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End of training

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  1. README.md +43 -0
  2. checkpoint-1000/optimizer.bin +3 -0
  3. checkpoint-1000/random_states_0.pkl +3 -0
  4. checkpoint-1000/scheduler.bin +3 -0
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  6. checkpoint-1000/unet/diffusion_pytorch_model.safetensors +3 -0
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  19. logs/dreambooth/events.out.tfevents.1724090428.automl02.1195684.0 +3 -0
  20. logs/dreambooth/events.out.tfevents.1724091072.automl02.1210691.0 +3 -0
  21. logs/dreambooth/events.out.tfevents.1724091661.automl02.1222901.0 +3 -0
  22. model_index.json +38 -0
  23. safety_checker/config.json +28 -0
  24. safety_checker/model.safetensors +3 -0
  25. scheduler/scheduler_config.json +15 -0
  26. text_encoder/config.json +25 -0
  27. text_encoder/model.safetensors +3 -0
  28. tokenizer/merges.txt +0 -0
  29. tokenizer/special_tokens_map.json +24 -0
  30. tokenizer/tokenizer_config.json +30 -0
  31. tokenizer/vocab.json +0 -0
  32. unet/config.json +68 -0
  33. unet/diffusion_pytorch_model.safetensors +3 -0
  34. vae/config.json +38 -0
  35. vae/diffusion_pytorch_model.safetensors +3 -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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+ tags:
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+ - text-to-image
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+ - dreambooth
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+ - diffusers-training
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ inference: true
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+ instance_prompt: a painting in the style of sks
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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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+ # DreamBooth - sj98/wiki_1
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+
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+ This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a painting in the style of sks using [DreamBooth](https://dreambooth.github.io/).
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+ You can find some example images in the following.
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+
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+
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+
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+ DreamBooth for the text encoder was enabled: False.
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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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