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LICENSE.md

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+Copyright (c) 2024 TheMisto.ai Inc. License dated April 11, 2024
+Section I: PREAMBLE Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation. Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations. In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation. Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this agreement aims to strike a balance between both in order to enable responsible open-science in the field of AI. This CreativeML Open RAIL++-M License governs the use of the model (and its derivatives) and is informed by the model card associated with the model. NOW THEREFORE, You and Licensor agree as follows: Definitions "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document. "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License. "Output" means the results of operating a Model as embodied in informational content resulting therefrom. "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material. "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model. "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any. 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"Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+Section II: INTELLECTUAL PROPERTY RIGHTS Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed. Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions: Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License; You must cause any modified files to carry prominent notices stating that You changed the files; You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5). The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+Section IV: OTHER PROVISIONS Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License. Trademarks and related. Nothing in this License permits You to make use of Licensors' trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+END OF TERMS AND CONDITIONS
+Attachment A Use Restrictions You agree not to use the Model or Derivatives of the Model: In any way that violates any applicable national, federal, state, local or international law or regulation; For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; To generate or disseminate verifiably false information and/or content with the purpose of harming others; To generate or disseminate personal identifiable information that can be used to harm an individual; To defame, disparage or otherwise harass others; For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation; For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; To provide medical advice and medical results interpretation; To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).

README.md

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----
-license: mit
----
+---
+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}
+}
+```

config.json

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+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.27.2",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": true
+}

diffusion_pytorch_model.fp16.safetensors

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mistoLine_fp16.safetensors

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mistoLine_rank256.safetensors

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