willisma/SiT

Health v2 · maintenance only

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1206 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of Scalable Interpolant Transformers (SiT), a family of generative models built on Diffusion Transformers that use flexible interpolant frameworks connecting distributions. Includes model definitions, pre-trained ImageNet 256x256 checkpoints, and DDP training/sampling scripts.

Use cases

• generate images with a pretrained interpolant transformer
• train a flow-based generative model on ImageNet
• compare flow and diffusion generative model design choices
• sample class-conditional images from pretrained checkpoints
• research interpolant frameworks for generative modeling
• reproduce FID-50K 2.06 benchmark results

When to choose

• you want state-of-the-art class-conditional ImageNet generation with a DiT-style backbone
• you need a flexible interpolant framework to study flow vs diffusion design choices
• you want pretrained checkpoints for image generation experiments

When to avoid

• you need text-to-image generation rather than class-conditional ImageNet synthesis
• you lack GPU resources for training large transformer models
• you need a production-ready image generation service rather than research code

Facets

library · maturity active

machine-learning deep-learning image-processing deep-learning machine-learning image-processing artificial-intelligence python cross-platform diffusion-models flow-matching interpolant-transformers generative-models pytorch image-generation research-code dit gpu linux

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