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Text-to-image finetuning - zachary-shah/mri-bruno-sd-v2_base-512-bs128

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:

val_imgs_grid

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", 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: 228
  • Learning rate: 5e-05
  • embeds rate: 1e-05
  • Batch size: 8
  • Classifier free guidance: 1
  • VAE scaling: 0.06
  • 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.

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