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1from diffusers import DDPMPipeline
2
3# Load the flow matching model
4pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")
5
6# Generate an image
7image = pipeline().images[0]
8image.save("generated_cifar10.png")1from diffusers import DDPMPipeline
2
3pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")
4
5# Generate with custom number of inference steps
6num_inference_steps: int = 1000
7pipeline.scheduler.set_timesteps(num_inference_steps)
8image = pipeline().images[0]
9image.save("fast_generated_cifar10.png")1from diffusers import DDPMPipeline
2
3pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/cfm-cifar10-32")
4
5# Generate multiple images at once
6images = pipeline(batch_size=4).images
7for i, image in enumerate(images):
8 image.save(f"generated_cifar10_{i}.png")pip install diffusers torch torchvision
1@inproceedings{DDPM,
2 author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
3 booktitle = {Advances in Neural Information Processing Systems},
4 title = {Denoising Diffusion Probabilistic Models},
5 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
6 year = {2020}
7}
8
9@inproceedings{FM,
10 title={Flow Matching for Generative Modeling},
11 author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matthew Le},
12 booktitle={The Eleventh International Conference on Learning Representations },
13 year={2023},
14 url={https://openreview.net/forum?id=PqvMRDCJT9t}
15}
16