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--- |
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license: creativeml-openrail-m |
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base_model: yurman/mri_full_512_v2_base |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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inference: true |
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--- |
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# Text-to-image finetuning - zachary-shah/mri-bruno-sd-v2_base-512-bs128-zerotermsnr |
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This pipeline was finetuned from **yurman/mri_full_512_v2_base** |
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on the stanford dataset for brain image generation. |
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Below are some example images generated with the finetuned pipeline: |
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## Pipeline usage |
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You can use the pipeline like so: |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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pipeline = DiffusionPipeline.from_pretrained("zachary-shah/mri-bruno-sd-v2_base-512-bs128-zerotermsnr", torch_dtype=torch.float16) |
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prompt = "An empty, flat black image with a MRI brain axial scan in the center" |
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image = pipeline(prompt).images[0] |
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image.save("my_image.png") |
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``` |
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## Training info |
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These are the key hyperparameters used during training: |
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* Epochs: 173 |
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* Learning rate: 5e-05 |
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* embeds rate: 1e-05 |
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* Batch size: 8 |
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* Classifier free guidance: 1 |
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* VAE scaling: Same as in the original model |
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* Input perturbation: 0 |
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* Noise offset: 0 |
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* Gradient accumulation steps: 4 |
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* Image resolution: 512 |
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* Mixed-precision: None |
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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). |
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