Denoising Diffusion Probabilistic Models
Paper • 2006.11239 • Published • 9
Unconditional Image Generation
Diffusers
Safetensors
PyTorch
DDPMPipeline
diffusion-models-class
medical-imaging
brain-mri
multiple-sclerosis
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("benetraco/brain_ddpm_256", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Brain MRI Synthesis with DDPM
This model is a diffusion-based model for unconditional image generation of brain MRI FLAIR slices of size 256x256 pixels.
The model was trained using the DDPM architecture, with attention mechanisms in the middle of the U-Net.
It is trained from scratch on a dataset of brain MRI slices, specifically designed for generating synthetic brain images.
Training Details
- Architecture: DDPM (Denoising Diffusion Probabilistic Model)
- Resolution: 256x256 pixels
- Dataset: Lesion2D VH splitted (FLAIR MRI slices) (70% of the dataset)
- Channels: 1 (grayscale, FLAIR modality)
- Epochs: 50
- Batch size: 4
- Optimizer: AdamW with learning rate of
1.0e-4 - Scheduler: Cosine with 500 warm-up steps
- Gradient Accumulation: 8 steps
- Mixed Precision: No
- Hardware: Trained on one NVIDIA GeForce GTX 1080 Ti GPU of 12GB
- Memory Consumption: Around 11 GB during training
U-Net Architecture
- Down Blocks: [DownBlock2D, DownBlock2D, DownBlock2D, DownBlock2D, AttnDownBlock2D, DownBlock2D]
- Up Blocks: [UpBlock2D, AttnUpBlock2D, UpBlock2D, UpBlock2D, UpBlock2D, UpBlock2D]
- Layers per Block: 2
- Block Channels: [128, 128, 256, 256, 512, 512]
Usage
You can use the model directly with the diffusers library:
from diffusers import DDPMPipeline
import torch
# Load the model
pipeline = DDPMPipeline.from_pretrained("benetraco/brain_ddpm_256")
pipeline.to("cuda") # or "cpu"
# Generate an image
image = pipeline(batch_size=1).images[0]
# Display the image
image.show()
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