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---
base_model: runwayml/stable-diffusion-v1-5
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-incorrect_counterfactual_coupled_factors_init_stable-diffusion-v1-5
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **michaelyli/dsprites-coupled-under-specified** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A square.', 'A ellipse.', 'A heart.']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors_init_stable-diffusion-v1-5", 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: 1
* 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-incorrect_counterfactual_coupled_factors_init_stable-diffusion-v1-5/runs/ul2ecoik).
## 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] |