| --- |
| license: openrail++ |
| tags: |
| - art |
| - stable diffusion |
| - ControlNet |
| - SDXL |
| - Diffusion-XL |
| pipeline_tag: text-to-image |
| --- |
| # MistoLine |
| ## Control Every Line! |
|
|
|  |
| [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: |
|  |
|  |
| *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 |
|  |
| |
| ## Compere with Other Controlnets |
|  |
| |
| ## Application Examples |
| |
| ### Sketch Rendering |
| *The following case only utilized MistoLine as the controlnet：* |
|  |
| |
| ### Model Rendering |
| *The following case only utilized Anyline as the preprocessor and MistoLine as the controlnet.* |
|  |
| |
| ## 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 |
|  |
|
|
| ## 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} |
| } |
| ``` |
| |
