title: Autoregressive Image Generation with Randomized Parallel Decoding
summary: We introduce ARPG, a novel visual autoregressive model that enables
  randomized parallel generation, addressing the inherent limitations of
  conventional raster-order approaches, which hinder inference efficiency and
  zero-shot generalization due to their sequential, predefined token generation
  order. Our key insight is that effective random-order modeling necessitates
  explicit guidance for determining the position of the next predicted token. To
  this end, we propose a novel guided decoding framework that decouples
  positional guidance from content representation, encoding them separately as
  queries and key-value pairs. By directly incorporating this guidance into the
  causal attention mechanism, our approach enables fully random-order training
  and generation, eliminating the need for bidirectional attention. Consequently,
  ARPG readily generalizes to zero-shot tasks such as image inpainting,
  outpainting, and resolution expansion. Furthermore, it supports parallel
  inference by concurrently processing multiple queries using a shared KV cache.
  On the ImageNet-1K 256 benchmark, our approach attains an FID of 1.94 with only
  64 sampling steps, achieving over a 20-fold increase in throughput while
  reducing memory consumption by over 75% compared to representative recent
  autoregressive models at a similar scale.
paper_authors._id: 67d3a952687a7a8a4963a030
paper_authors.name: Haopeng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:56:10.236Z
paper_authors._id: 64e86fbd0c2413c3571ef7a6
paper_authors.avatarUrl: /avatars/960e17ba6a0d03fbd4700cd198adf5af.svg
paper_authors.fullname: Haopeng Li
paper_authors.type: user
paper_authors.user: hp-l33
paper_authors._id: 67d3a952687a7a8a4963a031
paper_authors.name: Jinyue Yang
paper_authors._id: 67d3a952687a7a8a4963a032
paper_authors.name: Guoqi Li
paper_authors._id: 67d3a952687a7a8a4963a033
paper_authors.name: Huan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:36.608Z
paper_authors._id: 62b624f3b52bef716e248fd7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b624f3b52bef716e248fd7/AllcccKH-eBWduA8KVnOQ.png
paper_authors.fullname: Huan Wang
paper_authors.type: user
paper_authors.user: Huan-WhoRegisteredMyName
paper_title: Autoregressive Image Generation with Randomized Parallel Decoding
paper_summary: We introduce ARPG, a novel visual autoregressive model that enables
  randomized parallel generation, addressing the inherent limitations of
  conventional raster-order approaches, which hinder inference efficiency and
  zero-shot generalization due to their sequential, predefined token generation
  order. Our key insight is that effective random-order modeling necessitates
  explicit guidance for determining the position of the next predicted token. To
  this end, we propose a novel guided decoding framework that decouples
  positional guidance from content representation, encoding them separately as
  queries and key-value pairs. By directly incorporating this guidance into the
  causal attention mechanism, our approach enables fully random-order training
  and generation, eliminating the need for bidirectional attention. Consequently,
  ARPG readily generalizes to zero-shot tasks such as image inpainting,
  outpainting, and resolution expansion. Furthermore, it supports parallel
  inference by concurrently processing multiple queries using a shared KV cache.
  On the ImageNet-1K 256 benchmark, our approach attains an FID of 1.94 with only
  64 sampling steps, achieving over a 20-fold increase in throughput while
  reducing memory consumption by over 75% compared to representative recent
  autoregressive models at a similar scale.
paper_ai_summary: ARPG, a visual autoregressive model with randomized parallel generation, improves inference efficiency and zero-shot generalization by decoupling positional guidance and content representation into queries and key-value pairs within a causal attention mechanism.
paper_ai_keywords: visual autoregressive model randomized parallel generation raster-order approaches zero-shot generalization guided decoding framework causal attention mechanism ImageNet-1K 256 benchmark FID sampling steps throughput memory consumption