---
base_model: OFA-Sys/small-stable-diffusion-v0
library_name: diffusers
license: creativeml-openrail-m
tags:
- 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 - jpholanda/SD-cover-art

This pipeline was finetuned from **OFA-Sys/small-stable-diffusion-v0** on the **MusicBrainz** and **Cover Art Archive** datasets. Below are some example images generated with the finetuned pipeline using the following prompts: ['Cover art for a disco album titled "My Love", by "Meux Amis"']: 

![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("jpholanda/SD-coverart-v1", torch_dtype=torch.float16)
prompt = 'Cover art for a disco album titled "My Love", by "Meux Amis"'
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 5
* Learning rate: 1e-05
* Batch size: 32
* Gradient accumulation steps: 4
* Image resolution: 250
* Mixed-precision: fp16


More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/jparholanda/text2image-fine-tune/runs/3ukw0a9n).


## Training details

Used the [MusicBrainz](https://musicbrainz.org/) dataset for the metadata (title, genre, artist)
and the [Cover Art Archive](https://coverartarchive.org/) for the cover arts.