Text-to-image finetuning - MaxReynolds/MyStreamlitModel

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the MaxReynolds/MyPatternDataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("MaxReynolds/MyStreamlitModel", torch_dtype=torch.float16)
prompt = "<r4nd0m-l4b3l>"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 42
• Learning rate: 1e-05
• Batch size: 1
• Gradient accumulation steps: 4
• Image resolution: 512
• Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.