Instructions to use xinsir/controlnet-canny-sdxl-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xinsir/controlnet-canny-sdxl-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("xinsir/controlnet-canny-sdxl-1.0") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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Hello, I am very happy to announce the controlnet-canny-sdxl-1.0 model, a very powerful controlnet that can
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with large amount of high quality data, with carefully filtered and captioned. Besides, useful tricks are applied
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and multi resolution. With only 1 stage training, the performance outperforms the other opensource canny models
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the application of stable diffusion models. Canny is one of the most important
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## Model Details
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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.
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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
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during the training, including date augmentation, mutiple loss and multi resolution. With only 1 stage training, the performance outperforms the other opensource canny models
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([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
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ControlNet series models and can be applied to many jobs associated with drawing and designing.
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## Model Details
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