We introduce DC-AR, a novel masked autoregressive (AR) text-to-image generation framework that delivers superior image generation quality with exceptional computational efficiency. Due to the tokenizers' limitations, prior masked AR models have lagged behind diffusion models in terms of quality or efficiency. We overcome this limitation by introducing DC-HT - a deep compression hybrid tokenizer for AR models that achieves a 32× spatial compression ratio while maintaining high reconstruction fidelity and cross-resolution generalization ability. Building upon DC-HT, we extend MaskGIT and create a new hybrid masked autoregressive image generation framework that first produces the structural elements through discrete tokens and then applies refinements via residual tokens. DC-AR achieves state-of-the-art results with a gFID of 5.49 on MJHQ-30K and an overall score of 0.69 on GenEval, while offering 1.5-7.9× higher throughput and 2.0-3.5× lower latency compared to prior leading diffusion and autoregressive models.

Download the github repo and install the environment:

Download DC-HT and DC-AR

Download the safety check model:

Note: We use ShieldGemma-2B from Google DeepMind to filter out unsafe prompts in our demo. We strongly recommend using it if you are distributing our demo publicly.

You may launch the Gradio demo using the following script: