Diffusers
Safetensors
StableDiffusionUnconditionalPipeline
stable-diffusion
stable-diffusion-diffusers
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("yurman/uncond-sd2-base-complex-8", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Unconditioned stable diffusion finetuning - yurman/uncond-sd2-base-complex-8
This pipeline was finetuned from yurman/uncond_sd2-base 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 StableDiffusionUnconditionalPipeline
import torch
pipeline = StableDiffusionUnconditionalPipeline.from_pretrained("yurman/uncond-sd2-base-complex-8", 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: 474
- Max Train Steps: 100000
- Learning rate: 5e-05
- Batch size: 64
- VAE scaling: 0.12
- VAE type: MEDVAE
- Input perturbation: 0.0
- Noise offset: 0.0
- Gradient accumulation steps: 1
- 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.
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