Diffusers
Safetensors
StableDiffusionUnconditionalPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use zachary-shah/unconditional_cdmd_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zachary-shah/unconditional_cdmd_512 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zachary-shah/unconditional_cdmd_512", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Unconditioned stable diffusion finetuning - zachary-shah/unconditional_cdmd_512
This pipeline was finetuned from zachary-shah/unconditional_mri_full_512_v2_base on the OASIS-3 dataset 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("zachary-shah/unconditional_cdmd_512", torch_dtype=torch.float32)
image = pipeline(1).images[0]
image.save("brain_image.png")
Training info
For training info, refer the model card for the parent conditional model: zachary-shah/unconditional_mri_full_512_v2_base.
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Model tree for zachary-shah/unconditional_cdmd_512
Base model
stabilityai/stable-diffusion-2-base Finetuned
yurman/mri_full_512_v2_base