Unconditional Image Generation
Diffusers
Safetensors
PyTorch
DDPMPipeline
diffusion-models-class
medical-imaging
brain-mri
multiple-sclerosis
Instructions to use benetraco/brain_ddpm_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use benetraco/brain_ddpm_64 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("benetraco/brain_ddpm_64", 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
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Browse files
README.md
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- **Gradient Accumulation:** 1 steps
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- **Mixed Precision:** No
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- **Hardware:** Trained on **one NVIDIA GeForce GTX 1080 Ti GPU of 12GB**
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- **Memory Consumption:** Around **
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## U-Net Architecture
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- **Down Blocks:** [DownBlock2D, DownBlock2D, AttnDownBlock2D, DownBlock2D]
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- **Gradient Accumulation:** 1 steps
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- **Mixed Precision:** No
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- **Hardware:** Trained on **one NVIDIA GeForce GTX 1080 Ti GPU of 12GB**
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- **Memory Consumption:** Around **7 GB** during training
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## U-Net Architecture
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- **Down Blocks:** [DownBlock2D, DownBlock2D, AttnDownBlock2D, DownBlock2D]
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