200)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+image = Image.fromarray(image)
+
+images = pipe(
+    prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+    ).images
+
+images[0].save(f"hug_lab.png")
+```
+
+
+## Checkpoints
+* mistoLine_rank256.safetensors : General usage version, for ComfyUI and AUTOMATIC1111-WebUI.  
+* mistoLine_fp16.safetensors : FP16 weights, for ComfyUI and AUTOMATIC1111-WebUI.
+
+## !!!mistoLine_rank256.safetensors better than mistoLine_fp16.safetensors
+## !!!mistoLine_rank256.safetensors 表现更加出色！！
+
+## 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}
+}
+```