---
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]