| |
|
| | --- |
| | 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: |
| | |
| |  |
| | |
| | |
| | ## 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", 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](https://wandb.ai/mri-diffusion/uncond-sd2-base-complex/runs/krp3zs5e). |
| | |
