Hugging Face's logo

yurman
/
uncond-sd2-base-complex

Diffusers
Safetensors
StableDiffusionUnconditionalPipeline
stable-diffusion
stable-diffusion-diffusers
Model card Files Community
uncond-sd2-base-complex / README.md
yurman's picture
yurman
End of training
a5f260a verified
preview code
|
raw
history blame contribute delete
1.33 kB
metadata
license: creativeml-openrail-m
base_model: yurman/uncond_sd2-base
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - diffusers
inference: true

Unconditioned stable diffusion finetuning - yurman/uncond-sd2-base-complex

This pipeline was finetuned from yurman/uncond_sd2-base 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 StableDiffusionUnconditionalPipeline
import torch

pipeline = StableDiffusionUnconditionalPipeline.from_pretrained("yurman/uncond-sd2-base-complex", torch_dtype=torch.float32)
image = pipeline(1).images[0]
image.save("brain_image.png")

Training info

These are the key hyperparameters used during training:

  • Epochs: 22
  • Max Train Steps: 10000
  • Learning rate: 5e-05
  • Batch size: 18
  • VAE scaling: 0.11
  • VAE type: MEDVAE
  • Input perturbation: 0.0
  • Noise offset: 0.0
  • Gradient accumulation steps: 3
  • Image resolution: 256
  • Mixed-precision: no
  • Max rotation degree: 10
  • Prediction Type: v_prediction
  • SNR Gamma: 5.0

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