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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching

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arxiv 2412.15205 v1 pith:L62XB75G submitted 2024-12-19 cs.CV

Sucheng Ren , Qihang Yu , Ju He , Xiaohui Shen , Alan Yuille , Liang-Chieh Chen This is my paper
classification cs.CV
keywords scalenextpredictionflowarautoregressivedesigngenerationimage
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Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive modeling, which extends the next token prediction to the next scale prediction, preserving the 2D structure of images. However, VAR encounters two primary challenges: (1) its complex and rigid scale design limits generalization in next scale prediction, and (2) the generator's dependence on a discrete tokenizer with the same complex scale structure restricts modularity and flexibility in updating the tokenizer. To address these limitations, we introduce FlowAR, a general next scale prediction method featuring a streamlined scale design, where each subsequent scale is simply double the previous one. This eliminates the need for VAR's intricate multi-scale residual tokenizer and enables the use of any off-the-shelf Variational AutoEncoder (VAE). Our simplified design enhances generalization in next scale prediction and facilitates the integration of Flow Matching for high-quality image synthesis. We validate the effectiveness of FlowAR on the challenging ImageNet-256 benchmark, demonstrating superior generation performance compared to previous methods. Codes will be available at \url{https://github.com/OliverRensu/FlowAR}.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. Beyond Token-Level Cross-Entropy: Fr\'echet Distributional Post-Training for Autoregressive Image Generation

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  3. Amortized Moment Matching for Visual Generation

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    Amortized Fréchet Distance uses neural nets to match conditional means and covariances, yielding stronger one-step visual generators than explicit FD-loss or multi-step teachers.

  4. Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis

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    DisCon treats discrete image tokens as conditioning signals rather than targets, letting a continuous autoregressive model refine details and reach gFID 1.38 on ImageNet-256.

  5. DiSA: Diffusion Step Annealing in Autoregressive Image Generation

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    Autoregressive image diffusion models can use far fewer denoising steps for later tokens without losing quality, yielding 1.4-2.5x speedup from step annealing and up to 10x when combined with fewer autoregressive steps.

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    Decision Flow frames the gradual action generation of flow-based policies as a flow MDP and updates the flow policy with flow-level value functions, reporting state-of-the-art results on several D4RL tasks.

  7. Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance

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  8. Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots

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