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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 [email protected].  
-
-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[email protected]。
-
-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}
-}
-```