jpholanda/SD-coverart-v2

Text-to-image finetuning - jpholanda/SD-coverart

This pipeline was finetuned from jpholanda/SD-coverart-v1 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-v2', 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: 48
• 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

[TODO: describe the data used to train the model]