anime-painter
xinsir基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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AI 모델 탐색 7
Hello, I am very happy to announce the controlnet-canny-sdxl-1.0 model, **a very powerful controlnet that can generate high resolution images visually comparable with midjourney**. The model was trained with large amount of high quality data(over 10000000 images), with carefully filtered and captioned(powerful vllm model). Besides, useful tricks are applied during the training, including date augmentation, mutiple loss and multi resolution. With only 1 stage training, the performance outperforms the other opensource canny models ([diffusers/controlnet-canny-sdxl-1.0], [TheMistoAI/MistoLine]). I release it and hope to advance the application of stable diffusion models. Canny is one of the most important ControlNet series models and can be applied to many jobs associated with drawing and designing. - **Developed by:** xinsir - **Model type:** ControlNet_SDXL - **License:** apache-2.0 - **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0 prompt: A closeup of two day of the dead models, looking to the side, large flowered headdress, full dia de Los muertoe make up, lush red lips, butterflies, flowers, pastel colors, looking to the side, jungle, birds, color harmony , extremely detailed, intricate, ornate, motion, stunning, beautiful, unique, soft lighting
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗- **Developed by:** xinsir - **Model type:** ControlNet_SDXL - **License:** apache-2.0 - **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0 thanks feiyuuu for report the problem. When using the default pose line the performance may be unstable, this is because the pose label use more thick line in training to have a better look. This difference can be fix by using the following method: Find the util.py in controlnet_aux python package, usually the path is like: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnet_aux/open_pose/util.py Replace the draw_bodypose function with the following code:
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗The following five lines are using different control lines, from top to below, Scribble, Canny, HED, PIDI, Lineart Hello, I am very happy to announce the controlnet-scribble-sdxl-1.0 model, **a very powerful controlnet that can generate high resolution images visually comparable with midjourney**. The model was trained with large amount of high quality data(over 10000000 images), with carefully filtered and captioned(powerful vllm model). Besides, useful tricks are applied during the training, including date augmentation, mutiple loss and multi resolution. Note that this model can achieve higher aesthetic performance than our Controlnet-Canny-Sdxl-1.0 model, the model support any type of lines and any width of lines, the sketch can be very simple and so does the prompt. This model is more general and good at generate visual appealing images, The control ability is also strong, for example if you are unstatisfied with some local regions about the generated image, draw a more precise sketch and give a detail prompt will help a lot. **Note the model also support lineart or canny lines, you can try it and will get a surpurise!!!** - **Developed by:** xinsir - **Model type:** ControlNet_SDXL - **License:** apache-2.0 - **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗controlnet-tile-sdxl-1.0
xinsircode reference: https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic/blob/main/TTP_tile_preprocessor_v5.py https://github.com/lllyasviel/ControlNet-v1-1-nightly/blob/main/gradio_tile.py
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗- Use bucket training like novelai, can generate high resolutions images of any aspect ratio - Use large amount of high quality data(over 10000000 images), the dataset covers a diversity of situation - Use re-captioned prompt like DALLE.3, use CogVLM to generate detailed description, good prompt following ability - Use many useful tricks during training. Including but not limited to date augmentation, mutiple loss, multi resolution - Use almost the same parameter compared with original ControlNet. No obvious increase in network parameter or computation. - Support 10+ control conditions, no obvious performance drop on any single condition compared with training independently - Support multi condition generation, condition fusion is learned during training. No need to set hyperparameter or design prompts. - Compatible with other opensource SDXL models, such as BluePencilXL, CounterfeitXL. Compatible with other Lora models. ***We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney***. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters. We do thoroughly experiments on SDXL and achieve superior performance both in control ability and aesthetic score. We release the method and the model to the open source community to make everyone can enjoy it. Inference scripts and more details can found: https://github.com/xinsir6/ControlNetPlus/tree/main
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