FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Authors: Sucheng Ren
Qihang Yu
Ju He
Xiaohui Shen
Alan Yuille
Liang-Chieh Chen
Venue: ICML 2025 poster (2025)
Keywords: Image synthesis and generation
Primary area: deep_learning->generative_models_and_autoencoders

Abstract: 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 is available at \href{https://github.com/OliverRensu/FlowAR}{https://github.com/OliverRensu/FlowAR}.

FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Sucheng Ren
Qihang Yu Ju Hel
Xiaohui Shen
Alan Yuille
Liang- Ching Chen
"Johns Hopkins University
Batee
Abstract
FID
Autoregressive (AR) modeling has achieved re- markable success natural language processing by enabling models to generate text with coher- ence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive model- ing, which extends the next token prediction to the next scale prediction, preserving the 2D struc- ture 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 flexibil- ity in updating the tokenizer. To address these limitations, we introduce FlowAR, general next scale prediction method featuring a streamlined scale design, where each subsequent scale is sim- ply double the previous one. This eliminates the need for VAR's intricate multi-scale resid- ual tokenizer and enables the use of any off-the- shelf Variational AutoEncoder (VAE). Our sim- next
lower is better
VAR
FlowAR (Ours)
-2.10
0.65
3
-1.05
0.32
1.6
~100M
-30M
-600
-2B
Parameters
Figure 1. Performance Comparison. The proposed FlowAR, a general next-scale prediction model enhanced with flow matching, consistently outperforms state-of of-h-of-the-arr VAR (Tian et al., 2024) variants across different model sizes.
allowing for coherent and contextually relevant text gener- ation. Prominent models like GPT-3 (Brown et al., 2020) and its successors (OpenAI, 2022; 2023) have demonstrated remarkable language understanding and generation capabili- ties, setting new standards across diverse NI.P applications.
plified design enhances generalization scale prediction and facilitates the integration of Flow Matching for high-quality image synthesis. We validate the effectiveness of FlowAR on the challenging ImageNet-2256 benchmark, demon- strating superior generation performance com- pared to previous methods. Codes is available at http:///gihbbb.ommmmiivvraas///
Building on the success of autoregressive modeling in NLP, this paradigm has been adapted to computer vision, particu- larly for generating high-fidelity images through sequential content prediction (Esser al., 2021; Yu et al., 2022; Sun et al., 2024). In these approaches, images are discretely tok- enized, with tokens flattened into ID sequences, enabling autoregressive models to generate images token-by-token This approach leverages the sequence modeling strengths of AR architectures to capture intricate visual details. However, directly applying ID token-wise autoregressive methods images presents notable challenges. Images inherently two-dimensional (2D), with spatial dependencies across height and width. Flattening image tokens disrupts this 2D structure, potentially compromising spatial coherence and causing artifacts in generated images. Recently, VAR (Tian et al., 2024) addresses these issues by introducing scale-
1. Introduction
Autoregressive (AR) models have significantly advanced natural language processing (NLP) by modeling the proba- bility distribution of each token given its preceding tokens,
'Johns Hopkins University "ByteDance. Correspondence to: Sucheng Ren <sren196e jh..euu.. t: Work done while at ByteDance.
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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FlowAR: Scale-wis Autoregressive Image Generation Meets Flow Matching
wise autoregressive modeling, which progressively gener- ates images from coarse to fine scales, preserving the spatial hierarchies and dependencies essential for visual coherence. This seale-wise approach allows autoregressive models to retain the 21 structure during image generation, capturing the layered complexity of visual content more naturally.
ent in natural images. The final image is then produced by de- de-tooknizing the predicted latent representation at the finest scale. As shown in Figure 1, Our FRo consistently out- perform VAR under similar parameters. Specifically, with about 2B parameters our FR outperform VAR by 0.32 FID.
Despite its effectiveness, VAR (Tian et al., 2024) faces two significant limitations: (1) a complex and rigid scale design, and (2) a dependency between the generator and a tokenizer that shares this intricate scale structure. Specifically, VAR employs non-uniform scale sequence, 2, 4, 5, 6, 8, 10, 13, 16], where the coarsest scale tok- enizes a 256 x 256 image into a single 1 x 1 token and the finest scale into 16 x 16 tokens. This intricate sequence constrains both the tokenizer and generator to operate ex- clusively at these predefined scales, limiting adaptability to other resolutions or granularities. Consequently, the model struggles to represent or generate features that fall outside this fixed scale sequence. Additionally, the tight coupling between VAR's generator and tokenizer restricts flexibility in independently updating the tokenizer, as both components must adhere to the same scale structure.
The seamless integration of the scale-wise autoregressive Transformer and scale-wise flow matching model enables FlowAR to capture both the sequential and probabilistic aspects of images with multi-scale information, resulting in improved image synthesis performance. We demonstrate FlowAR's s effectiveness on the challenging ImageNet bench- mark (Deng et al., 2009), where it achieves state-of-the-arr results.
2. Related Work
Autoregressive Models. Autoregressive modeling (Radford et al., 2018; 2021; Brown et al., 2020; OpenAl, 2023; Tou- vron et al., 2023a;b; Chowdhery et al., 2023; Team et al., 2023; Ren et al., 2024b;a; 2025b;a; Wang et al., 2024; 2025; Dubey et al., 2024; Bai et al., 2023; Yu et al., 2024a) began in natural language processing, where language Transform- ers (Vaswani, 2017) are pretrained to predict the next word in a sequence. This concept was first introduced to computer vision by PixeICNN (Van den Oord et al., 2016), which uti- lized a CNN-based model to predict raw pixel probabilities. With the advent of Transformers, iGPT (Chen et al., 2020) extended this approach by modeling raw pixels using Trans- former architectures. VQGAN (Esser et al., 2021; Weber et al., 2024) further advanced the field by applying autore- gressive learning within the latent space of VQ-VAE (Van Den Oord et al., 2017), thereby simplifying data represen- tatiown for more efficient modeling (Yu et al., 2024b; Kim et al., 2025). Taking a different direction, Parti (Yu et al., 2022) framed image generation as sequence-to-sequeee task akin machine translation, using sequences of image tokens targets instead of text tokens, and leveraging sig- nificant advancements in large language models through data and model scaling. Llamaaien (Sun et al., 2024) ex- panded on this by applying the traditional "next token pre- diction" paradigm of large language models to visual genera- tion, demonstrating that standard autoregressive models like Llama (Touvron et al., 2023a) can achieve tate-of-theeaar image generation performance when appropriately scaled, even without specific inductive biases for visual signals. The work most similar to ours is VAR (Tian et al., 2024), which transitioned from token-wise to scale-wise autoregressive modeling by developing a coarse-to- next scale predic- tion. However, VAR faces significant challenges due to its complex scale designs and deep dependency on scale resid- ual discrete tokenizer. In contrast, our proposed Flow AR employs simple scale design and maintains compatibility
To address these limitations, we introduce FlowAR, flexi- ble and generalized approach to scale-wise autoregressive modeling for image generation, enhanced with flow match- ing (Lipman et al., 2022). Unlike VAR (Tian et al., 2024), which relics on a complex multi-scale VQGAN discrete tokenizer (Razavi et al., 2019; Lee et al., 2022), we uti- lize any off-the-soeeee shelf VAE continuous tokenizer (Kingma & Welling, 2014) with simplified scale design, where each subsequent scale is simply double the previous one (e.g., 21, 4, 8, 16)), and coarse scale tokens are obtained by di- rectly downsampling the finest scale tokens (i.e., the largest resolution token map). This streamlined design eliminates the need for a specially designed tokenizer and decouples the tokenizer from the generator, allowing greater flexibility to update the tokenizer with any modern VAE (Li et al., 2024; Chen et al., 2024).
To further enhance image quality, we incorporate the flow matching model (Lipman et al., 2022) to learn the probabil- ity distribution at each scale. Specifically, given the class token and tokens from previous scales, we use an autore- gressive Transformer (Radford et al., 2018) to generate con- tinuous semanties that condition the flow matching model, progressively transforming noise into the target latent rep- resentation for the current scale. Conditioning is achieved through the proposed spatially adaptive layer normalization (Spatial-adail adaLNN, which adaptively adjusts layer normaliza- tion (Ba et al., 2016) on a position-by bone--yy--ppooonnn basis, captur- ing fine-grained details and improving the model's ability to generate high-fidelity images. This process is repeated across scales, capturing the hierarchical dependencies inher-
2

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
with any VAE tokenizer (Kingma & Welling, 2014).
3. Method
Flow- and Diffusion-based Models. Diffusion mod- els (Rombach et al., 2022; Peebles & Xie, 2023; Li et al., 2024; Hoogeboom et al., 2023; Ho et al.. 2020; Song et al., 2020; Liu et al., 2024; Yang et al., 2024) have surpassed earlier image generation methods like GANs (Goodfellow et al.. 2014; Sauer et al., 2022) by utilizing multistep dif- fusion and denoising processes. Latent Diffusion Models (LDMs) (Rombach et al., 2022) advance this approach by transitioning diffusion from pixel space to latent space, en- hancing efficiency and scalability. Building on this foun- dation, DiT (Peebles & Xie, 2023) and U-ViT (Bao et al., 2023) replace the traditional convolution- sanoouttion-base U-Net (Ron- neherger et al., 2015) with Transformer architectures within the latent space, further improving performance. Flow matching (Lipman al., 2022; Liu et al., 2022; Albergo & Vanden-Fijnden, 2022; Shin et al., 2025; He et al., 2025) redefines the forward process as direct paths between the data distribution and standard normal distribution, offering a more straightforward transition from noise to target data compared to conventional diffusion methods. SiT (Atito et al., 2021) leverages the backbone of DiT and employs flow matching to more directly connect these distributions. Scaling this concept, SD3 (Esser et al., 2024) introduces novel Transformer-bast based architecture trained with flow matching for text-to-image generation (Deng et al., 2025). MAR (Li et al., 2024) presents a diffusion-basse strategy to model per-token probability distributions in a continuous space, enabling autoregressive models without relying on discrete tokenizers and utilizing specialized diffusion loss function instead of the traditional categorical cross-entropy loss. In contrast to MAR, our proposed FlowAR employs scale-wise flow matching model to capture per-scale prob- abilities, utilizing coarse-to-fine scale-wise conditioning derived from a scale-wise autoregressive model.
In this section, we begin with an overview of autoregressive modeling in Sec. 3.1, followed by a detailed introduction of the proposed method in Sec. 3.2.
3.1. Preliminary: Autoregressive Modeling
Autoregressive Modeling in NLP. Consider a corpus repre- sented as sequence of words U = {Wi, W2..... wn In NLP, autoregressive models predict each word based on all preceding words in the sequence:
11 p(4) II p(wx W1, W2, Wk WK-1, θ),
(1)
where the autoregressive model is parameterized by O. The objective function to minimize is the negative log-likelihood over the entire corpus:
L C= Σ logp(ux |wck θ)
(2)
where h denotes all positions preceding k. This approach serves as the foundation for successful large-scale language models, as demonstrated in (Touvron et al., 2023a; OpenAI, 2022; 2023).
Tiken-wise Autoregressive Modeling for Image Gener- ation. Extending autoregressive modeling to images in- volves tokenizing 2D image X E 3 using vector quantization (Van Den Oord al., 2017). This process converts the image into grid of discrete tokens, which is subsequently flattened into one-dimensional sequence X = (ti, 12, tt.]:
N Σ log |tek, θ).
C=
Discussion. Our FlowAR provides a more flexible frame- work for next scale prediction, enhanced with flow matching. In Figure 2, we compare our FlowAR model with VAR (Tian et al., 2024), highlighting key differences in both the tok- enizer and generator components. For the tokenizer, VAR relies on a multi-scale VQGAN (Razavi et al., 2019; Esser et al., 2021; Lee et al., 2022) that is tightly integrated with its generator and trained on a complex set of scales ((1, [1, 2, 3, 4, 5, 6, 8, 10, 13, 16)). In contrast, FRowAR can use any off-the-shell VAE (Kingma& & Welling, 2014) as its to- kenizer, offering greater flexibility by constructing coarse scale token maps through direct downsampling of the finest scale token map. For the generator, VAR is constrained by a complex and rigid scale structure, whereas FlowAR benefits from a simpler and more general scale design, allowing the integration of the modern flow matching model (Lipman et al., 2022).
However, flattening the token grid disrupts the intrinsic two- dimensional spatial structure of the image. To preserve this spatial information, VAR (Tian al., 2024) introduces a scale-wise autoregressive modeling approach, as described below.
Scale-wise Autoregressive Modeling for Image Genera- tion. Rather than flattening images into token sequences, VAR (Tian et al., 2024) decomposes the image into a series of token maps across multiple scales, S = {(1, $2, ....... Sp. Each token map sk has dimensions h4 x wy and is obtained by specially designed multi-scale VQGAN with residual structure (Razavi et al., 2019; Lee et al., 2022; Tian et al., 2024; Esser et al., 2021). In contrast to single flattened tokens t that lose spatial context, each 8 maintains the two-dimensional structure with h2 x we tokens. The autore- gressive loss function is then reformulated to predict each
3

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
VAR
Multi-scale VQGAN
Scale-wise Autoregressive
Discrete Tokens
Decoder
Multi Scale-
Resihaal
X Bonded with Generator
Diserete Tokens
Complex and Rigid Scale Design {1, 21,2,3, 3,4, 8, 10, 13, 16)
FlowAR
Semantics
Any
Contimuous
Scale-wise Autoregressive
Scale-wids
Decoder
VAE
Latents
Flow Matching
Downsample
Simple and General Scale Design 2, 4, 8, 16)
Off-the-shelf
(a) Tokenizer Comparison
(b) Generator Comparison
Figure 2. Comparison Between VAR and Our FlowAR in (a) Tokenizer and (h) Generator design. VAR (Tian al., 2024) utilizes complex multi-son scale residual VQGAN discrete tokenizer, whereas FRo can leverage any fer-the-shelf VAE continuous tokenizer, constructing coarse scale token maps by directly downsampling the finest scale token map. (b) VAR's generator constrained by the same complex and rigid scale design its tokenizer, while FRowAR benefits from simple and flexible scale design, enhanced by the flow matching model.
scale based on all preceding scales:
crete tokenizer with any off-the-shelf VAE continuous to- kenizer (Kingma & Welling, 2014), and (2) modeling the per-scale prediction (i.e., predicting all h2 x we tokens in k-th scale 8x) using flow matching to learn the probabil- ity distribution. The first change enables the flexibility to leverage any existing VAE tokenizer, benefiting from recent advances in VAE technology without being constrained by complex scale sequence design. The second improvement enhances generation quality by utilizing the modern flow matching algorithm.
L C=
plsk Sck, θ).
(4)
In this framework, generating the k-th scale in VAR re- quires attending to all previous scales 8ek (iee,, 81 to 8k- 1) and simultaneously predicting all hy wy tokens in 83 via categorical distributions. The chosen scales, S (1,2,3, 4, 5, 6, 8, 10, 13, 16), introduce significant complexity the scale design and constrain the model's generalization capabilities. This is due the tight coupling between the generator and tokenizer with the scale design, reducing flexibility in updating the tokenizer or support- ing alternative scale configurations. Furthermore, VAR's discrete tokenizer relies on a complex multi-scale residual structure, complicating the training process, with essential training code and details remaining publicly unavailable at the time of our submission.
Simple Scale Sequence with Any VAE Tokenizer. Given image, we extract its continuous latent representation F E Rexxhx using an off-the-shelf Variational Autoen- coder (VAE) (Kingma & Welling, 2014). We then construct a set of coarse-to-fin scales by downsampling the latent F as follows:
S {8), 82 ,....,8))
(Down( F. 2n Down F, F,2"-2) 2).-))..... Down F, 1)},
(5)
3.2. Proposed Method: FlowAR
where Down (F F, denotes downsampling the latent F by factor of and no downsampling is applied when y= 1.
Overview. To address the issues outlined above, intro- duce FIowAR seen in Figure 3, more general scale-wise autoregressive modeling, enhanced with flow matching (Lip- man et al., 2022). Our method incorporates two primary improvements over existing next scale prediction (Tian al., 2024): (1) replacing the multi-scale VQGAN dis-
Notably, our multi-scale token maps S are derived by di- rectly downsampling the highest-resolution latent F, re- moving the need for complex multi-scale residual VQGAN design (Tian et al., 2024; Razavi et al., 2019; Lee al.,

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Semantics
Flow
[C]
Velocity V
Any VAE
Matching
Autoregressive
Semantics
Downsmple
Upsample
Flow Matching
Velocity V?
Semantics
g3
Upsample
Flow
Velocity V
Matching
Figure 3. Overview The Proposed FlowAR. FlowAR consists three main components: (1) an off-the- shelf VAE that extracts a continuous latent representation of the image. We then create set of coarse-to to to-fine scales by downsampling this latent, forming a sequence of token maps s² " ..... ,s"", where each subsequent scale doubles size from the previous one. (2) scale-wise autoregressive Transformer that takes as input the sequence {[C]. Up(s"_ 2).... Up(s"~1,3 2)]. where [C] is condition token and Up(: 2) denotes upsampling by a factor of 2. This Transformer generates semantic representations different scales, s² "'....,""", (3) A seale- wise flow matching model, conditioned on the semantics s' at each scale (time step conditions are not shown for simplicity), predicts the velocity given a random time step f that moves the noises to target data distribution.
2022; Esser et al., 2021). To distinguish our approach, we use the superscript ø to denote our i-th scale token map, s We set n = (i.e., S = {1, 2, 4, 8, 16)), making the scale design in FlowAR significantly simpler and more versatile than that of VAR (Tian et al., 2024). This design allows us to integrate various off-the-shelf VAE tokenizers into our framework, eliminating the need for a multi-scale tokenizer trained on a predefined scale sequence.
pling of latent s by a factor of r. For the initial scale (i = 1), only the class condition C is used as input. We set r = 2, fol- lowing our simple scale design, where each seale is double the size of the previous one. We refer to the resulting output s as the semantics for the i-th scale, which then used to condition a flow matching module to learn the per-scale probability distribution.
Scale- Flow Matching Model Conditioned by Autore- gressive Transformer Output. Flow matching (Lipman et al., 2022) generates samples from the target data distribu- tion by gradually transforming source noisy distribution, such Gaussian. For each i-th scale, FlowAR extends flow matching to generate the scale latent s', conditioned on the autoregressive Transformer's output š Specifically, during training, given the scale latent s² from the target data distribution, we sample a time step t E [o, 1] and a source noise sample FG from the source noisy distribution, typically setting Fj ~ (O, 1) match the shape of conditioned la- tent g analogous the "noise" in diffusion models (Rom- bach et al., 2022). We then construct the interpolated input F as:
With this simplified scale sequence and the fiexibility to use any off-the-shelf VAE tokenizer, we introduce our improve- ment for next scale prediction. Unlike VAR (Tian et al., 2024), which models the categorical distribution of each scale using autoregressive Transformer, we utilize the Transformer to generate conditioning information for each scale, while a scale-wise flow matching model captures the scale's probability distribution based on this information. Below, we outline how the autoregressive Transformer gen- erates conditioning information for each scale, followed by details of the scale-wise flow matching module.
Generating Conditioning Information via Scale-wise Au- toregressive Transformer. To produce the conditioning information for each subsequent scale, we utilize conditions obtained from all previous scales:
= + + - F)
(7)
The model is trained to predict the velocity V using F
dF
3² T (IC, ((Cs))(s))2), " Vi 1,
V dt
(8)
(6)
s'-
where T(.) represents the autoregressive Transformer model, C' is the class condition, and Up(s, r) denotes the upsam-
where V indicates the direction to move from Fj toward s
5

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
guiding the transformation from the source to the target dis- tribution at each scale. Unlike prior approaches (Atito al., 2021; Esser et al., 2024) that condition velocity prediction on class or textual information, we condition on the scale- wise semantics 8 from the autoregressive Transformer's s output. Notably, in prior methods (Atito et al., 2021; Esser et al., 2024), the conditions and image latents often have different lengths, whereas FlowAR shares the same length (i.e., s and s have the same shape, Vi 1,,..,,) The training objective for scsee-wise flow matching is:
autoregressive Transformer. We apply the Spatial-adaL.N to every Transformer block within the flow matching mod- ule. Alternative design choices are explored in the ablation study.
Inference Pipeline. the beginning of inference, the au- toregressive Transformer generates the initial semantics 8 using only the class condition C. This semantics á' condi- tions the flow matching module, which gradually transforms noise sample into the target distribution for s The result- ing token map is upsampled by factor of 2, combined with the class condition, and fed back into the autoregressive Transformer to generate the semantics $?, which conditions the flow matching module for the next scale. This process is iterated n scales until the final token map 8" estimated and subsequently decoded by the VAE decoder to produce the generated image. Notably, et al., 2023) in the autoregressive Transformer to efficiently generate each semantics 8
L= (F2,s,)) vill"
-
(9)
where FM denotes the flow matching model parameterized by 0. This approach allows the model to capture scale- wise information bi-directionally, enhancing flexibility and efficiency in image generation within our framework.
the KV cache (Pope
Scale-wise Injection of Semantics via Spatial-adaLN. A key design choice is determining how best to inject the semantic information st, generated by the autoregressive Transformer, into the flow matching module. A straightfor- ward approach would be to concatenate the semantics s² with the flattened input Fi, similar to in-context condition- ing (Bao et al., 2023) where the class condition is concate- nated with the input sequence. However, this approach has two main drawbacks: (I) it increases the sequence length input to the flow matching model, raising computational costs, and (2) it provides indirect semantic injection, poten- tially weakening the effectiveness of semantic guidance. To address these issues, we propose using spatially adaptive layernorm for position-by caattion-by-posee by-position semantic injection, re- sulting in the proposed Spatial-adaL.N. Specifically, given the semantics 3 from the scale-wise autoregressive Trans- former and the intermediate feature F in the flow matching model, we inject the semantics to the scale 7, shift 8, and gate a parameters of the adaptive normalization, following the standard adaptive normalization procedure (Ba et al., 2016; Zhang& & Sennrich, 2019; Peebles & Xie, 2023):
4. Experimental Results
In this section, we present our main results on the chal- lenging ImageNet-256 and ImageNet-512 generation bench- mark (Deng et al., 2009) (Sec. 4.1), followed by ablation studies (Sec. 4.2).
4.1. Main Results
Following the settings in VAR (Tian et al., 2024), we train FlowAR on ImageNet for class-conditional image gener- ation. We evaluate the model using Fréchet Inception Distance (FID) (Heusel et al., 2017) and inception score (IS) (Salimans et al., 2016), Precision (Pre.) (Powers, 2020) and Recall (rec.) (Powers, 2020) as metrics.
Quantitative Results on ImageNet-256. As shown in Ta- ble I, when compared to previous generative adversarial models (Brock et al., 2018; Sauer et al., 2022; Kang et al., 2023), autoregressive models (Razavi et al., 2019; Esser et al., 2021; Lee et al., 2022; Sun et al., 2024; Yu et al., 2021), diffusion-based methods (Dhariwal & Nichol, 2021; Rombach et al., 2022: Peebles & Xie, 2023; Bao al., 2023), and flow matching methods (Atito al., 2021),
01, 02 31, 11, 72 = MLP(8' +t),
FlowAR achieves significant performance gains. Specif- ically, our best model variant, FlowAR-H, attains an FID of 1.65, outperforming StyleGAN (Sauer et al., 2022) (2.30), LlamaGen-3B (Sun et al., 2024) (2.18), DiT (Peebles & Xie, 2023) (2.27), and SiT (Atito et al., 2021) (2.06).
Ft' = Attn
11 o LN(FF') + B1) 3 a1.
F MLP
12 O LN(FF)) + ② 022
(10)
where Attn denotes the attention mechanism, and LN de- notes the layer norm, Py" is the block's 's output, and @ is the spatial-wise product. Unlike traditional adaptive normaliza- tion, where the scale, shift, and gate parameters lack spatial information, our spatial adaptive normalization provides po- sitional control, enabling dependency on semantics from the
Compared to the closely related VAR (Tian et al., 2024). FR provides superior image quality at similar model scales. For example, FlowAR-L, with 589M parameters, achieves an FID of 1.90-surpassing both VAR-d20 (FII) 2.95) of comparable size and even largest VAR-d30 (FID 1.97), which has 2B parameters. Furthermore, our largest
6

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
method
type
params
FID +
IS f
Pre.1
Rec.1
BigGAN (Brock et al., 2018)
GAN
112M
6.95
224.5
0.89
0.38
GigaGAN (Kang ct al., 2023)
GAN
569M
3.45
225.5
0.84
0.61
StyleGAN (Sauer et al., 2022)
GAN
166M
2.30
265.1
0.78
0.53
ADM (Dhariwal Nichol, 2021)
diffusion
554M
10.94
101.0
0.69
0.63
LDM (Rombach et al., 2022)
diffusion
400M
3.60
247.7
U-ViT(Bao et al., 2023)
diffusion
287M
3.40
219.9
0.83
0.52
DiT (Peebles & Xie, 2023)
diffusion
675M
2.27
278.2
0.83
0.57
SiT (Atito et al., 2021)
flow matching
675M
2.06
270.3
0.82
0.59
VQVAH-2 (Razavi al., 2019)
token-wise autoregressive
13.5B
31.11
45.0
0.36
0.57
VQGAN (Esser et al., 2021)
token-wise autoregressive
1.4B
15.78
74.3
RQTransformer (Lee et al., 2022)
token-wise autoregressive
3.8B
7.55
134.0
LlamaGen-B (Sun et al., 2024)
token-wise autoregressive
111M
5.46
193.6
0.83
0.45
ViT-VQGAN (Yu et al., 2021)
token-wise autoregressive
1.7B
4.17
175.1
LlamaGen-L (Sun al., 2024)
token-wise autoregressive
343M
3.07
256.1
0.83
0.52
LlamaGen-XL. (Sun et al., 2024)
token-wise autoregressive
775M
2.62
244.1
0.80
0.57
LJamaGen-3B (Sun al., 2024)
token-wise autorerressive
3.1B
2.18
263.3
0.81
0.58
VAR-d12 (Tian et al., 2024)
scale-wise autoregressive
132M
5.81
201.3
0.81
0.45
FlowAR-S
scale-wise autoregressive
170M
3.61
234.1
0.83
0.50
VAR-d16 (Tian al., 2024)
scale-wise autoregressive
310M
3.55
280.4
0.84
0.51
FlowAR-B
scale-wise autoregressive
300M
2.90
272.5
0.84
0.54
VAR-d20 (Tian al., 2024)
scale-wise autoregressive
600M
2.95
302.6
0.83
0.56
FlowAR-L
scale-wise autoregressive
589M
1.90
281.4
0.83
0.57
VAR-d30 (Tian et al., 2024)
scale-wise autoregressive
2.0B
1.97
323.1
0.82
0.59
FlowAR-H
scale-wise autoregressive
1.9B
1.65
296.5
0.83
0.60
Table 1. Generation Results ImageNett226.. Metrics reported include Fréchet Inception Distance (FID), inception (IS).
precision (Pre.) and recall (Rec.). The proposed FlowAR demonstrates state-of-the generation performance. VAR is evaluated using code and pretrained weights from their official GitHubb repository.
model, FlowAR-H (1.9B parameters, FID 1.65), new state-of-the-art benchmark for scale-wise autoregressive im- age generation.
method
params
FID J
IS
BigGAN (Brock al., 2018)
158M
8.43
177.9
MaskGiT (Chang et al., 2022)
227M
7.32
156.0
VQGAN (Esser et al., 2021)
227M
26.52
Quantitative Results on ImageNet-512. As demonstrated in Table 2, we present quantitative results on ImageNet-512 generation. Our FlowAR-L model surpasses the closely related VAR-d36 by 0.2 FID while utilizing only 25.6% of its parameters. Compared other state-of-the-art gen- erative models-including GAN-based BigGAN (Brock al., 2018), musk-prediction-base based MaskGiT (Chang et al., 2022), token-wise autoregressive VQGAN (Fiser et al., 2021), and diffusion-based DiT-XL/2 (Peebles & Xie, 2023), our FRoL achieves superior performance across both FID and metrics, establishing consistent improvements over all competing approaches.
DIT-XL/2 (Peebles & Xie, 2023)
675M
3.04
240.8
VAR-d36 (Tian et al., 2024)
2.3B
2.63
303.2
FlowAR-L.
590M
2.43
295.1
Table 2. Generation Results on ImageNet-512. Metrics reported include Fréchet Inception Distance (FID), inception score (IS). The proposed FlowAR outperforms prior works.
achieves
4.2. Ablation Studies
We conduct ablation studies on tokenizer compatibility, scale-wise flow matching model and injection semantics. More ablation studies on construction of scale sequence and scale configurations can be found in the Appendix.
Visualization. We visualize samples generated by FlowAR using different tokenizers in Figure showing that FlowAR is capable of producing high-quality images with impressive visual fidelity and is compatible with various off-the-shelf VAHS. More samples at 256x 256 and 512x512 resolution are provided the Appendix.
Tokenizer Compatibility. VAR (Tian et al., 2024) relies on complex multi-scale residual tokenizer that compresses images into discrete tokens at diffferent scales, with the scale structure of the tokenizer directly mirroring VAR's archi- tectural scales. This tight coupling between the tokenizer and VAR limits the framework's flexibility and adaptabil-
7

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
MAR-VAE
SD-VAE
ostrich (9)
macaw (88)
wallaby (104)
blenheim spaniel (156)
lesser panda (387)
barn (425)
stone wall (825)
Figure Visualization of Samples Generated by FlowAR Using Different Tokenizers. FlowAR consistently produces high-quality visual samples across various tokenizer configurations including VAE from MAR (Li et al., 2024) and SD (Rombach et al., 2022).
tokenizer
Param.
generator
FID
IS
per-token
per-scale
diffusion
flow-matching
FID
IS
multi-scale residual VQGAN
109.0M
5.81
201.3
6.85
155.6
DC-AE (Chen et al., 2024)
323
FlowAR
4.22
220.9
6.15
184.1
SD-VAE (Rombach et al., 2022)
83.7M
FlowAR
3.94
231.0
3.93
223.9
MAR-VAE (Li et al., 2024)
66.5M
FlowAR
3.61
234.1
3.61
234.1
Table 3. Ablation on Tokenizer Compatibility, While VAR (Tian al., 2024) depends heavily multi-scale residual discrete tokenizer, FlowAR is compatible with variety of continuous VAE tokenizers. The final setting marked gray.
Table 4. Ablation on Scale-wise Flow Matching Model. We investigate two design choices in this module: (1) per-token vs.per- scale prediction and (2) diffusion vs.flow- fitt--nttching framework. The final setting is highlighted in gray.
the flow matching framework.
ity. In contrast, our proposed FlowAR is compatible with wide range of variational autoencoders (VAEs), enhancing versatility and ease of integration. As shown in Table 3,
Injection of Semantics. There are several methods to inject the semantics s², generated by the autoregressive Trans- former, into the flow matching module. We summarize these methods Table 5: (1) 'addition': Element-wise ad- dition of the semanties and the flattened input. (2) 'cross attention': Using cross-ateention where the flattened input serves the query and the semantics act as the key and value. (3) 'sequence concatenation': Concatenating the semantics with the flattened input along the sequence di- mension. (4) 'channel concatenation': Concatenating the semantics with the flattened input along the channel dimen- sion. (5) 'adaLN': Adaptive LayerNorm conditioned the spatially averaged semantics. (6) 'Spatial-adal.N" The proposed spatial adaptive LayerNorm, injecting semantics position-by- by-position..
FlowAR achieves superior performance across various VAE architectures, including DC-AE (Chen et al., 2024) (FID of 4.22), SD-VAE (Rombach al., 2022) (FID of 3.94), and MAR- MAR-VAE (Li et al., 2024) (FID of 3.61), compared to VAR's s multi-scale residual discrete tokenizer, which yields an FID of 5.81. These results underscore FlowAR 's effec- tiveness and adaptability, highlighting its advantage over VAR's s more rigid tokenizer dependency.
Scale-wise Flow Matching Model. The flow matching model is used learn the per-scale probability distribu- tion, predicting all h2 Wi tokens in the k-th scale sg. We consider two design alternatives. First, per-scale pre- diction could be replaced with per-token prediction using Multi-Layer Perceptrons (MLPs) (Li et al., 2024), which, however, lacks the ability to capture interactions between tokens. Second, we could substitute the flow matching ap- proach (Lipman al., 2022) with diffusion framework (Ho et al., 2020). These design choices explored in Table 4, where per-scale prediction consistently outperforms per- token prediction, regardless of whether flow matching or diffusion used. Additionally, flow matching provides marginal improvements over the diffusion framework. Our final model configuration employs per-scale prediction with
As shown in Table 5, the choice of semantic injection method significantly impacts performance. The proposed 'Spatial-adia adaLN' achieves the best results, with an FID of 3.61 and an Inception Score (IS) of 234.1, outperforming all other methods. These results indicate that methods preserv- ing spatial structures and offering position-wiss semantic guidance yield superior image generation quality. The ex- ceptional performance of Spatial-adaL.N can be attributed to its ability to inject semantics directly into the normalization layers spatially adaptive manner.
8

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
injection of semantics
FID
IS
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan,
adaLN
14.28
111.9
Y., Ge, W., Han, Y., Huang. F., al. Qwen technical report. arXiv preprint arXiv:2309.
sequence concatenation
9.22
146,9
2023.
addition
6.22
188.2
Bao, F., Nie, S., Xue, K., Cao, Y., Li, C., Su, H., and Zhu, J. All worth words: A vit backbone for diffusion models. In CVPR, 2023.
cross attention
5.37
202.5
channel concatenation
4.85
210.9
spatial-adaL.
3.61
234.1
Brock, A., Donahue, J., and Simonyan, K. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv: 0akiv:/8000 1809. 1096, 2018.
Table Ablation on Semantic Injection Schemes for the Flow Matching Module. The proposed Spatial-adaLNN injects semantics in spatially adaptive manner, achieving the best performance. The final setting is highlighted in gray.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G.,
5. Conclusion
Askell, A., et al. Language models are few-shot learners. NeurIPS, 2020. URL
In this work, we presented FlowAR, a flexible and gen- eralized approach to scale-wise autoregressive modeling for image generation, enhanced with flow matching for im- proved quality. By adopting a streamlined scale design and compatible with any VAE tokenizer, FlowAR addresses lim- itations of prior models, offering greater adaptability and superior image quality. With spatially adaptive layer normal- ization, achieving state-of-the-art results on ImageNet-256 generation benchmark. We hope FlowAR will inspire more future research in autoregressive image modeling.
neuzipss.cc/ppper///2///EL
1457006bb..o0961111tttacclll Paper.. pdf.
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. Maskgit: Masked generative image transformer. In CVPR, 2022.
Chen, J., Cai, H., Chen, J., Xie, E., Yang. S., Tang, H., Li, M., Lu, Y., and Han, S. Deep compression autoen- coder for efficient high-resolution diffusion models, arXiv preprint arXiv:2410.1000 10733, 2024.
Impact Statement
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Dhariwal, P., Luan, D., and Sutskever, I. Generative pretraining from pixels. In ICML, 2020.
FRowAR represents major advance in scale-wise autore- gressive image generation: it eliminates VAR's complex multi-scale residual tokenizer, supports any off-the-shelf Variational AutoEnc and adopts streamlined scale de- sign in which each successive resolution is simply twice that of its predecessor. FlowAR not only elevates the quality of synthetic imagery but also establishes modular framework that we anticipate will catalyze a new wave of innovation in scalable, adaptable image generation.
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1-113, 2023.
Deng. J., Dong, W., Socher, R., L.-.,, Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
Acknowledge
This work is supported by Office of Naval Research with award N000142412696.
Deng. X., Yu, Q. Athar, A., Yang. C., Yang, I., Jin, X., Shen, X., and Chen, L.-C. Coconut-pancap: Joint panoptic segmentation and grounded captions for fine- grained understanding and generation. arXiv preprint WwXi::250.00258) 2025.
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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Appendix
scale construction
FID
IS
image
12.19
118.2
The appendix includes the following additional information:
latent
3.61
234.1
Sec. A lists the
of FlowAR.
Table 8. Ablation on Construction of Scale Sequence. We com- pare two approaches for constructing the scale sequence: down- sampling the input image before feeding it into the VAE, or down- sampling the latents extracted by the VAE, The final setting highlighted in gray.
Sec. provides the architectural details FlowAR model variants.
Sec. C provides more ablation studies.
method
scales S
FID
IS
Sec. provides more visualization results.
VAR
(1,2,3,4,5, 6, 8, 10, 13, 16)
5.81
201.3
VARs {1,2,4,8, 16)
N/A
N/A
Sec. E includes the impact statement.
FlowAR
{1,2, 4, 16)
3.61
234.1
FlowAR
{1,4, 8, 16) 11,4,66
4.88
200.1
A. Hyper-parameters
FlowAR
6.10
194.2
We list the hyper-parameters of our FRowAR in Table 6.
Table 9, Ablation on Scale Configurations. We compare VAR and FlowAR under various scale configurations. VAR is con- strained by its predefined scale sequence and fails to generalize to other configurations (2nd row), whereas FlowAR demonstrates flexibility across different scale setups. The final setting is high- lighted in gray.
training
optimizer
AdamW
warmup epochs
100
total epochs
400
batch size
1024
peak learning rate
2e-4
as in VAR (Tian et al., 2024), we propose a simpler ap- proach by directly downsampling the latent representations extracted by any off-the-shelf continuous VAE (Kingma & Welling, 2014) tokenizer. An alternative design choice would be to downsample the image before feeding it into VAE. We explore this design choice in Table 8, where down- sampling the latents (FID 3.61) significantly outperforms downsampling the image (FID 12.19).
minimal learning rate
le-5
learning rate schedule class label dropout rate
cosine
0.1
max gradient norm
1.0
Table 6. Slyper-parameter: of FlowAR.
B. Model Variants
Scale Configurations, To demonstrate FlowAR's flexibility with respect to scale design, we perform ablation study by progressively reducing the number of scales used in the model. Table 9 presents the results of this study. VAR (Tian et al., 2024) relies on a complex scale configuration with ten scales ((1, 2, 4, 5, 6, 8, 10, 13, 16})) to achieve its reported performance. Simplifying VAR's scale configuration to {1, 2, 4, 8, 16) leads to training failure, indicating strong dependency between its tokenizer and generator. In contrast, FlowAR demonstrates strong robustness to scale reduction. With the simplified sequence {1, 2, 4, 8, 16), FlowAR achieves an FID of 3.61, outperforming VAR even with its full scale sequence. Reducing the seales further to {1, 4, 8, 16), FlowAR still maintains competitive performance with an FID of 4.88. Even with just three scales ((1, 4, 16)). FlowAR achieves FID of 6. 10, comparable VAR's FID of 5.81.
In Table 7, provide four kinds of different configurations of FlowAR for a fair comparison under similar parameters with VAR (Tian et al., 2024). The proposed FlowAR con- tains two main modules: Autoregressive Model and Flow Matching Model, both build on top of Transformer architec- tures (Vaswani, 2017).
variants
autoregressive model
flow matching model
FlowAR-S
D=12, W=768
D=2, W= 1024
FlowAR-B
D=16, W= 768
D=6, W=1024
FlowAR-L
D=16, W= 1024
D=12, W= 1024
FlowAR-H
D=30, W= 1536
D=18, W= 1536
Table Model Variants. "D" and "W" represent model depth and width, respectively.
C. More Ablation Studies
D. More Visualization Results
Construction of Scale Sequence. Instead of using a multi- scale VQGAN with residual connections (Tian al., 2024),
Additional visualization results generated by FlowAR are provided from Figure 5 to Figure 10.
12

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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Figure 5. Generated samples from FlowAR. FR generate high-fidelity great grey owl (24) images.
Figure 7. Generated samples from FlowAR. FlowAR generate high-fidelity golden retriever (207) images.
Figure 6. Generated samples from FlowAR. FlowAR generate high-fidelity macaw (88) images.
Figure 8, Generated samples from FlowAR. FlowAR generate high-fidelity otter (360) images.
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FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
E. Impact Statement
Our main objective to advance foundational research on generative models. direct next step is extend our method to large-scale visual generation tasks, such as text-to- image or text-to-video models, where it can notably reduce both training and inference costs while enhancing perfor- mance. Nonetheless, since our approach derives statistics directly from ImageNet, it may also inherit any biases inher- ent in that data.
Figure Generated samples from FlowAR. FR generate high-fidelity artichoke (944) images.
Figure 10. Generated samples from FlowAR. FRowAR generate high-fidelity alp (970) images.
14


Reviews:
[{"review_id": "vafgKye3Yt", "reviewer": "Reviewer_Ry1o", "summary": "The paper explores image generation using a next-scale prediction objective, in which image latents (from a VAE) are progressively super-resolved from 1x1 scale to 2x2, 4x4, and so on. To that end, the authors present a method in which an autoregressive model predicts a conditioning for a per-scale flow-matching model. Unlike models like VAR that rely on a specifically trained residual multi-scale tokenizer, FlowAR works on down-sampled latents from arbitrary VAEs, which makes it broadly applicable. On class-conditional ImageNet generation, FlowAR outperforms VAR and other baselines, and achieves SotA performance for next-scale prediction models.\n\n## update after rebuttal\nThe authors addressed most of my concerns and I raised my rating accordingly.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive feedback.\n\n> C1: importance of AR\n\nUnlike Matryoshka Diffusion Models (MDM), which use a nestedUNet to jointly denoise all scales, FlowAR conditions on previously generated scales. Below, we include a baseline, “diffuse all noisy scales” (without AR), which faces two issues: (1) ***slower inference***, as denoising is applied to all scales, resulting in extremely long sequences; and (2) ***worse FID***, due to the absence of guidance from previously generated scales. \n\n|model|params|inference time (sec/image)| FID|\n|:---:|:---:|:---:|:---:|\n|MDM|434M|3.16|3.51|\n|diffuse all noisy scale|600M|2.80|1.98|\n|FlowAR-L|589M|0.12|1.90|\n\n> C2: single-scale flow model \n\nBelow, we compare with SiT-XL, a pure single-scale flow model, and “FlowAR-L w/ single-scale.” Our multi-scale design outperforms SiT-XL while requiring 14% of the Flops and achieving a 13.9× speedup. Slightly slower than the single-scale variant, the multi-scale design achieves a 1.78 FID improvement.\n\n|model| params | FLOPS |inference time (sec/image)|FID | \n|:---:|:---:|:----:|:----:|:----:|\n|SiT-XL| 675M |58.3T |1.67|2.06|\n|FlowAR-L w/ single-scale| 589M |  6.2T| 0.09  |3.68  |\n|FlowAR-L w/ multi-scale  | 589M |8.2T  |0.12|1.90|\n\nFLOPs are computed across the entire generation process.\n\n> C3: token-wise prediction of semantics \n\nThis token-wise design results in slower inference and worse performance:\n\n|model | inference time (sec/image)| FID|\n|:---:|:---:|:----:|\n|token-wise AR | 0.58 |3.02|\n|scale-wise AR (ours) | 0.12|1.90|\n\n> C4: no split between AR and flow models\n\nBelow, we experimented with directly predicting the flow between the upsampled previous scale and the next scale.\n\n|model| inference time (sec/image)|FID | \n|:---:|:---:|:----:|\n|no split  | 0.45|2.45 |\n|with split (ours)  |0.12|1.90|\n\n> C5: performance vs. runtime\n\nUnlike MAR, which models each token individually, FlowAR models the probability over entire scales. It also reduces inference time by using only 5 AR steps (with 25 denoising steps each), compared to MAR’s 256 AR steps (with 100 steps each). As shown below, FlowAR-H is 5× faster than MAR-B while achieving a 0.66 FID improvement.\n\n|model|params|inference time(sec/image) |FID|\n|:---:|:---:|:----:|:----:|\n|MAR-B|208M|1.25|2.31|\n|FlowAR-H|1.9B|0.24|1.65|\n\nDue to response length limitation, please also refer to our response to Reviewer 6Cuw’s Q3 for the ablation on AR and flow model sizes.\n\n> ImageNet train-set gFID\n\nWe thank the reviewer for raising this point. FID is a widely used metric in prior works (e.g., MAR, DiT, VAR, SiT, StyleGAN), and our setup follows these standards for fair comparison. \n\n> Training settings of VAR and FlowAR\n\nFor ***data augmentation***, we follow VAR’s setup with RandomCrop and horizontal flipping. As noted in the paper, we will ***fully open-source*** our code and checkpoints for reproducibility and transparency.\nBelow, we clarify the training settings of VAR and FlowAR. While diffusion and flow models typically require long training (e.g., MAR: 800 epochs, DiT/SiT: 1400), FlowAR achieves strong performance with just 400 epochs. Compared to VAR-d30/d24 (350 epochs), FlowAR-H trains for a similar duration but uses nearly half the GPU hours, thanks to its simpler scale design and shorter sequences. The table below summarizes A100 training hours and FID scores. Compared to VAR-d20/12, our 250-epoch version of FlowAR achieves better performance while incurring only half the training cost.\n|model|epochs | params|training hours| FID|\n|:--:|:--:|:--:|:--:|:--:|\n|VAR-d30| 350 | 2B|9657 hours| 1.97 |\n|FlowAR-H| 350| 1.9B| 4667| 1.70|\n|FlowAR-H| 400 | 1.9B|5334 hours| 1.65 |\n|VAR-d12|250|132M  |788 hours  | 5.81|\n|FlowAR-S|250|170M|401 hours |4.12|\n|FlowAR-S|400|170M|642 hours| 3.61|\n|VAR-d20|250|600M  |3012 hours |2.95|\n|FlowAR-L|250|589M  | 1460 hours | 2.15 |\n|FlowAR-L|400|589M  |2335 hours  |1.90 |\n\n> Essential References\n\nWe will cite and discuss all mentioned works in the related work section and compare with [3, 8, 9] in the experiments. Our work is most closely related to VAR and its follow-up [6], which autoregressively predict the next scale—unlike other methods that, while leveraging multi-scale information, do not adopt this formulation.\n\n> IS vs. FID \n\nWe observe an IS–FID trade-off when tuning the CFG scale. While we focus on optimizing FID, a slight adjustment still allows both FID and IS to outperform VAR by a notable margin.\n\n|model|cfg|FID|IS|\n|:---:|:---:|:----:|:----:|\n|VAR|2.4|1.97| 323.1\n|FlowAR-H|2.4|1.65| 296.5|\n|FlowAR-H|3.0|1.75|357.3|\n\n> Visualization\n\nDue to response length limitation, please also refer to our response to Reviewer 6Cuw’s Q4 for the visualization of intermediate stage generated samples.\n\n\n> L288 claim\n\nOur Spatial-adaLN injects scale- and position-wise semantics for fine-grained conditioning, unlike the simpler concat approach (L281), which provides only sequence-level, indirect guidance.", "claims_and_evidence": "Overall the claims are sensible, yet, there are a few (FLOPS) controlled baselines that would be important to ablate to properly verify the contribution of the method:\n1. What's the importance of the autoregressive model in the method? How would a baseline perform in which there is no AR model, but all scales can attend to each other and all scales are diffused at once like Matryoshka Diffusion Models [Gu et al., 2023]? Of course, in this case the input is not the fully predicted previous scales, but the noised version of all the scales at once.\n2. Similar to before, how then would a FLOPS-controlled single-scale flow model perform? Is there a need for multi-scale prediction?\n3. I appreciate the per-token prediction ablation in L413, but how would the performance change if the semantics s were not predicted in a next-scale manner, but fully autoregressive? I.e. autoregressive on a inter-scale, and on a intra-scale level (raster-scan).\n4. Like pyramidal flow [Jin et al., 2024], what if there were no split between AR and flow models, but instead you predict the flow between upsampled previous scale and next scale directly? \n5. What is the performance and runtime trade-off when trading-off AR model and flow model layers? MAR shows that the diffusion layer can be rather small, but in FlowAR they are somewhat large.", "methods_and_evaluation_criteria": "The submission demonstrates the proposed method in a class-conditional generation framework with ImageNet-1k. This setting is very commonly used, and under that umbrella the evals make sense. That said, class-conditional ImageNet generation is a benchmark that is somewhat over-optimized, and the common generation metrics (gFID against train-set statistics) favor and measure overfitting to the train set. Indeed, FlowAR surpasses the train-val FID of 1.78, as reported by VAR. At that point, I would argue that the benchmark becomes nearly meaningless, and more scalable and less data-starved benchmarks are required. Commonly, fully autoregressive models can quickly overfit with just a few passes over the same data, and the FlowAR trains for 400 epochs. This might differ with hypbrid AR+Flow models, but it is also not clear whether any data augmentation was used.\nI would also like to note here that it is unclear to me if the Fig 1 results should be compared like that. As far as I can tell, VAR was trained for a shorter number of epochs. How did the authors ensure these two settings are comparable?", "theoretical_claims": "The submission does not make any theoretical claims. All claims are empirical in nature.", "experimental_designs_or_analyses": "The paper gives enough implementation details to make a reasonable attempt at reimplementing it, but not enough to be fully confident of the exact training procedure and settings. It is also not very detailed in the ablation settings.", "relation_to_broader_scientific_literature": "This submission is timely, given the community's recent interest into alternative generation schemes (other than diffusion and raster-scan next-token prediction) that explore hierarchical, or multi-scale approaches. The work is very related to VAR and classical \"progressive super-resolution\" works (e.g. cascaded diffusion, Imagen), as well as recent literature that explores next-token prediction with continuous latents (e.g. GIVT, MAR, Fluid).", "essential_references_not_discussed": "There are a few important missing references. \"Next-scale\" training and prediction has been a long-standing research area, going back to at least early explorations with GANs [1,2]. It has also been studied from the perspective of cascaded super-resolution with diffusion models, e.g. [3,4], as well as quite recently and relevant to FlowAR in videos with flow models [5]. VAR has also had relevant follow-up work, e.g. [6]. More related to the autoregression + continuous targets direction, [7,8] are also relevant works in that direction, with [7] building upon the cited MAR work. I would also note [9] as a quite relevant work that performs diffusion on multiple scales at once, instead of in an autoregressive manner.\n\n[1] Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, Denton et al., 2015\n\n[2] Progressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al., 2017\n\n[3] Cascaded Diffusion Models for High Fidelity Image Generation, Ho et al., 2021\n\n[4] Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, Saharia et al., 2022\n\n[5] Pyramidal Flow Matching for Efficient Video Generative Modeling, Jin et al., 2024\n\n[6] Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis, Han et al. 2024\n\n[7] Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens, Fan et al., 2024\n\n[8] DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation, Gu et al., 2024\n\n[9] Matryoshka Diffusion Models, Gu et al., 2023", "other_strengths_and_weaknesses": "The paper presents strong results on IN1K class-conditional generation. The method is overall straight-forward, and greatly simplifies some of the limitations of VAR's need for a specially trained multi-scale tokenizer. Indeed, it can be run with a wide variety of standard VAE models. That said, I feel that there are a few important baselines missing, and I have doubts about the validity of the IN1K class-conditional generation benchmark when pushing models to such high performance levels. Train-set gFID to me is not a solid metric, and it looks like VAR significantly out-performs FlowAR in terms of IS. The setting and the rank-reversal between metrics makes me have some doubts about the more fine-grained side of results, but overall the method is conceptually simple and seems to work reasonably well.", "other_comments_or_suggestions": "Generally the manuscript is well written and easy to read. I did not spot many obvious typos.\nI would suggest to show more visuals, and if possible, show visuals of intermediate stage reconstructions (even if they may not be fully valid).", "questions_for_authors": "On L288: \"it provides indirect semantic injection, potentially weakening the effectiveness of semantic guidance.\" This claim is not very clear to me. Could the authors expand upon the reasoning for this? In which sense is the \"semantic injection\" indirect?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GsHeWEqOtS", "reviewer": "Reviewer_U6ur", "summary": "This paper proposes a multi-scale approach for image generation by combining  autoregressive modeling with flow matching at each scale. Instead of using a VQVAE-based tokenization as in the VAR approach, the method uses continuous latents from a VAE which are downsampled to get tokens at different scales. An autoregressive transformer takes as input the condition and multiscale representations encoding semantics at different scale. Conditioned on this, the velocity vectors are predicted. Experiments are performed on the ImageNet dataset where the approach outperforms prior work.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive feedback and address the concerns below.\n\n> Essential References\n\nWe thank the reviewer for the suggestion. These works represent early efforts in applying autoregressive modeling to image generation in pixel space. We will cite and discuss them in the related work section.\n\n> Q1: scale with resolution\n\nIn our design, each subsequent scale is simply double the previous one—providing a simple and intuitive hierarchical structure. Additionally, our method supports non-square resolutions, though this may result in a slight drop in performance.\n\n|scales|FID|\n|:---|:---:|\n|$\\{1\\times 1, 2\\times 2, 4\\times 4, 8\\times 8, 16\\times 16\\}$ (default setting)|1.90|\n|$\\{1\\times 1, 2\\times 4, 4\\times 8, 8\\times 16, 16\\times 16\\}$|2.21|\n|$\\{1\\times 1, 4\\times 2, 8\\times 4, 16\\times 8, 16\\times 16\\}$|2.16|\n\n> Q2: time steps across scales\n\nThank you for the question. By default, we use ***the same number of time steps*** (25 steps) for all scales. We ablate the effect of using different denoising time steps across scales (1x1, 2x2, 4x4, 8x8, 16x16) in the following table.\n\n\n|steps at 1X1|steps at 2X2|steps at 4X4|steps at 8X8|steps at 16X16|inference time (sec/image)|FID|\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|25|25|25|25|25|0.12|1.90|\n|50|50|50|50|50|0.24|1.89|\n|15|15|15|15|15|0.08|2.08|\n|10|10|10|10|10|0.06|2.48|\n|15|15|20|25|25| 0.12|1.96|\n|25|25|20|15|15| 0.08|1.94|\n\nAs shown, increasing the time steps from 25 to 50 across all scales results in only a marginal FID improvement of 0.01, while doubling the inference time. Reducing the steps to 15 or 10 significantly speeds up inference but comes at the cost of degraded image quality. We also evaluate varying the number of steps across scales. Using fewer steps for smaller scales leads to a slight FID drop of 0.06 without affecting inference speed. Conversely, assigning fewer steps to larger scales also slightly degrades performance but yields faster inference. Overall, these results suggest that a uniform 25-step setting offers a better trade-off between generation quality and efficiency.\n\n> Q3: long-text conditioning\n\nSince FlowAR is an autoregressive model, we can either concatenate the long text condition in front of the image sequence—similar to other autoregressive models [A]—or incorporate cross-attention as done in diffusion models [B].\n\n[A] Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation\n\n\n[B] High-Resolution Image Synthesis with Latent Diffusion Models\n\n> Q4: positional encodings\n\nThe positional embeddings are handled in the same way as prior methods [C, D, E, F] (e.g., absolute position embeddings are added at the input layer). As stated in the paper, the detailed implementation (for both training and inference) will be fully open-source, allowing the community to reproduce and examine all details.\n\n[C] Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction\n\n[D] Autoregressive Image Generation without Vector Quantization\n\n[E] Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization\n\n[F] Scalable Diffusion Models with Transformers\n\n> Q5: level of controllability\n\nWe thank the reviewer for the insightful question. In this work, our goal is to develop a general and broadly applicable framework by treating all scales uniformly—for example, using the same number of denoising steps across scales—on the standard ImageNet benchmark, following the next-scale prediction framework introduced by VAR. While datasets such as LAION or COCO may benefit from dataset-specific scale designs, our focus is on establishing a simple and general scale design that can generalize across settings without requiring dataset-specific tuning.", "claims_and_evidence": "+The work claims to address the problem of multiscale autoregressive image generation. To this end, limitation of prior work are identified and a new framework is proposed for autoregressive image generation\n+ The method section is clearly written and provides substantial argument in support of the design choices, for example, VAE encoder for multiscale semantics instead of a VQ-VAE based approach. \n+Experimental results on the ImageNet dataset show the effectiveness of the approach.", "methods_and_evaluation_criteria": "+To address the problem of image generation within the framework of autoregressive modeling the idea of using VAE over VQVAE with streamlined upsampling procedure compared to prior work is good. \n+The evaluation is based on comparison of the visual fidelity of the generated images and the parameter overhead with respect to the baseline. \n+ Adequate ablations are performed.", "theoretical_claims": "The work does not make any new theoretical claims. The efficiency of continuous representations from VAE compared to the code-book look-up of the VQ-VAE is well-established.", "experimental_designs_or_analyses": "+The experimental setup is inline with prior work and compared wrt to the FID and IS scores.\n+Ablations are performed to validate the design choices.", "relation_to_broader_scientific_literature": "The work is related to  multiscale autoregressive image generation within the transformer backbone. The motivation is the success of transformers and autoregressive modeling in NLP. This has gained traction for modeling images.", "essential_references_not_discussed": "Literature on multiscale image generation is not new and the work should discuss them in the related work. \nFor example.\n[a] PixelCNN models with auxiliary variables for natural image modeling. ICML 2017\n\n[b] Generating high fidelity images with subscale pixel networks and multidimensional upscaling. ICLR, 2019.\n\n[c] MaCow: Masked convolutional generative flow. NeurIPS, 2019\n\n[d] Pixelpyramids: Exact inference models from lossless image pyramids. ICCV 2021", "other_strengths_and_weaknesses": "+The work is incremental but provides a good solution to advance the field of autoregressive image generation using transformers.\n+The paper is very well written and follows the limitations of prior work which are well addressed with new modeling and formulation.\n- Related work does not do justice to the prior work on multiscale generation even though prior work are not based on transformers, they are still relevant and should be discussed.", "other_comments_or_suggestions": "No", "questions_for_authors": "1. How does the method scale with resolution? Are there any limitations on the resolutions eg, square images in the VAE based framework considered? \n2. Are same number of timesteps used for flow-matching across image scales? \n3. How well does the approach work for long-text conditioning?\n4. How are the positional encodings handled with the VAE based approach and transformers?\n5. What is the level of controllability that can be handled with these models at different scales? for complex images and conditions how does the model ensure faithful generation across scales?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Kd35QxieBG", "reviewer": "Reviewer_6Cuw", "summary": "In this work, the authors propose FlowAR which generates images sequentially at different scales. FlowAR first generates conditional vector of different resolution using AR model and then relies on flow matching model to generate clean image of corresponding resolution. Unlike VAR, FlowAR is more flexible which doesn't rely on curated VAE to acquire the latent space. On standard ImageNet generation benchmark, FlowAR achieves competitive performance comparing to SoTA baselines.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive feedback and address the concerns below.\n\n> Q1: speed comparison with VAR\n\nWhile FlowAR requires additional denoising steps due to the use of flow matching, it is only slightly slower than VAR, and FlowAR-L still achieves a significant 10× speedup over other diffusion- and flow-matching-based models such as MAR, DiT, and SiT.\n\n|model|params|inference time (sec/image)|FID|\n|:---:|:---:|:---:|:---:|\n|DiT-XL|675M|1.7|2.26|\n|SiT-XL|675M|1.7|2.06|\n|MAR-B|208M|1.25|2.31|\n|VAR-d30|2B|0.07|1.97|\n|FlowAR-L|589M|0.12|1.90|\n|FlowAR-H|1.9B|0.24|1.65|\n\n> Q2: more detailed description about the AR and flow-matching Transformer\n\nOur autoregressive and flow matching modules are built upon the VAR and SiT codebases, respectively. As stated in the paper, we will ***fully open-source*** the training and inference code, along with model checkpoints, to enable the community to reproduce our results and examine all implementation details. \n\n> Q3: balance sizes of AR and flow-matching\n\nThank you for the question. We ablate the effect of AR and flow matching model sizes in the table below. Increasing the size of the flow matching module initially improves performance, but further scaling eventually leads to a performance drop.\n\n|model|AR params|Flow matching params|inference time (sec/image)|FID|\n|:---:|:---:|:---:|:---:|:---:|\n|FlowAR-L|504M|70M|0.03 |2.32|\n|FlowAR-L|411M|143M| 0.07|2.01|\n|FlowAR-L(default setting)|309M|280M| 0.12 |1.90|\n|FlowAR-L|152M|420M| 0.19|1.98|\n\n> Q4: visualization of samples\n\nWhile our primary focus is on the image quality at the final scale, we provide intermediate-scale visualizations at [anonymous link](https://anonymous.4open.science/r/Visualization-1A5C/README.md) and will add more intermediate-scale visualizations in the final version.\n\n> Q5: VAE from MAR\n\nTo analyze the impact of different underlying VAEs, we report their reconstruction FID (rFID) in the table below:\n \n|tokenizer|rFID|\n|:--:|:--:|\n|MAR-VAE|0.53|\n|SD-VAE|0.82|\n\nAs shown, MAR-VAE achieves a better rFID, indicating better reconstruction quality, which correlates with improved generation FID (gFID) in both our experiments and those reported in the MAR paper.\n\nTo further validate the effect of MAR-VAE versus SD-VAE, we also compare FlowAR with another generator, UViT, using both tokenizers. MAR-VAE consistently delivers better generation performance across different generator architectures.\n\n|model|MAR-VAE|SD-VAE|gFID|\n|:--:|:--:|:--:|:--:|\n|UViT|√||3.24|\n|UViT||√|3.52|\n|FlowAR-S|√||3.61|\n|FlowAR-S||√|3.94|", "claims_and_evidence": "Yes, claims are supported by convincing evidence.", "methods_and_evaluation_criteria": "Yes, the proposed methods are properly evaluated.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "Yes, experimental designs are sound.", "relation_to_broader_scientific_literature": "The paper is related to diffusion/flow matching model as well as autoregressive model for image generation.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "See \"Questions For Authors\".", "other_comments_or_suggestions": "See \"Questions For Authors\".", "questions_for_authors": "1. What is the inference speed of FlowAR compared with baselines like VAR. How much overhead is there from the flow-matching module?\n2. Can the authors give more detailed description about the AR and flow-matching Transformer? For example, is it based on the implementation of VAR?\n3. Did the authors have ablation study on how to balance sizes of AR and flow-matching models? How are their sizes determined?\n4. Are there visualization of samples generated of different scales in FlowAR which could help better demonstrate what FlowAR learns to generate images. \n5. In table 3, FlowAR with MAR-VAE achieves the best performance. However, MAR used VAR from LDM which is less powerful than SD-VAE. Does authors have any assumptions about why this is the case?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IIzlQF1mqb", "reviewer": "Reviewer_4dzR", "summary": "This paper attempts to address two limitations in VAR work: (1) the complex and rigid scale design and (2) the dependency between the generator and the tokenizer. To address these issues, the paper makes two simplifications: (1) each scale is simply double the previous one; (2) the coarse scale tokens are obtained by directly downsampling the finest scale tokens. These two simplifications make the VAR framework more concise and general, allowing it to be combined with existing continuous tokenizers (VAE) while achieving better generation results despite the simplified design.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive feedback and address the concerns below.\n\n> Essential References\n\nThank you for the suggestions. FlowAR focuses on ***next-scale*** prediction through ***autoregressive modeling***, whereas the referenced works explore diffusion-based approaches with multi-stage architectures [1], multi-scale designs [2], or independent noise levels combined with causal next-token prediction [3]. We will cite and discuss all related works accordingly.\n\n> W1: scale design analysis\n\nWe have ablated the scale design in Table 9 of the Appendix (page 11 in paper). For quick reference, we provide the results below. As shown, even with just three scales ({1, 4, 16}), FlowAR achieves an FID of 6.10—comparable to VAR, which uses a much more complex scale configuration ({1, 2, 3, 4, 5, 6, 8, 10, 13, 16}) and obtains an FID of 5.81.\n\n|method |scales | FID |\n|:--:|:--:|:--:|\n| VAR| {1, 2, 3, 4, 5, 6, 8, 10, 13, 16} |5.81 |\n| VAR| {1, 2, 4, 8, 16}| N/A |\n| FlowAR| {1, 2, 4, 8, 16} |3.61|\n| FlowAR| {1, 4, 8, 16} |4.88 |\n| FlowAR| {1, 4, 16} |6.10|\n\n> W2: speed comparison with VAR\n\nIn the table below, we compare the generation speed and performance of FlowAR with VAR and other widely used diffusion- and flow-matching-based models, including MAR, DiT, and SiT.\n\n|model|params|inference time (sec/image)|FID|\n|:---:|:---:|:---:|:---:|\n|DiT-XL|675M|1.7|2.26|\n|SiT-XL|675M|1.7|2.06|\n|MAR-B|208M|1.25|2.31|\n|VAR-d30|2B|0.07|1.97|\n|FlowAR-L|589M|0.12|1.90|\n|FlowAR-H|1.9B|0.24|1.65|\n\nAs shown, although FlowAR involves additional denoising steps due to flow matching and is slightly slower than VAR, FlowAR-L remains over 10× faster than other diffusion- and flow-matching-based models such as MAR, DiT, and SiT while also achieving superior image generation quality.\n\n> W3: Precision and Recall metrics in Table 1\n\nPrecision and Recall metrics often saturate and offer limited additional insight beyond FID. Therefore, following prior works such as MAR, VAR, DiT, and SiT, we primarily focus on optimizing and reporting FID, which remains the most sensitive and informative metric for assessing image generation quality in our evaluations.\n\n> W4: Update Table 3 with tokenizer’s parameters\n\nThank you for the suggestion. In the updated table below, we include the tokenizer parameters to provide a more complete and transparent comparison.\n\n|tokenizer|tokenizer params |generator |FID|\n|:---:|:---:|:---:|:---:|\n| multi-scale residual VQGAN|109.0M |VAR |5.81 |\n|DC-AE | 323.4M | FlowAR|4.22 |\n|SD-VAE |83.7M | FlowAR|3.94 |\n| MAR-VAE|66.5M | FlowAR|3.61 |\n\n> W5: Change “Any VAE” to “Any continuous tokenizer (VAE)”\n\nThank you for the suggestion. We will update the text to “any continuous tokenizer (VAE)” accordingly.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes. I checked the class-conditional image generation results (ImageNet) and ablation studies, including results for 256 and 512. All benchmarks and metrics follow the VAR paper, and there are no issues with my aspects.", "theoretical_claims": "There are no theoretical claims in this paper.", "experimental_designs_or_analyses": "Yes. I checked the class-conditional image generation results (ImageNet) and ablation studies, including results for 256 and 512 resolutions. These experimental designs and evaluation metrics follow the VAR paper, and I see no issues.", "relation_to_broader_scientific_literature": "NA", "essential_references_not_discussed": "The idea of combining flow-matching with multi-scale latents has been widely applied in previous works [1,2]. Additionally, the paradigm of leveraging previous scales to better generate new scales has already been proposed [3]. However, this paper lacks the corresponding citations.\n\n* [1] f-dm: A multi-stage diffusion model via progressive signal transformation, ICLR 2023\n* [2] Pyramidal Flow Matching for Efficient Video Generative Modeling, ICLR 2025\n* [3] Diffusion forcing: Next-token prediction meets full-sequence diffusion, NeurIPS 2024", "other_strengths_and_weaknesses": "## Strengths:\n1. This paper simplifies the complex scale design in VAR, making the VAR framework compatible with any continuous tokenizer (VAE), simplifying the framework to make it more general while also improving the performance.\n2. Using autoregression to generate semantics and using them as conditions for diffusion (flow matching) is a reasonable idea.\n3. The paper's writing and figures are clear and easy to understand.\n\n## Weaknesses:\n\n1. The authors lack detailed analysis of the proposed scale design. For example, why did the paper simply double the scale rather than triple it? Why were only 5 scales designed? How would the results be affected with more or fewer scales? Providing analysis and results for these design choices would be helpful.\n2. The method is essentially a combination of Autoregressive and Diffusion approaches, where Autoregressive integrates information from multiple scales to generate semantics, which then serve as conditions for Diffusion (FlowMatching) generation. Compared to the original VAR, this method might lead to slower generation speed. However, the paper lacks experiments comparing generation speeds and related analysis.\n3. In Table 1, FlowAR's performance advantage mainly comes from FID and IS, but the improvements in Precision and Recall metrics are not substantial. Could the authors provide more analysis and explanation for this experimental phenomenon?\n4. The direct comparison of different tokenizers in Table 3 may not be entirely fair, as different tokenizers have different parameter counts. It would be helpful if the authors added parameter count comparisons for the Tokenizers (VAEs).\n5. The \"Any VAE\" in Figure 2 might be ambiguous; it's suggested that the authors change it to \"Any continuous tokenizer (VAE)\".", "other_comments_or_suggestions": "NA", "questions_for_authors": "NA", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes FlowAR, a multi-scale image generation approach combining autoregressive modeling with flow-matching, showing strong performance improvements on class-conditional ImageNet generation compared to previous methods such as VAR. Reviewers generally agree that the paper makes valuable contributions. Given the positive reviewer consensus post-rebuttal, I recommend acceptance for this paper.
Decision: Accept (poster)