Papers › Autoregressive Image Generation with Randomized Parallel Decoding

Autoregressive Image Generation with Randomized Parallel Decoding

13 Mar 2025arXiv:2503.10568archive 2025-07-28

Haopeng Li, Jinyue Yang, Guoqi Li, Huan Wang

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.

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Normalize hp-l33/ARPG/models/vq_model.py official repository ran · our draft was wrong MIT (permissive) · 01b5cf639b6bac89 · report
adjust_learning_rate hp-l33/ARPG/train_c2i.py official repository ran MIT (permissive) · 3a774664bfc712ec · report
build_t2i hp-l33/ARPG/dataset/t2i.py official repository ran MIT (permissive) · 02cb09299c18ee5d · report
build_t2i_code hp-l33/ARPG/dataset/t2i.py official repository ran MIT (permissive) · ff9ed4675cb307aa · report
build_t2i_image hp-l33/ARPG/dataset/t2i.py official repository ran MIT (permissive) · 090d7b6b1260114e · report
center_crop_arr hp-l33/ARPG/dataset/augmentation.py official repository ran · our draft was wrong MIT (permissive) · 1712a07966b542ee · report
compute_entropy_loss hp-l33/ARPG/models/vq_model.py official repository ran · fixture could not drive it MIT (permissive) · 774c95b77d7621f3 · report
creat_optimizer hp-l33/ARPG/train_c2i.py official repository ran · our draft was wrong MIT (permissive) · ba65976c1d96a7cb · report
create_npz_from_sample_folder hp-l33/ARPG/sample_c2i_ddp.py official repository ran · our draft was wrong MIT (permissive) · 7b21a01ae77703a3 · report
find_multiple hp-l33/ARPG/models/arpg.py official repository ran · honoured contract fingerprinted MIT (permissive) · f6ff7671338c9c92 · report
nonlinearity hp-l33/ARPG/models/vq_model.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 3137073275f8c21a · report
batch_seq_shuffle hp-l33/ARPG/models/arpg.py official repository unverified MIT (permissive) · 69acd83901569c6a · report
build_coco hp-l33/ARPG/dataset/coco.py official repository unverified MIT (permissive) · a5dc3fcd09ebc843 · report
build_imagenet_code hp-l33/ARPG/dataset/imagenet.py official repository unverified MIT (permissive) · c5aec8b86d5fb5f7 · report
build_openimage hp-l33/ARPG/dataset/openimage.py official repository unverified MIT (permissive) · 0a65aaf586d4f90b · report
create_deepspeed_config hp-l33/ARPG/utils/deepspeed.py official repository unverified MIT (permissive) · a2c47fee1f9960c8 · report
precompute_freqs_cis hp-l33/ARPG/models/arpg.py official repository unverified MIT (permissive) · 0f0ff4e443413018 · report
random_crop_arr hp-l33/ARPG/dataset/augmentation.py official repository unverified MIT (permissive) · 980fb099f82d8b84 · report

Tasks

Conditional Image GenerationImage GenerationImage InpaintingZero-shot Generalization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation ImageNet 256x256 ARPG-XXL FID 1.94 #44 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 ARPG-XL FID 2.1 #51 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 ARPG-L FID 2.44 #59 of 94 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionSoftmax

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