DDPM-Cat-256 Open-Source Image Generation Model - Free Deployment for High-Quality Unconditional Image Generation

Ddpm Cat 256

Developed by google

High-quality image generation model based on diffusion probabilistic models, excelling in unconditional image generation tasks

Image Generation Open Source License:Apache-2.0 #High-quality image generation #Progressive denoising #Unconditional image synthesis

Downloads 2,658

Release Time : 7/19/2022

Model Overview

DDPM is a latent variable model inspired by non-equilibrium thermodynamics, generating high-quality images through a progressive denoising process and supporting unconditional image generation tasks

Model Features

High-quality image generation

Achieves state-of-the-art Inception and FID scores on datasets like CIFAR10 and LSUN

Progressive denoising

The generation process through step-by-step denoising can be viewed as a generalized form of autoregressive decoding

Multi-scheduler support

Supports various noise schedulers including DDPM, DDIM, and PNDM, allowing flexible choices between quality and speed

Model Capabilities

Unconditional image generation

Progressive image synthesis

High-resolution image generation

Use Cases

Creative content generation

Artistic image creation

Generate artistic-style cat images

Produces high-quality 256x256 resolution images

Data augmentation

Training data expansion

Generate additional training samples for computer vision tasks

🚀 Denoising Diffusion Probabilistic Models (DDPM)

This project presents high - quality image synthesis results using diffusion probabilistic models, offering a new approach to unconditional image generation.

🚀 Quick Start

Inference

DDPM models can use discrete noise schedulers such as:

Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade - off between quality and inference speed you might want to consider the ddim or pndm schedulers instead.

Training

If you want to train your own model, please have a look at the official training example

✨ Features

Paper and Authors

Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel

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 Examples

Basic Usage

# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline

model_id = "google/ddpm-cat-256"

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference

# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]

# save image
image.save("ddpm_generated_image.png")

Advanced Usage

For more in - detail information, please have a look at the official inference example

📦 Samples

📄 License

This project is licensed under the Apache - 2.0 license.

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