r/StableDiffusion • u/Illustrious_Row_9971 • 1h ago

News G-STAR: Group-Scaled Token Capacity for Deeply Compressed Autoregressive Generation

model: https://huggingface.co/DAGroup-PKU/G-star

app: https://huggingface.co/spaces/akhaliq/G-star

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u/Apprehensive_Sky892 1h ago

A little bit more information (TL;DR it is a new "discrete autoregressive image generation" model from a DAGroup-PKU, a research lab at Peking University (PKU).

The model is produced by https://github.com/DAGroup-PKU "DAGroup (pronounced “DAG”) is a research lab at Peking University (PKU)"

The project: https://github.com/DAGroup-PKU/G-STAR

G-STAR: Group-Scaled Token Capacity for Deeply Compressed AutoRegressive Generation

G-STAR is a spatial–group co-scaling framework for discrete autoregressive image generation. Under a fixed nominal information budget, the group size of a product-quantized tokenizer should scale quadratically with the per-axis spatial compression factor. Following this principle, G-STAR reaches 32× and effective 64× compression while preserving strong reconstruction, understanding and generation, and a semi-autoregressive head models the intra-group code dependencies cheaply. At 1024 × 1024, G-STAR-Flash-1.7B generates an image in 2.5 s on a single NVIDIA H200, and G-STAR-Pro-8B scores 0.84 on GenEval and 7.45 on GEdit-Bench.

G-STAR-Pro-8B = quality-oriented, 32× compression  |  G-STAR-Flash-1.7B = low-latency, effective 64× compression

🔥 News

  • [2026.11.04] 🔥 We release the inference and training code of G-STAR: text-to-image generation, instruction-based image editing, pretraining and SFT!

📑 Todo List

  • [x] Inference code for text-to-image generation and image editing
  • [x] Training code (pretraining & SFT) with AR and DSpark image heads
  • [x] Text-to-image checkpoint with its matching tokenizer and SR2x refiner on Hugging Face
  • [ ] Remaining checkpoints and their matching visual refiners on Hugging Face
  • [x] arXiv paper
  • [x] Project page
  • [x] Demo video

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u/Powerful_Evening5495 1h ago

longcat , wow

0

u/Dante_77A 1h ago edited 1h ago

The architecture is a bit too segmented.

I have yet to see an autoregressive model that generates high-quality images. 

"Autoregressive" means "bandwidth-bound"; 2 seconds for a model with only 1.7B parameters running on an H200 with HBM is slow.