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my_controlnet

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Diffusers
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stable-diffusion-diffusers
controlnet
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  1. README.md +39 -0
  2. checkpoint-3000/controlnet/config.json +52 -0
  3. checkpoint-3000/controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-3000/controlnet/model.safetensors +3 -0
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  9. config.json +52 -0
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  28. logs/train_controlnet/events.out.tfevents.1740726110.CHINAMI-7F392VC.6312.0 +3 -0
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  30. logs/train_controlnet/events.out.tfevents.1740727876.CHINAMI-7F392VC.3640.0 +3 -0
  31. logs/train_controlnet/events.out.tfevents.1740728162.CHINAMI-7F392VC.23456.0 +3 -0
README.md ADDED
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+ ---
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+ base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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+ library_name: diffusers
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+ license: creativeml-openrail-m
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+ inference: true
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-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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+ ---
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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-Jason248/my_controlnet
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+
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+ These are controlnet weights trained on stable-diffusion-v1-5/stable-diffusion-v1-5 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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+ ],
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+ "controlnet_conditioning_channel_order": "rgb",
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+ "cross_attention_dim": 768,
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+ "down_block_types": [
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+ "CrossAttnDownBlock2D",
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+ "CrossAttnDownBlock2D",
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+ "CrossAttnDownBlock2D",
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+ "DownBlock2D"
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