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old_controlnet_1

Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
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  1. README.md +47 -0
  2. checkpoint-10/controlnet/config.json +51 -0
  3. checkpoint-10/controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-10/optimizer.bin +3 -0
  5. checkpoint-10/random_states_0.pkl +3 -0
  6. checkpoint-10/scheduler.bin +3 -0
  7. checkpoint-100/controlnet/config.json +51 -0
  8. checkpoint-100/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  12. checkpoint-1000/controlnet/config.json +51 -0
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  17. checkpoint-1050/controlnet/config.json +51 -0
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  22. checkpoint-1100/controlnet/config.json +51 -0
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  32. checkpoint-1200/controlnet/config.json +51 -0
  33. checkpoint-1200/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  42. checkpoint-1300/controlnet/config.json +51 -0
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  47. checkpoint-1350/controlnet/config.json +51 -0
  48. checkpoint-1350/controlnet/diffusion_pytorch_model.safetensors +3 -0
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README.md ADDED
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+ ---
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+ license: creativeml-openrail-m
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+ library_name: diffusers
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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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+ base_model: runwayml/stable-diffusion-v1-5
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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-tempertrash/model_out
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+
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+ These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
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+ You can find some example images below.
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+
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+ prompt: a photography of a woman with a smile on her face
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+ ![images_0)](./images_0.png)
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+ prompt: a close up of a passion flower
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+ ![images_1)](./images_1.png)
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+ prompt: a couple laying on the beach
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+ ![images_2)](./images_2.png)
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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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