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--- |
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license: openrail++ |
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tags: |
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- art |
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- stable diffusion |
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- ControlNet |
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- SDXL |
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- Diffusion-XL |
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pipeline_tag: text-to-image |
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--- |
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# MistoLine |
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## Control Every Line! |
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[GitHub Repo](https://github.com/TheMistoAI/MistoLine) |
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**MistoLine: A Versatile and Robust SDXL-ControlNet Model for Adaptable Line Art Conditioning.** |
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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. |
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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 |
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different types of line art inputs, surpassing existing ControlNet models in terms of detail restoration, prompt alignment, and stability, particularly in more complex scenarios. |
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MistoLine maintains consistency with the ControlNet architecture released by @lllyasviel, as illustrated in the following schematic diagram: |
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*reference：https://github.com/lllyasviel/ControlNet* |
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More information about ControlNet can be found in the following references: |
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https://github.com/lllyasviel/ControlNet |
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https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl |
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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. |
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The following usage of this model is not allowed: |
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* Violating laws and regulations |
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* Harming or exploiting minors |
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* Creating and spreading false information |
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* Infringing on others' privacy |
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* Defaming or harassing others |
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* Automated decision-making that harms others' legal rights |
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* Discrimination based on social behavior or personal characteristics |
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* Exploiting the vulnerabilities of specific groups to mislead their behavior |
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* Discrimination based on legally protected characteristics |
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* Providing medical advice and diagnostic results |
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* Improperly generating and using information for purposes such as law enforcement and immigration |
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If you use or distribute this model for commercial purposes, you must comply with the following conditions: |
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1. Clearly acknowledge the contribution of TheMisto.ai to this model in the documentation, website, or other prominent and visible locations of your product. |
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Example: "This product uses the MistoLine-SDXL-ControlNet developed by TheMisto.ai." |
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2. If your product includes about screens, readme files, or other similar display areas, you must include the above attribution information in those areas. |
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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. |
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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. |
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If you have any questions about how to provide attribution in specific cases, please contact info@themisto.ai. |
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署名条款 |
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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: |
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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。 |
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示例: "本产品使用了 TheMisto.ai 开发的 MistoLine-SDXL-ControlNet。" |
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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。 |
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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。 |
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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。 |
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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。 |
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The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk. |
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## Apply with Different Line Preprocessors |
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## Compere with Other Controlnets |
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## Application Examples |
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### Sketch Rendering |
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*The following case only utilized MistoLine as the controlnet：* |
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### Model Rendering |
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*The following case only utilized Anyline as the preprocessor and MistoLine as the controlnet.* |
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## ComfyUI Recommended Parameters |
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``` |
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sampler steps:30 |
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CFG:7.0 |
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sampler_name:dpmpp_2m_sde |
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scheduler:karras |
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denoise:0.93 |
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controlnet_strength:1.0 |
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stargt_percent:0.0 |
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end_percent:0.9 |
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``` |
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## Checkpoints |
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* mistoLine_rank256.safetensors : General usage version, for ComfyUI and AUTOMATIC1111-WebUI. |
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* mistoLine_fp16.safetensors : FP16 weights, for ComfyUI and AUTOMATIC1111-WebUI. |
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## ComfyUI Usage |
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## 中国（大陆地区）便捷下载地址： |
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链接：https://pan.baidu.com/s/1DbZWmGJ40Uzr3Iz9RNBG_w?pwd=8mzs |
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提取码：8mzs |
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## Citation |
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``` |
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@misc{ |
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title={Adding Conditional Control to Text-to-Image Diffusion Models}, |
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author={Lvmin Zhang, Anyi Rao, Maneesh Agrawala}, |
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year={2023}, |
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eprint={2302.05543}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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