T2I Models Explained,
Diffusion-GAN

Diffusion-GAN is a GAN framework that uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise. It has three components, an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator that allows it to produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.

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An Overview of Diffusion-GAN

Diffusion-GAN is a GAN framework that uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise.

Overcoming the ineffectiveness of adding instance noise

Forward Diffusion Chain

Diffusion-GAN uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise for GAN training.

Authors establish the theoretical basis for the consistency

True Data Distribution

Discriminator's timestep-dependent strategy guides the generator to match the true data distribution.

Diffusion-GAN outperforms strong GAN models

State of the Art GANs

Diffusion-GAN outperforms strong GAN models on different datasets by producing more realistic images.

Introduction
Key Highlights
Training Details
Key Results
Business Applications
Model Features
Model Tasks
Fine-tuning
Benchmarking
Sample Codes
Limitations
Other LLMs

About Model

Diffusion-GAN is a GAN framework that uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise. It has three components, an adaptive diffusion process, a diffusion time step-dependent discriminator, and a generator that allows it to produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.

Research Paper

Model Repository

Github

Papers with Code

Key highlights

Highlights of the Diffusion-GAN model are:

Diffusion-GAN is a GAN framework that uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise.
It has three components: an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator.
The same adaptive diffusion process diffuses both the observed and generated data, and there is a different noise-to-data ratio at each diffusion timestep.
The timestep-dependent discriminator learns to distinguish the diffused real data from the diffused generated data.
The generator learns from the discriminator’s feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels.
The discriminator’s timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution.
The Diffusion-GAN model outperforms strong GAN baselines on various datasets.
It can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.

Training Details

Training data

Diffusion-GAN is evaluated on multiple image datasets, including CIFAR-10, LSUN, and ImageNet.

Training dataset size

The training dataset sizes used in the experiments vary from 10,000 to 1.28 million images, depending on the dataset.

Training Procedure

Diffusion-GAN trains the generator with feedback from a discriminator by adding noise to data over time.

Training time and resources

The training time varies depending on the dataset and the size of the generator and discriminator.

Key Results

Diffusion-GAN is a GAN framework that uses a forward diffusion chain to generate Gaussian-mixture distributed instance noise.

Task	Dataset	Score
Image Generation	CelebA 64x64	1.69
Image Generation	CIFAR-10 32x32	3.19
Image Generation	STL-10 64x64	11.43
Image Generation	LSUN-Bedroom 256 × 256	3.65
Image Generation	LSUN-Church 256 × 256	3.17
Image Generation	FFHQ 1024 × 1024	2.83

Business Applications

This table provides a quick overview of how Diffusion-GAN can streamline various business operations relating to image generation.

Tasks	Business Use Cases	Examples
Image Synthesis	Product image generation	Generating realistic product images for e-commerce
	Virtual try-on	Creating virtual try-on platforms for fashion brands
	Augmented reality applications	Generating augmented reality images for marketing
	Gaming industry	Generating realistic gaming backgrounds and characters
	Fashion industry	Creating high-quality images for fashion lookbooks
Image Classification	Object detection	Identifying and classifying objects in images and videos
	Autonomous driving	Detecting and classifying objects for autonomous vehicles
	Healthcare	Analyzing medical images for disease detection and diagnosis
	Surveillance	Detecting and identifying objects and individuals for security
	Agriculture and Environmental Monitoring	Analyzing images to monitor crop growth and environmental changes

Model Features

Diffusion-GAN is a GAN framework with several features, including:

Forward Diffusion Chain

Diffusion-GAN leverages a forward diffusion chain to generate Gaussian-mixture distributed instance noise. The same adaptive diffusion process diffuses both the observed and generated data.

Adaptive Diffusion Process

Diffusion-GAN uses an adaptive diffusion process to control the noise-to-data ratio at each diffusion step.

Timestep-Dependent Discriminator

The discriminator is designed to distinguish the diffused real data from the diffused generated data at each diffusion step. The timestep-dependent strategy of the discriminator gives consistent and helpful guidance to the generator, enabling it to match the true data distribution.

Generator Feedback

The generator learns from the discriminator's feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels.

Theoretical Foundation

Diffusion-GAN has a theoretical foundation that shows that the discriminator's timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution.

Performance

Diffusion-GAN outperforms state-of-the-art GANs on various datasets, showing that it can produce more realistic images with higher stability and data efficiency.

Model Tasks

Image Synthesis

Diffusion-GAN can generate realistic 32x32 images using the CIFAR-10 dataset, which contains 60,000 color images divided into ten classes. Using CelebA, Diffusion-GAN uses the CelebA dataset, which contains 202,599 images annotated with 40 binary attributes, to generate high-quality human face images with 1024x1024 resolution. Diffusion-GAN can generate high-quality face images with a resolution of 1024x1024 using the FFHQ dataset, which contains 70,000 images of human faces.

Image Classification

By fine-tuning it for image classification using the features extracted from the generator, Diffusion-GAN can also perform image classification tasks using the STL-10 dataset, which consists of 10 classes of 96x96 resolution images. Diffusion-GAN uses the LSUN dataset, which contains over one million images divided into 20 scene categories, to generate diverse, high-quality images of scenes and objects. Diffusion-GAN uses the AFHQ dataset to generate high-quality animal images, which include images of cats, dogs, and wild animals.

Fine-tuning

The authors haven't explicitly mentioned about fine-tuning methods in the research paper. The fine-tuning methods will be updated here soon.

Sample Codes

Here's a sample code to run Diffusion-GAN on GPU using PyTorch

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from diffusion_gan import DiffusionGAN

# Define the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Define the dataset
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])

train_set = datasets.MNIST('data/', train=True, download=True, transform=transform)
train_loader = DataLoader(train_set, batch_size=128, shuffle=True)

# Define the model
model = DiffusionGAN().to(device)

# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=1e-4)

# Train the model
for epoch in range(10):
for i, (x_real, _) in enumerate(train_loader):
x_real = x_real.to(device)

loss = model(x_real)

optimizer.zero_grad()
loss.backward()
optimizer.step()

print(f"Epoch [{epoch+1}/{10}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item()}")

# Save the model
torch.save(model.state_dict(), "diffusion_gan.pth")

Other LLMs

PFGM++

PFGM++ is a family of physics-inspired generative models that embeds trajectories for N dimensional data in N+D dimensional space using a simple scalar norm of additional variables.

MDT-XL2

MDT proposes a mask latent modeling scheme for transformer-based DPMs to improve contextual and relation learning among semantics in an image.

Stable Diffusion

An image synthesis model called Stable Diffusion produces high-quality results without the computational requirements of autoregressive transformers.

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T2I Models Explained,Diffusion-GAN

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of Diffusion-GAN

Overcoming the ineffectiveness of adding instance noise

Forward Diffusion Chain

Authors establish the theoretical basis for the consistency

True Data Distribution

Diffusion-GAN outperforms strong GAN models

State of the Art GANs

About Model

Key highlights

Training Details

Training data

Training dataset size

Training Procedure

Training time and resources

Key Results

Business Applications

Model Features

Forward Diffusion Chain

Adaptive Diffusion Process

Timestep-Dependent Discriminator

Generator Feedback

Theoretical Foundation

Performance

Model Tasks

Image Synthesis

Image Classification

Fine-tuning

Benchmark Results

Sample Codes

Here's a sample code to run Diffusion-GAN on GPU using PyTorch

Model Limitations

Other LLMs

PFGM++

MDT-XL2

Stable Diffusion

T2I Models Explained,
Diffusion-GAN