Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive
  Text-to-image Generation
Authors: name: Yao Teng, name: Fuyun Wang, name: Xian Liu, name: Zhekai Chen, name: Han Shi, name: Yu Wang, name: Zhenguo Li, name: Weiyang Liu, name: Difan Zou, name: Xihui Liu

As a new paradigm of visual content generation, autoregressive text-to-image
models suffer from slow inference due to their sequential token-by-token
decoding process, often requiring thousands of model forward passes to generate
a single image. To address this inefficiency, we propose Speculative
Jacobi-Denoising Decoding (SJD2), a framework that incorporates the denoising
process into Jacobi iterations to enable parallel token generation in
autoregressive models. Our method introduces a next-clean-token prediction
paradigm that enables the pre-trained autoregressive models to accept
noise-perturbed token embeddings and predict the next clean tokens through
low-cost fine-tuning. This denoising paradigm guides the model towards more
stable Jacobi trajectories. During inference, our method initializes token
sequences with Gaussian noise and performs iterative
next-clean-token-prediction in the embedding space. We employ a probabilistic
criterion to verify and accept multiple tokens in parallel, and refine the
unaccepted tokens for the next iteration with the denoising trajectory.
Experiments show that our method can accelerate generation by reducing model
forward passes while maintaining the visual quality of generated images.