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.
