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Ddpm Ema Celebahq 256

Developed by google
DDPM is a high-quality image generation model based on diffusion probabilistic models, inspired by non-equilibrium thermodynamics, which generates images through a progressive denoising process.
Downloads 36.98k
Release Time : 7/19/2022

Model Overview

This model utilizes diffusion probabilistic methods to achieve unconditional image generation, supports multiple noise schedulers, and performs excellently on CIFAR10 and LSUN datasets.

Model Features

High-quality image generation
Achieves state-of-the-art Inception and FID scores on CIFAR10 and LSUN datasets.
Progressive decompression
Supports a progressive lossy decompression scheme, which can be viewed as a generalization of autoregressive decoding.
Multiple scheduler support
Provides three noise schedulers: DDPM, DDIM, and PNDM, balancing generation quality and speed.

Model Capabilities

Unconditional image generation
High-quality face synthesis
Progressive image denoising

Use Cases

Creative content generation
Facial image generation
Generate high-fidelity celebrity facial images
Generate high-quality face samples at 256x256 resolution
Data augmentation
Training data expansion
Generate additional training samples for computer vision tasks
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