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experiment8

Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
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End of training

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  1. .gitattributes +3 -0
  2. README.md +45 -0
  3. checkpoint-1000/controlnet/config.json +51 -0
  4. checkpoint-1000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  28. checkpoint-500/controlnet/config.json +51 -0
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  33. config.json +51 -0
  34. diffusion_pytorch_model.safetensors +3 -0
  35. image_control.png +3 -0
  36. images_0.png +3 -0
  37. images_1.png +3 -0
  38. logs/train_controlnet/1742451951.844084/events.out.tfevents.1742451951.ucy0303.41866.1 +3 -0
  39. logs/train_controlnet/1742451951.8473275/hparams.yml +50 -0
  40. logs/train_controlnet/events.out.tfevents.1742451951.ucy0303.41866.0 +3 -0
.gitattributes CHANGED
@@ -33,3 +33,6 @@ 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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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-chichichaechae/experiment8
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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: Improve low_haze_snow damage in the image.
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+ ![images_0)](./images_0.png)
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+ prompt: Improve low_haze_rain damage in the image.
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+ ![images_1)](./images_1.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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