Image To One Hot Causal Factor VAE Dsprites

--- 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 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # 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](https://microsoft-research.wandb.io/t-ricardoc/image_to_one_hot_causal_factor_vae_dsprites/runs/ckbzzzmd). ## Intended uses & limitations #### How to use ```python # 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]

Source context: 20 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo rcannizzaro/image_to_one_hot_causal_factor_vae_dsprites

Source context: 4 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo rcannizzaro/image_to_one_hot_causal_factor_vae_dsprites