SD-coverart-v1

<!-- 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"']:

Pipeline usage

You can use the pipeline like so:

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.

Training details

Used the MusicBrainz dataset for the metadata (title, genre, artist) and the Cover Art Archive for the cover arts.