FrankCCCCC/cfm-cifar10-32

Flow Matching CIFAR-10 Model

A flow matching model for unconditional image generation trained on the CIFAR-10 dataset. This model uses continuous normalizing flows with the FlowMatchEulerDiscreteScheduler for efficient sampling.

Model Details

• Architecture: UNet2DModel with flow matching
• Dataset: CIFAR-10 (32x32 RGB images)
• Scheduler: FlowMatchEulerDiscreteScheduler
• Training Steps: 1000 timesteps
• Framework: Diffusers 0.35.0.dev0

Flow Matching Configuration

The model uses FlowMatchEulerDiscreteScheduler with the following key parameters:

• Base shift: 0.5
• Shift: 1.0 (exponential time shifting)
• Base image sequence length: 256
• Max image sequence length: 4096
• Stochastic sampling: Disabled for deterministic generation

Usage

Basic Generation

from diffusers import DDPMPipeline

# Load the flow matching model
pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")

# Generate an image
image = pipeline().images[0]
image.save("generated_cifar10.png")

Custom Inference Steps

from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")

# Generate with custom number of inference steps
num_inference_steps: int = 1000
pipeline.scheduler.set_timesteps(num_inference_steps)
image = pipeline().images[0]
image.save("fast_generated_cifar10.png")

Batch Generation

from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")

# Generate multiple images at once
images = pipeline(batch_size=4).images
for i, image in enumerate(images):
    image.save(f"generated_cifar10_{i}.png")

Flow Matching vs Standard Diffusion

This model implements flow matching, which offers several advantages over standard diffusion models:

• Faster sampling: More efficient ODE solving with fewer steps
• Better training stability: Continuous normalizing flows provide smoother optimization
• Flexible scheduling: Exponential time shifting for improved sample quality

Model Architecture

• UNet: Standard UNet2DModel for denoising/flow prediction
• Scheduler: FlowMatchEulerDiscreteScheduler with exponential time shifting
• Output: 32x32 RGB images matching CIFAR-10 distribution

Requirements

pip install diffusers torch torchvision

Samples

1. 
2. 
3. 
4. 
5. 

Citation

If you use this model, please cite the original flow matching and diffusion literature:

@inproceedings{DDPM,
  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  booktitle = {Advances in Neural Information Processing Systems},
  title = {Denoising Diffusion Probabilistic Models},
  url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
  year = {2020}
}

@inproceedings{FM,
  title={Flow Matching for Generative Modeling},
  author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matthew Le},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=PqvMRDCJT9t}
}