SiT

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SiT is an open-source PyTorch implementation for exploring flow and diffusion-based generative models using Scalable Interpolant Transformers. It provides model definitions, pre-trained weights, and training/sampling code for generative AI research.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SiT?

SiT (Scalable Interpolant Transformers) offers an official PyTorch implementation for exploring advanced generative models. Built on the foundation of Diffusion Transformers (DiT), SiT introduces an interpolant framework that allows for flexible connections between distributions, surpassing DiT's performance on the conditional ImageNet 256x256 benchmark with identical backbones and parameters. This repository includes pre-trained class-conditional SiT models, a training script utilizing PyTorch DDP, and sampling code with various configurable options for ODE and SDE samplers. Researchers and developers can leverage SiT to experiment with discrete vs. continuous time learning, different model predictions, interpolant choices, and deterministic or stochastic sampling strategies.

Best used for

Ideal for AI researchers and developers who need to implement and experiment with advanced generative models, train custom SiT models on large datasets, and evaluate their performance using metrics like FID scores. Especially valuable for those exploring the nuances of interpolant models and diffusion-based generative AI.

Common actions

train generative models

sample images

evaluate model performance

explore diffusion models

collaborationopen-sourcedeepfakelow-code/no-codeworkflowsautomated workflowgithub copilotface swapping"AI Agents"

Capabilities

Key features

PyTorch implementation
Pre-trained SiT models
Training scripts (DDP)
Configurable sampling options
Likelihood calculation

Target Audience

AI researcherdeveloper

Integrations

wandb

Pricing & Plans

Open Source

Free

FAQs

What kind of generative models does SiT explore?

SiT explores flow and diffusion-based generative models, specifically focusing on Scalable Interpolant Transformers. It allows for flexible connections between distributions, offering a modular study of various design choices in generative AI built on dynamical transport.

Can I train my own SiT models using this repository?

Yes, the repository provides a training script (train.py) that supports distributed data parallel (DDP) training. You can launch SiT training with multiple GPUs and configure various interpolant settings and model predictions.

How can I sample images from pre-trained SiT models?

You can sample from pre-trained SiT models using the sample.py script. It allows you to adjust sampler configurations (ODE & SDE), sampling steps, and classifier-free guidance scale. Weights for pre-trained models are automatically downloaded.

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This tool also appears in

AI Agents & Automation › AI Frameworks & Infra Content & Design › Image Generation

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