---
base_model: michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
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 - michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0

This pipeline was finetuned from **michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors** on the **michaelyli/dsprites-counterfactual-exp-decouple_factors-filtered-iter-0** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A square.', 'A ellipse.', 'A heart.']: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0", torch_dtype=torch.float16)
prompt = "A square."
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 400
* Learning rate: 1e-05
* 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-michaelli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0/runs/wkl7e3m7).


## 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]