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controlnet-floorplan-topdown

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Diffusers
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stable-diffusion-diffusers
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  1. README.md +39 -0
  2. checkpoint-1000/controlnet/config.json +51 -0
  3. checkpoint-1000/controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-1000/optimizer.bin +3 -0
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  8. checkpoint-1200/controlnet/config.json +51 -0
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  14. checkpoint-1400/controlnet/config.json +51 -0
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  20. checkpoint-1600/controlnet/config.json +51 -0
  21. checkpoint-1600/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  26. checkpoint-1800/controlnet/config.json +51 -0
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  32. checkpoint-200/controlnet/config.json +51 -0
  33. checkpoint-200/controlnet/diffusion_pytorch_model.safetensors +3 -0
  34. checkpoint-200/optimizer.bin +3 -0
  35. checkpoint-200/random_states_0.pkl +3 -0
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  38. checkpoint-2000/controlnet/config.json +51 -0
  39. checkpoint-2000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  42. checkpoint-2000/scaler.pt +3 -0
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  44. checkpoint-2200/controlnet/config.json +51 -0
  45. checkpoint-2200/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  50. checkpoint-2400/controlnet/config.json +51 -0
README.md ADDED
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+ ---
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+ base_model: runwayml/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-aqhareus/controlnet-floorplan-topdown
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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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+
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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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