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  1. README.md +47 -0
  2. checkpoint-1000/controlnet/config.json +56 -0
  3. checkpoint-1000/controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-1000/optimizer.bin +3 -0
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  7. checkpoint-1500/controlnet/config.json +56 -0
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  17. checkpoint-2500/controlnet/config.json +56 -0
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  22. checkpoint-3000/controlnet/config.json +56 -0
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  32. checkpoint-4000/controlnet/config.json +56 -0
  33. checkpoint-4000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  37. checkpoint-4500/controlnet/config.json +56 -0
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  42. checkpoint-500/controlnet/config.json +56 -0
  43. checkpoint-500/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  47. checkpoint-5000/controlnet/config.json +56 -0
  48. checkpoint-5000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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README.md ADDED
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+ ---
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+ base_model: stabilityai/stable-diffusion-2-1-base
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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-shunki555/model_out2
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+
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+ These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base 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 lymph node without cancer metastasis, lymph, center of the image,
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+ ![images_0)](./images_0.png)
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+ prompt: A lymph node without cancer metastasis, lymph, center of the image,
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+ ![images_1)](./images_1.png)
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+ prompt: A lymph node without cancer metastasis, lymph, center of the image,
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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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