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.