metadata
license: creativeml-openrail-m
base_model: yurman/mri_full_512_v2_base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
Text-to-image finetuning - zachary-shah/mri-bruno-sd-v2_base-512-bs128-zerotermsnr
This pipeline was finetuned from yurman/mri_full_512_v2_base on the stanford dataset for brain image generation. Below are some example images generated with the finetuned pipeline:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("zachary-shah/mri-bruno-sd-v2_base-512-bs128-zerotermsnr", torch_dtype=torch.float16)
prompt = "An empty, flat black image with a MRI brain axial scan in the center"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 173
- Learning rate: 5e-05
- embeds rate: 1e-05
- Batch size: 8
- Classifier free guidance: 1
- VAE scaling: Same as in the original model
- Input perturbation: 0
- Noise offset: 0
- Gradient accumulation steps: 4
- Image resolution: 512
- Mixed-precision: None
More information on all the CLI arguments and the environment are available on your wandb
run page.