Text-to-image finetuning - Stomper10/CXR_unet_nf

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A photo of a lung-xray.']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Stomper10/CXR_unet_nf", torch_dtype=torch.float16)
prompt = "A photo of a lung-xray."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 1
• Learning rate: 0.0064
• Batch size: 32
• Gradient accumulation steps: 1
• Image resolution: 512
• Mixed-precision: fp16

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