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image_to_one_hot_causal_factor_vae_dsprites

Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
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image_to_one_hot_causal_factor_vae_dsprites / README.md
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metadata
license: creativeml-openrail-m
library_name: diffusers
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
base_model: VAE
inference: true

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]