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DiT (Diffusion Transformer) is a powerful architecture that applies transformer-based modeling directly to diffusion generative processes for high-quality image synthesis. Unlike CNN-based diffusion models, DiT represents the diffusion process in the latent space and processes image tokens through transformer blocks with learned positional encodings, offering scalability and superior sample quality. The model architecture parallels large language models but for image tokens—each block refines noisy latent representations toward cleaner outputs through iterative denoising steps. DiT achieves strong results on benchmarks like ImageNet and LSUN while being architecturally simple and highly modular. It supports variable resolution, conditioning on class or text embeddings, and integration with latent autoencoders (like those used in Stable Diffusion).
Features
- Transformer-based architecture for diffusion image generation
- Iterative denoising with token-wise refinement and attention-based context modeling
- Operates in latent space for efficient high-resolution synthesis
- Supports conditioning on class labels or text embeddings
- Pretrained weights, training code, and visualization utilities for diffusion trajectories
- Modular design enabling easy scaling and hybrid integrations with latent autoencoders
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