Parallelized Autoregressive Visual Generation

Comparison of different parallel generation strategies. Both strategies generate initial tokens [1,2,3,4] sequentially then generate multiple tokens in parallel per step, following the order [5a-5d] to [6a-6d] to [7a-7d], etc. (a) Our approach generates weakly dependent tokens across non-local regions in parallel, preserving coherent patterns and local details. (b) The naive method generates strongly dependent tokens within local regions simultaneously, while independent sampling for strongly correlated tokens can cause inconsistent generation and disrupted patterns, such as distorted tiger faces and fragmented zebra stripes.

Illustration of our parallel generation process. Stage 1: sequential generation of initial tokens (1-4) for each region (separated by dotted lines) to establish global structure. Stage 2: parallel generation at aligned positions across different regions (e.g., 5a-5d), then moving to next aligned positions (6a-6d, 7a-7d, etc.) for parallel generation. Same numbers indicate tokens generated in the same step, and letter suffix (a,b,c,d) denotes different regions .

Overview of our parallel autoregressive generation framework. (a) Model implementation. The model first generates initial tokens sequentially [1,2,3,4], then uses learnable tokens [M1,M2,M3] to help transition into parallel prediction mode. (b) Comparison of visible context between our parallel prediction approach (left) and traditional single-token prediction (right).