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  1. README.md +68 -0
  2. checkpoint-13000/optimizer.bin +3 -0
  3. checkpoint-13000/random_states_0.pkl +3 -0
  4. checkpoint-13000/scaler.pt +3 -0
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  6. checkpoint-13000/unet/config.json +68 -0
  7. checkpoint-13000/unet/diffusion_pytorch_model.safetensors +3 -0
  8. checkpoint-13000/unet_ema/config.json +75 -0
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  15. checkpoint-13500/unet/diffusion_pytorch_model.safetensors +3 -0
  16. checkpoint-13500/unet_ema/config.json +75 -0
  17. checkpoint-13500/unet_ema/diffusion_pytorch_model.safetensors +3 -0
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  31. checkpoint-14500/unet/diffusion_pytorch_model.safetensors +3 -0
  32. checkpoint-14500/unet_ema/config.json +75 -0
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  39. checkpoint-15000/unet/diffusion_pytorch_model.safetensors +3 -0
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  41. checkpoint-15000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
  42. feature_extractor/preprocessor_config.json +44 -0
  43. model_index.json +38 -0
  44. scheduler/scheduler_config.json +15 -0
  45. text_encoder/config.json +25 -0
  46. text_encoder/model.safetensors +3 -0
  47. tokenizer/merges.txt +0 -0
  48. tokenizer/special_tokens_map.json +24 -0
  49. tokenizer/tokenizer_config.json +30 -0
  50. tokenizer/vocab.json +0 -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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+ - 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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+ - diffusers-training
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+ inference: true
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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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+ # Text-to-image finetuning - michaelyli/sd-dsprites-star-toy
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+
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+ This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **michaelyli/dsprites-captions-simple** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A medium-sized heart.', 'A large-sized square.', 'A small heart.']:
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+
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+ ![val_imgs_grid](./val_imgs_grid.png)
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+
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+
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+ ## Pipeline usage
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+
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+ You can use the pipeline like so:
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+
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+ ```python
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+ from diffusers import DiffusionPipeline
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+ import torch
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+
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+ pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-star-toy", torch_dtype=torch.float16)
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+ prompt = "A medium-sized heart."
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+ image = pipeline(prompt).images[0]
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+ image.save("my_image.png")
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+ ```
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+
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+ ## Training info
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+
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+ These are the key hyperparameters used during training:
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+
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+ * Epochs: 7500
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+ * Learning rate: 1e-05
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+ * Batch size: 1000
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+ * Gradient accumulation steps: 1
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+ * Image resolution: 64
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+ * Mixed-precision: fp16
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+
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+
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+ More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://microsoft-research.wandb.io/t-michaelli/sd-dsprites-star-toy/runs/bz7vz7oj).
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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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