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animagine-result-10000

Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
controlnet
diffusers-training
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End of training

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  1. README.md +39 -0
  2. checkpoint-1000/controlnet/config.json +57 -0
  3. checkpoint-1000/controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-1000/optimizer.bin +3 -0
  5. checkpoint-1000/random_states_0.pkl +3 -0
  6. checkpoint-1000/scheduler.bin +3 -0
  7. checkpoint-2000/controlnet/config.json +57 -0
  8. checkpoint-2000/controlnet/diffusion_pytorch_model.safetensors +3 -0
  9. checkpoint-2000/optimizer.bin +3 -0
  10. checkpoint-2000/random_states_0.pkl +3 -0
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  12. checkpoint-3000/controlnet/config.json +57 -0
  13. checkpoint-3000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  15. checkpoint-3000/random_states_0.pkl +3 -0
  16. checkpoint-3000/scheduler.bin +3 -0
  17. config.json +57 -0
  18. diffusion_pytorch_model.safetensors +3 -0
README.md ADDED
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+ ---
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+ base_model: cagliostrolab/animagine-xl-3.1
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+ library_name: diffusers
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+ license: openrail++
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+ tags:
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - text-to-image
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+ - diffusers
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+ - controlnet
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+ - diffusers-training
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+ inference: true
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the training script had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+
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+ # controlnet-watsydney/animagine-result-3000
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+
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+ These are controlnet weights trained on cagliostrolab/animagine-xl-3.1 with new type of conditioning.
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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ # TODO: add an example code snippet for running this diffusion pipeline
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+ ```
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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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+ "_class_name": "ControlNetModel",
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+ "_diffusers_version": "0.30.0",
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+ "act_fn": "silu",
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+ "controlnet_conditioning_channel_order": "rgb",
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+ "cross_attention_dim": 2048,
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+ "down_block_types": [
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+ "DownBlock2D",
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+ "CrossAttnDownBlock2D",
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+ "CrossAttnDownBlock2D"
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+ ],
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+ "downsample_padding": 1,
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