SonyResearch/micro_diffusion

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1594 stars · 57 forks observed · 2026-08-28

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

Official implementation of Sony Research's micro-budget approach to training large-scale text-to-image diffusion transformer models from scratch. It trains a 1.16B parameter sparse transformer for about $1,890 using patch masking, mixture-of-experts layers, and a mix of real and synthetic images.

Use cases

• train a text-to-image diffusion model on a small budget
• reproduce low-cost diffusion transformer training from a paper
• generate images with a pretrained micro-budget DiT checkpoint
• experiment with patch masking for diffusion training
• train diffusion models on a small image dataset subset
• fine-tune a diffusion model from 256x256 to 512x512 resolution

When to choose

• you want to train or fine-tune a text-to-image diffusion model with limited GPU budget
• you need reproducible research code for micro-budget diffusion training
• you want to study patch masking and mixture-of-experts in DiT models

When to avoid

• you need a production-ready image generation API or polished inference tool
• you lack multi-GPU hardware (reference setup uses 8x H100)
• you want general-purpose diffusion fine-tuning of existing models like Stable Diffusion rather than training from scratch

Facets

library · maturity active

deep-learning machine-learning image-processing llm-training deep-learning machine-learning image-processing artificial-intelligence python diffusion-models text-to-image micro-budget-training patch-masking mixture-of-experts dit research-code image-generation gpu linux

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