LTH14/JiT

Health v2 · maintenance only

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2507 stars · 169 forks observed · 2026-08-28

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

A PyTorch/GPU re-implementation of JiT (Just image Transformer), a minimalist pixel-space diffusion model for high-resolution image generation from the paper 'Back to Basics: Let Denoising Generative Models Denoise'. It includes training and evaluation scripts for ImageNet at 256x256 and 512x512 resolutions.

Use cases

• train a pixel-space diffusion transformer on ImageNet
• generate high-resolution images with a diffusion model
• reproduce the JiT paper results in PyTorch
• experiment with denoising generative models
• train image generation models on multiple GPUs

When to choose

• you want a simple, self-contained PyTorch implementation of pixel-space diffusion
• you need to reproduce or build on the JiT paper
• you have multi-GPU resources for training image generation models

When to avoid

• you need a production-ready image generation API
• you lack the GPU resources to train large diffusion models
• you want latent diffusion rather than pixel-space diffusion

Facets

library · maturity active

deep-learning machine-learning image-processing deep-learning machine-learning image-processing python diffusion-models image-generation pytorch research-code transformer pixel-space-diffusion gpu

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