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---
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-4
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:
```python
from diffusers import StableDiffusionUnconditionalPipeline
import torch
pipeline = StableDiffusionUnconditionalPipeline.from_pretrained("yurman/uncond-sd2-base-complex-4", 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: 400
* Max Train Steps: 100000
* Learning rate: 5e-05
* Batch size: 18
* VAE scaling: 0.12
* 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](https://wandb.ai/mri-diffusion/uncond-sd2-base-complex/runs/fpsgaddb).
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