license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.
Usage
# !pip install diffusers
from diffusers import DiffusionPipeline
model_id = "google/ddpm-celebahq-256"
# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)
# run pipeline in inference (sample random noise and denoise)
images = ddpm()["sample"]
# save image
images[0].save("ddpm_generated_image.png")
Samples
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