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image_to_one_hot_causal_factor_vae_dsprites

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Text-to-image finetuning - rcannizzaro/image_to_one_hot_causal_factor_vae_dsprites

This Image to One-Hot Causal Factor Encoder/Decoder VAE Network was trained on the osazuwa/dsprite-counterfactual dataset. Below are some example images generated with the finetuned pipeline using the following prompts:

Training info

These are the key hyperparameters used during training:

  • Epochs: 7
  • Learning rate: 0.0001
  • Batch size: 100
  • Gradient accumulation steps: 1
  • Image resolution: 64
  • Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.

Intended uses & limitations

How to use 

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]

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