Overview of TransGAN and examples of algorithms and implementations

Overview of TransGAN

TransGAN is the first generative adversarial network (GAN) in the world to be built entirely on a pure Transformer architecture, without relying on any convolutional neural networks (CNNs).

• Paper title: TransGAN: Two Transformers Can Make One Strong GAN

• Authors: Yifan Jiang et al.

• Conference: NeurIPS 2021

• Key feature: A GAN composed solely of Transformers, without any use of CNNs

Traditionally, most GANs have been based on CNNs, as local convolutional operations were considered essential for effective image generation. TransGAN breaks this convention by demonstrating that self-attention mechanisms alone can achieve high-quality image generation, drawing significant attention in the research community.

The architecture of TransGAN consists of two main Transformer-based components: a Generator and a Discriminator.

The Generator takes a noise vector sampled from a standard Gaussian distribution as input. This vector is transformed into patch-like feature tokens, which are then passed through multiple Transformer encoder blocks to learn the image structure. The output tokens are finally merged and linearly projected to produce an image.

The Discriminator, on the other hand, divides the input image into small patches, treats each as a token, and processes them using a structure similar to a Vision Transformer (ViT) to determine whether the image is real or fake.

The architecture incorporates several Transformer-specific innovations, including:

• Patch-based input representation (as in ViT)

• Positional encoding to preserve spatial information

• Stabilization techniques such as Layer Normalization and GELU

• Optimizations for data-efficient training, enabling learning from limited datasets

• Stabilization mechanisms for GAN training, including spectral normalization, tailored loss functions, and warm-up schedules

In terms of performance, TransGAN has been evaluated on benchmark datasets such as CIFAR-10, CelebA, and LSUN, achieving results comparable to or even surpassing traditional CNN-based models like DCGAN and StyleGAN. This validates that Transformers alone are capable of high-quality image synthesis, marking a pivotal moment in generative modeling.

TransGAN is also notable for its relationship to other Transformer-based models such as ViT, GANformer, and non-GAN models like Image GPT and DALL·E. Among these, TransGAN stands out for pioneering the intersection of Self-Attention and image generation, establishing itself as a foundational work in the evolution of Transformer-based generative models.

1. Environment Setup (PyTorch)

Before implementing TransGAN, ensure the following Python environment is properly configured:

Recommended Environment

• Python 3.8 or later

• PyTorch 1.9 or later

• CUDA 10.2+ (for GPU acceleration, optional but recommended)

• Development environment: VSCode, Jupyter Notebook, or Colab

1. environment construction (PyTorch)

pip install torch torchvision

2. simple implementation of Generator (based on Transformer)

import torch
import torch.nn as nn

class TransformerGenerator(nn.Module):
    def __init__(self, latent_dim=128, img_size=32, patch_size=4, dim=256, depth=6, heads=4):
        super().__init__()
        num_patches = (img_size // patch_size) ** 2
        self.patch_size = patch_size
        self.img_size = img_size
        self.dim = dim

        self.latent_proj = nn.Linear(latent_dim, num_patches * dim)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=dim, nhead=heads), num_layers=depth
        )
        self.out_proj = nn.Linear(dim, patch_size * patch_size * 3)

    def forward(self, z):  # z: (batch, latent_dim)
        batch = z.shape[0]
        tokens = self.latent_proj(z).view(batch, -1, self.dim)  # (batch, N, dim)
        tokens = self.transformer(tokens)                       # (batch, N, dim)
        patches = self.out_proj(tokens)                         # (batch, N, patch_pixels)
        patches = patches.view(batch, self.img_size, self.img_size, 3).permute(0, 3, 1, 2)
        return patches  # (batch, 3, H, W)

3. simple implementation of Discriminator (ViT style)

class TransformerDiscriminator(nn.Module):
    def __init__(self, img_size=32, patch_size=4, dim=256, depth=6, heads=4):
        super().__init__()
        num_patches = (img_size // patch_size) ** 2
        self.patch_embed = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=dim, nhead=heads), num_layers=depth
        )
        self.classifier = nn.Linear(dim, 1)

    def forward(self, x):  # x: (batch, 3, H, W)
        patches = self.patch_embed(x)                    # (batch, dim, H', W')
        tokens = patches.flatten(2).transpose(1, 2)      # (batch, N, dim)
        out = self.transformer(tokens)                   # (batch, N, dim)
        global_token = out.mean(dim=1)                   # (batch, dim)
        return self.classifier(global_token).squeeze(1)  # (batch,)

4. training overview (pseudo code)

G = TransformerGenerator()
D = TransformerDiscriminator()

z = torch.randn(64, 128)
fake_images = G(z)
real_images = next(iter(data_loader))  # CIFAR-10など

# discriminator loss
real_score = D(real_images)
fake_score = D(fake_images.detach())
loss_D = -torch.mean(real_score) + torch.mean(fake_score)

# generator loss
loss_G = -torch.mean(D(fake_images))

Application Examples

Below are specific application examples of TransGAN (Transformer-based GAN):

1. Low-Resolution Image Generation (e.g., CIFAR-10, CelebA)

• Targets unconditional generation of low-resolution images (32×32 to 64×64).

• Successfully generates natural images without using any CNN.

• Applied to datasets such as CIFAR-10 and CelebA.

• Used as an ablation benchmark to eliminate CNN dependency.

2. Benchmarking Transformer-Based Architectures

• Evaluates the effectiveness of Transformer architectures for image generation.

• Serves as a baseline for comparison with models like ViTGAN, GANformer, and Styleformer.

• Tests how far generation quality can go without relying on CNN’s inductive biases.

3. Visualization and Interpretability of Generation

• Utilizes attention mechanisms to visually interpret the generation process.

• Potentially less of a black box than traditional CNN-based models.

• Applied in domains like medical imaging and anomaly detection for reliability evaluation.

4. Unified Use with General Transformer Models

• Enables unified Transformer-based construction of both generation (TransGAN) and classification (ViT).

• Promotes modular reusability across generation, classification, and transformation tasks.

• Pretrained Transformer modules can be fine-tuned for further applications.

5. Foundation for New GAN Architecture Design

• Serves as a foundation for CNN-free generative model research.

• Opens pathways for integration with other models like UNet and Diffusion models.

• Used as an experimental platform for extended models like TransGAN2 and StyleSwin for high-resolution generation.

6. Evaluation of Generation Quality and Representation Learning

• Supports evaluation using metrics like Inception Score (IS) and Frechet Inception Distance (FID).

• Achieved FID ≈ 8.6 on CIFAR-10 with TransGAN-128.

• Demonstrates significantly better performance than traditional CNN-based models like DCGAN (FID ≈ 26).

References

Below is a curated list of references related to TransGAN (Transformer-based GAN), categorized by original paper, related studies, foundational theory, and implementation resources:

1. Original Paper (TransGAN Core)

Jiang, Yifan, et al. (2021)
Title: TransGAN: Two Transformers Can Make One Strong GAN

• The first GAN composed entirely of Transformers without using any CNNs

• Achieved high-quality image generation on datasets like CIFAR-10 and CelebA
  🔗 GitHub Repository

2. Related Research (Transformer × GAN)

Hudson & Zitnick (2021)
Title: Generative Adversarial Transformers

• Combines self-attention and cross-attention in a Transformer-GAN hybrid architecture (CNN included)

Touvron et al. (2020)
Title: DeiT: Data-efficient Image Transformers

• Lightweight training technique for Vision Transformers (ViT)

• Influenced the design of TransGAN’s Discriminator

3. Foundational Theory / Technical Background

Dosovitskiy, Alexey, et al. (2020)
Title: An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale

• The foundational ViT paper, introducing patch tokenization and positional embeddings
  🔗 arXiv:2010.11929

Goodfellow, Ian, et al. (2014)
Title: Generative Adversarial Nets

• The original GAN paper, which TransGAN reimagines using Transformer architectures
  🔗 NIPS 2014 PDF

4. Implementation Resources & Tutorials

TransGAN GitHub

• Official PyTorch implementation of TransGAN

Hugging Face Blog

• Tutorial and overview of Vision Transformer applications

Papers with Code

• Benchmark scores and performance metrics for TransGAN