---
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: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
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](https://wandb.ai/mri-diffusion/mri-bruno-sd-v2_base-512-bs128/runs/p2psohjh).