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eduardoprea44
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multiview-controlnet-nullcond

Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
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  1. .gitattributes +4 -0
  2. README.md +49 -0
  3. checkpoint-1000/controlnet/config.json +51 -0
  4. checkpoint-1000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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.gitattributes CHANGED
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ images_0.png filter=lfs diff=lfs merge=lfs -text
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+ images_1.png filter=lfs diff=lfs merge=lfs -text
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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-eduardoprea44/multiview-controlnet-nullcond
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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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+ You can find some example images below.
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+
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+ prompt: long sleeve upper rotated by 0 degrees
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+ ![images_0)](./images_0.png)
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+ prompt: dress rotated by 60 degrees
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+ ![images_1)](./images_1.png)
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+ prompt: short sleeve upper rotated by 180 degrees
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+ ![images_2)](./images_2.png)
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+ prompt: long sleeve dress rotated by 270 degrees
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+ ![images_3)](./images_3.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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