EchoGen: Generating Visual Echoes in Any Scene via Feed-Forward Subject-Driven Auto-Regressive Model
Authors: Ruixiao Dong
Zhendong Wang
Keli Liu
Li Li
Ying Chen
Kai Li
Daowen Li
Houqiang Li
Venue: ICLR 2026 Poster (2026)
Keywords: Subject-driven Image Generation; Autoregressive Generation
Primary area: generative models

Abstract: Subject-driven generation is a critical task in creative AI; yet current state-of-the-art methods present a stark trade-off. They either rely on computationally expensive, per-subject fine-tuning, sacrificing efficiency and zero-shot capability, or employ feed-forward architectures built on diffusion models, which are inherently plagued by slow inference speeds. 
Visual Auto-Regressive (VAR) models are renowned for their rapid sampling speeds and strong generative quality, making them an ideal yet underexplored foundation for resolving this tension.
To bridge this gap, we introduce EchoGen, 
a pioneering framework that empowers VAR models with subject-driven generation capabilities.
The core design of EchoGen is an effective dual-path injection strategy that disentangles a subject's high-level semantic identity from its low-level fine-grained details, enabling enhanced controllability and fidelity. 
We employ a semantic encoder to extract the subject's abstract identity, which is injected through decoupled cross-attention to guide the overall composition. Concurrently, a content encoder captures intricate visual details, which are integrated via a multi-modal attention mechanism to ensure high-fidelity texture and structural preservation.
To the best of our knowledge, EchoGen is the first feed-forward subject-driven framework built upon VAR models. Both quantitative and qualitative results substantiate our design, demonstrating that EchoGen achieves subject fidelity and image quality comparable to state-of-the-art diffusion-based methods with significantly lower sampling latency.

Published as a conference paper at ICLR 2026
ECHOGEN: GENERATING VISUAL ECHOES IN ANY SCENE VIA FEED-FORWARD DIBJECT-DRIVER AUTO-
REGRESSIVE MODEL
Ruixiao Dong Zhendong Wang Keli Liu
Li Li
Ying Chen
Kai Li
Daowen Li Houqiang Li
University of Science and Technology of China
2 Alibaba Group
fdongruixiaoye zhendongwang, sa23005063 ustc. edu. (1i11, 1 linq) fuste. www.ss..eeeee edu. en
(chenying. ailab, kaishi. 1k, lidaowen. ldw)ealibaba com
jingle
style
nop
on white rug
"EchoGeen" sign
with backgrsuug
comic style
fleot ongel sings
Figure 1: Feed-forward subject-driven generation by EchoGen. By employing a visual autore- gressive paradigm, EchoGen achieves both high-quality image synthesis with lower latency, pre- serving intricate subject identity with exceptional efficiency.
ABSTRACT
Subject-driven generation is critical task in creative AI; yet current state-of-the- art methods present a stark trade-off. They either rely on computationally ex- pensive, per-subject fine-tuning, sacrificing efficiency and zero-shot capability, employ feed-forward architectures built on diffusion models, which are inherently plagued by slow inference speeds. Visual Auto-Regressive (VAR) models are renowned for their rapid sampling speeds and strong generative quality. making them an ideal yet underexplored foundation for resolving this tension. To bridge this gap, we introduce EchoGen, pioneering framework that empowers VAR models with subject-driver generation capabilities. The core design EchoGen is effective dual-path injection strategy that disentangles subject's high-level semantic identity from its low-level fine-grained details, enabling enhanced con- trollability and fidelity. We employ semantic encoder to extract the subject's abstract identity, which is injected through decoupled cross-attention to guide the overall composition. Concurrently, a content encoder captures intricate visual details, which are integrated via a multi-modal attention mechanism to ensure high-fidelity texture and structural preservation. To the best of our knowledge. EchoGen is the first feed-forward subject-driven framework built upon VAR mod- els. Both quantitative and qualitative results substantiate our design, demonstrat- ing that EchoGen achieves subject fidelity and image quality comparable to state- of-the-ar diffusion-bar based methods with significantly lower sampling latency.
INTRODUCTION
The rapid evolution of text-to-image synthesis models (Saharia et al. 2022 Rombach et al., 2022 Batifol al., 2025 Esser et al. 2024) has catalyzed a variety of novel applications (Zhang et al,, 2023). among which subject-driven generation stands out as an important task. This task aims
"qqual contribution. Corresponding authors.

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to accurately depict a specified subject within diverse, user-defined scenes described through text prompts, while rigorously upholding the subject's core identity. The early approaches (Ruiz et al.) 2023 Gal et al. 2022 Kumari et al. 2023) introduced test-time fine-tuning paradigm that op- timizes a large pretrained model using a few images for each new subject. Although effective in preserving identity to some extent, this per-subject optimization process is computationally expen- sive, demanding at least hundreds of training iterations and substantial GPU resources, ultimately resulting in a distinct model checkpoint for each subject. These limitations significantly hinder the practicality and scalability of the test-time fine-tuning paradigm in real-world applications.
To improve efficiency and practicality, a new class of feed-forward approaches has recently emerged (Li et al., [023] Pan et al., 22024; Ye et al.] [2023; Tan et al., [2025] [Shin et al. 2025)
based on diffusion models (Rombach al. 2022 Podell et al. 2024 Batifol et al., 2025). ). In-
stead of fine-tuning on a small set of images for each new subject, feed-forward approaches perform a single, large-scale supervised fine-tuning on a vast dataset composed of triplets (text, reference image, target image). The model is trained to learn a generalizable mapping from a subject image to the snapshot version in the specified scene. The single process of pretraining enables zero-shot generation at inference time-a a novel subject can be synthesized immediately without any subject- specific fine-tuning, significantly reducing the initial setup cost and decreasing generation latency by eliminating the need for test-time optimization. Nevertheless, these methods still inherit the computational demands of the underlying diffusion models due to the iterative denoising process.
Inspired by autoregressive generation in language models (Radford et al.. 2018; (Achiam et al., 2023), autoregressive visual generation (Esser et al. [021] Ramesh et al.) 2021; Sun et al. 2024) has emerged as compelling alternative to diffusion models. Unlike diffusion 's iterative denoising. autoregressive models synthesize content sequentially, token by token. This paradigm is further advanced by the Visual Autoregressive (VAR) model (Tian al., 2024 Han et al. 2025), which employs a coarse-to cossee-toffine fine next-scale generation strategy instead of traditional next-token generation. first generates tokens for the global composition and then renders fine-grained details, capturing a complete hierarchical representation from structure to texture. The novel paradigm allows VAR to achieve superior performance compared to traditional autoregressive models, outperforming top-tier diffusion models while offering faster inference speed. Despite the inherent suitability of the autore- gressive paradigm for fine-gra -heigraieed conditioning, its potential for controllable generation, especially in the wowwadd subject-driven context, remains largely untapped compared to the wealth of research on diffusion-based methods. This critical gap severely limits the practical applicability of VAR models, hindering their adoption in real-world scenarios where subject control is paramount.
In this work, we aim to bridge this gap by leveraging the inherent advantages of VAR to build an effective, scalable, and highly controllable system for subject-driven image synthesis. We propose EchoGen, the first efficient feed-forward autoregressive framework that generates faithful visual renditions of a given subject in arbitrary scenes. At the core of EchoGen is a dual-path injection mechanism that disentangles semantic features from fine-grained details. We inject high-level fine- grained semantic features extracted by a semantic encoder based on the pretrained vision foundation model (DINOv2 (Qqubb et al. 2024h) into the decoupled cross-attention layers (Kumari et al. 2023) to bring structural and stylistic coherence while avoiding drift in prompt following. To enable global semantic conditioning, we prepend the global semantic embedding extracted from DINOv2 as prefix and subsequently infuse it via Adaptive LayerNorm, thereby steering the overall semantic generation. However, generating with semantic features alone often misses low-level details. To complement these features, a second pathway employs pretrained content encoder (FLUX. 1-dev VAE (Batifol et al.] 2025) to extract fine-grained image features, which are incorporated via multi-modal attention module, ensuring faithful reconstruction of local textures and details. To preserve the generative capabilities of the pretrained VAR model, we adopt a parameter-efffcient fine-tuning strategy that freezes the backbone and only updates key components within the subject injection modules. Extensive quantitative and qualitative evaluations on DreamBench (Ruiz et al.) 2023) benchmark and human evaluation demonstrate that EchoGen achieves subject fidelity, text alignment, and image quality comparable to and even exceeding state-of-the- diffusion-based methods, while exhibiting lower sampling latency.
Our principal contributions can be summarized as follows:
We introduce EchoGen, the first feed-for efficient, subject-driven generation frame- work built upon visual autoregressive model. This establishes compelling new paradigm
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for controllable subject-driven synthesis beyond the dominant diffusion- approaches.
We propose novel dual-path injection strategy that disentangles the identity of a sub- ject into high-level semantics and fine-grained details. By injecting these features through separate pathways within garameter--fffcientt tuned model, EchoGen achieves faithful subject representation across diverse scenes.
Extensive experiments demonstrate that EchoGen achieves subject fidelity, text alignment, and image quality that are competitive with or superior to statc-of-the-ar diffusion-base methods with much faster inference speed.
2 RELATED WORKS
2.1 AUTOREGRESSIVE IMAGE GENERATION
Unlike diffusion- based methods that synthesize images via iterative denoising. the autoregressive paradigm models image distributions by sequentially predicting visual tokens conditioned on the preceding context. This approach evolves from inefficient and low quality early pixel-level meth- ods (Van den Oord et al. 2016) [Salimans et al., 2017) to dominant two-stage framework that first compresses images into discrete tokens and then models their distribution utilizing Trans- former (Esser et al. 2021). This paradigm substantially improves generation fidelity and efficiency, underpinning advances in text-to-image synthesis (Ramesh et al. 202T; Yu et al. 2022b) and con- trollable generation (Li et al. 2025)- Subsequent work further refines it by improving image tok- enizers (Yu et al.] [2022a] Mentzer et al. (024). exploring continuous representations with diffusion modeling (Li et al. [024a]] Fan et al. (025), or adapting large language models for visual gener- ation (Sun et al.) 2024 Wu et al. 2024). To mitigate structural degradation induced by the fixed raster-scan order, Visual Autoregressive (VAR) models (Tian et al. 2024) introduce hierarchical coarse-to-fine fine strategy that progressively refines fine-grained details by next-scale prediction. The following version Infinity (Han et al, (022) extends the VAR model to text-to-image generation, achieving superior quality with significantly lower sampling latency than diffusion models. While existing works extend VAR to controllable generation (Yao et al.) 2024 Li et al.) (024b) [Chung al. (025), feed-forward subject-driven personalization remains underexplored, limiting the prac- tical applicability of the VAR framework.
2.2
SURJECT-DRIVEN IMAGE GENERATION
Test-time fine-tuning methods. Diffusion models (Ho et al. 2020 Rombach et al. 2022 have achieved remarkable success in high-fidelity text-to-image (T2I) synthesis (Podell et al. 2024 Esser
al. 2024 Batifol et al. 2025). For subject-driven tasks, relying solely on text prompts is often insufficient to preserve the defining characteristics of specific subjects. To address this, pioneering methods (Gal et al., 2022, Ruiz et al., 2023) Kumari et al., 2023) introduce customization by fine- tuning small set of reference images for each target subject. While these approaches can capture intricate details and deliver high fidelity to some extent, their dependence on per-subject optimization remains time-consuming and computationally demanding, which limits practical use.
Feed-forward subject-driven approaches. To overcome the efficiency limitations of per-subject optimization, forward methods have been developed (Wei al. 2023 Zeng et al.] [024] Patel
et al. 2024: Ma et al.) 2024 Wang et al. 2025). These models are trained once to condition on subject features from vision encoders, enabling fast, zero-shot synthesis for novel subjects. Early works such as BLIP- Diffusiin (Li et al. 2023) jointly fine-tune the denoising network with multi- modal alignment modules while suffering from inadequate fidelity and image quality. To mitigate the high computational cost of full model tuning, earfmeter-efffccient strategies (Pan et 2024, Ye
jt al., 2023 Tan al., 2025 Zhang et al. 2025 Wu et al. (025) incorporate lightweight modules such as LoRA (Hu et al. 20022) or adapters. These modules inject reference features into the diffu- sion transformer, typically via attention mechanisms, while keeping most pretrained weights frozen. However, since these methods all rely on diffusion backbones, they inherit the substantial inference latency of the iterative denoising process, which constrains their practical deployment.
3 PRELIMINARY OF VISUAL AUTOREGRESSING MODELING
Autoregressive models (Esser et al. 202T) reframe image synthesis as a sequential token predic- tion, under the next-toker token prediction. The image is first tokenized into discrete feature map
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1 3 4 R, R2
0
Trage Tikes
Trozen Modds
EchoGen Block
Garient Festure
Taken
x.V
EchoGen
Decoupled Gress Attention
K/V
K/V
Text
Content Enceder
Semontic Enceder
Infinity Excoder
Encoder
Malti- Modal Attentton Mask
Multi- Hodai Attention
QA/V
Q/K/V
Frompt
Image
Ground-
Figure 2: Overview of the EchoGen architecture. The left panel illustrates the overall model framework with dual-path subject injection, while the right panel provides a detailed schematic of the EchoGen block with carefully designed attention mask applied the Multi-Modal Attention module to avoid feature leakage. C' denotes the global semantic token extracted from the semantic encoder, which prepended to the input sequence. represents the start token for the first-scale generation. Adaptive Layer Normalization modules in the EchoGen blocks are omitted for clarity.
ing visual tokenizer and then flattened into a one-dimensional sequence, typically following the raster scan order. The model is then trained to predict each token 2; given the preceding to- kens (I, x1-1) and the condition c, factorizing the sequence distribution as I, [c) = I p( c). However, the vanilla next-token paradigm with fixed raster order in- duces structural degradation and insufficient modeling.
Visual autoregressive modeling (Tian et al. 2024) addresses the above issues by shifting the predic- tion paradigm from next-token next-scale: instead of predicting one token at a time, predicts entire token maps at progressively increasing resolutions. The visual encoder first maps an image to latent F, and then produces multi-scale token maps (ri,...Kk) with increasing resolutions ha x wx by applying residual vector quantizer. A GPT--style Transformer begins from the gener- ation of the 1x1 map 71 and autoregressively predicts each subsequent scale given prior scales and condition achieving generation from global structure to fine details, which is formulated as:
p(ri,...,TKe)) = II ( 1=1
This scale-wise coarse-to-fine paradigm is well suited for scalable text-to-image generation. The text-to-ine -it-te-immgeeea generation model Infinity (Han et al. 2025) leverages bitwise quantization to expand the vocabulary size under the next-scale paradigm, reporting state-of-the-an performance with re- duced sampling latency compared to diffusion baselines. In this paper, to bypass the cumbersome per-subject fine-tuning and the heavy computational cost during inference, we propose novel feed- forward framework based on VAR models, featuring single parfieter-efficient fine-tuning phase.
ECHOGEN
4.1 OVERALL FRAMEWORK
We are secking novel feed-forward framework for subject-driven generation built upon Infinity, based on the proposed EchoGen block with effective dual-path subject information injection, in which a content encoder and semantic encoder cooperate to provide comprehensive subject fea- tures from both sides of coin. The overview of the EchoGin architecture and its basic block illustrated in Figure 2 Before subject injection, to ensure robustness against background noise that may interfere with subject injection, pipeline based the multi-modality model Qwen2.5- VL (Bai et al. 2025) and the open segmentation model GroundingDINO (Liu et al. 2024) is care- fully designed segment the subject from complex scenes. Given the segmented subject image, our EchoGen model is trained using parameter-
methodology that freezes the pretrained backbone while fine-tuning only newly introduced attention modules. During inference, apply
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flexible subject-text classifier-free guidance for explicit control over the trade-off between subject fidelity and textual aligmment, enabling versatile and controllable generation.
4.2 DUAL-PATH SUBJECT INJECTION
Semantic feature injection for identity preservation. The semantic feature, which captures ab- stract characteristics, provides a representation that is critical for avoiding the identity drift com- mon in subject-driven generative models. Following this principle, we introduce a bifurcated in- jection strategy that targets both the fine-grained and global levels of the generative process. For fine- une-ggrined conditioning. we employ the pretrained DINOv2 vision encoder to extract patch-level semantic embeddings. These embeddings are synergistically integrated with the original textual conditioning via a decoupled cross-attention mechanism (Kumari al. (023). Our decoupled cross- attention mechanism operates query features Z, conditioning them on both the text embedding Ct and the fine geee-graind semantic features C2. formulated as follows:
Q 2 W K concal (cs W Ct W V concat (c. WT, c W?"),
Z' Attention (2,K, V) Softmax @K (QKT///]) /V//vaa
where W is the query projector, (WA. W and (W*, W.") are two distinct sets of (k, u) pro- jectors to embed text prompting a and semantic injection Cs, respectively. The resulting key and form the final context vectors K. and V. We keep
value pairs for each condition are concatenated the projectors for text prompting (Wr, Wy)) and the query projector W frozen while exclusively optimizing the key and value projectors ( [Wr. W(") that map the semantic features of the reference images, enabling an alignment mapping from the semantic visual space to the generator's latent space without perturbing the pretrained knowledge.
Moreover, we prepend the DINOv2 global semantic token to the input sequence to impose holistic semantic guidance. At the same time, this global token also serves as condition for the Adaptive Layer Normalization (AdaLN) layer in the proposed EchoGen block, following (Han et al.) 22255) The infusion of fine-grained and global semantics ensures comprehensive semantic-informed gen- eration, promoting fine-grained fidelity and global structural coherence.
Content feature injection for detail preservation. While the semantic embeddings provide a ro- bust identity preservation, their high abstraction leads to generation with insufficient subject details. To achieve high fidelity the subject's 's content, we complement it with a content feature infusion mechanism. To be specific, EchoGen employs the FLUX. VAE to extract low-level content features cc. which are then integrated via the multi-modal attention. The generation process is then steered by carefully designed attention operation: generated tokens have unobstructed access to the reference tokens, allowing them to distill fine-grained visual cues on demand; conversely, a causal mask renders the reference tokens oblivious to the generated sequence, which is critical constraint for ensuring the autoregressive sampling trajectory. This masking schema is precisely demonstrated in the lower-right inset of Figure Specifically, given the generated token sequence 2 and the detailed content condition ce. the multi-modal attention utilizes separate linear projections ( (W,, W Wo) for Z and ( (W2, W W.) for the condition Cc, with the applied attention mask Mask, and then calculate the generated sequence Z' and condition co' via:
2 = concat (2 W Ce W K. = concat (2 ce W V concat (2 W Ce W
2', = Attention (2, /C, V, Mask) = Softmax (Mask (oTT/va)>
(3)
The pathways for the generated token sequence remain frozen, while exclusively parallel attention projectors (W2, W... W and FFN modules for processing content features are optimized.
Through this dual-path subject injection strategy, our model faithfully preserves the salient visual characteristics of the reference image while simultaneously maintaining a strong adherence to the provided text instructions.
4.3 SUBJECT SEGMENTATION
common challenge real-world scenarios that user-provided reference images comprised of
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the subject of interest within visually complex backgrounds may harm the performance of sub- ject injection. To mitigate this issue, we employ a subject segmentation pre-processing pipeline, illustrated in Figure First, the Qwen2.5-
Extractie
ta
Bounding Box
VL (Bai et al., 2025) vision-language model Figure 3: The pipeline of subject segmentation.
identifies the subject's semantic identity, producing a descriptive text prompt. This prompt is then used to condition the GroundingDINO (Liu et al.) 2024) model for precise subject localization and bounding box generation. The foreground region subsequently cropped according to this bounding box, while the surrounding unrelated regions are explicitly discarded and replaced with uniform white background. This process ensures that subsequent feature injection operates attentively on the isolated representation of the referred subject.
4.4 SAMPLING WITH SUBJECT-TEXT FLASSIFIER-FRES GUIDANCE
Classifier-Fres Guidance (CFG) (Ho & Salimans 202T) has become a cornerstone technique for enhancing conditional control in generative models, especially in diffusion models. Its core principle to amplify the conditional signal by extrapolating from an unconditional prediction towards a conditional one, thereby improving condition following the cost of some diversity. Recently, many autoregressive models (Chang et al.] [003] Tian et al., 2024) have also incorporated CFG into their frameworks. In this work, further enhance the influence of the text embedding or and the subject condition Cs, C. within the CFG scheme for subject-driven generation. During training, we independently replace the text condition G with unconditional token Ø, and the image condition ca. Ce with unconditional embeddings 2., Øe. each with a probability of 10%. During inference, assuming the independence between the text condition e; and the image condition c., Co, we compute the final logits predicted by EchoGen via flexible guidance rule that integrates both controls:
i= 1(2,, 2., Ø.) + x (M(, 2,, 2.) 1(2,, 2. Ø.)) + x (I(ce, Co, Ge) I(c, 2, Ø.), (4)
where 1 denotes the Transformer output logits, and together with "/r are hyperparameters that govern the guidance scales. This dynamic text-subject guidance not only strengthens the influence of text embeddings and image prompts, thereby improving generation performance, but also provides a flexible mechanism to balance text alignment with the reference preservation.
EXPERIMENT
5.1 SETUP
Datasets. We conduct experiments merged dataset curated from the Subjects200K (Tan et al. 2025) and UNO-IM datasets (Wu et al. 2025), yielding a large-scale high-quality corpus of ap- proximately 640,000 triplets (text prompts, reference images, and target images). The corpora were synthetically generated using large language models (e.g., GPT-4o) and text-to-image generative models (e.g., 1-d and with image resolutions larger than 500 For EchoGen-O. IB training. both the reference and target images are resized and center-cropped to 256x256. To en- able high-resolution generation for EchoGen-2B training, we avoid direct interpolation. which may introduce undesirable artifacts; instead, we upscale the images to 1024x 1024 using the PiSA-SR super-resolution model (Sun et al., 2025).
Training details. Our training protocol largely follows Infinity Han et al. 2025). We train EchoGen for 80K iterations, utilizing the Adam W (Loshchilov Hutter 2017) optimizer with a global batch size of I 128, setting the base learning rate as 3 x 10-5 and the momentum parameters (18,83) (0.9, 0.97). To stabilize fine-tuning, we apply a reduced learning rate of x 10-6 6 to the multi-modal attention parameters. More training details can be found in the appendix 7.3.1
Following prior works (Ruiz et al. 2023 Li et al. 2023), we evaluate our approach in terms of subject fidelity and text alignment the DreamBench benchmark (Ruiz et al. 2023). Sub-
Evaluation.
ject fidelity is measured by the cosine similarity between the generated and reference images using both CLIP (Radford et al. 2022)) image embeddings (CLIP-I) and DINO (Zhag et al. 2022 features (DINO). Text alignment is assessed via the CLIP cosine similarity between the generated image and its corresponding input prompt (CIIP-T).. DreamBench, comprising real-world images with prompt

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Method
Base Model
DINO1 CLIP-I1
CLIP-TY
Latency
Test-fime Fine-tuning
Textual-Inversie Gal et al.
2022)
SD-v1.5
0,569
0.780
0.255
50min
DrcamBooth (Ruiz al. 2023)
SD-v1.5
0.668
0,803
0.305
15min
BLIP-Diffusion (Li ct al.
2023
SD-v1.5
0.670
0.805
0.302
AR-Booth (Chung et al. 2025)
Infinity-2B
0.750
0.808
0.269
2.8h
Unified Generation
OmniGen (Xiao al.
2025
OmniGen
0.693
0.801
0.315
93.4s
Feed-Forward
ELITE (Wei et al. 2023
SD-v1.4
0.621
0.771
0.293
11.0s
Re-Imagen (Chen et al. 2023)
Imagen
0.600
0.740
0.270
BLIP-Diffusinn (L. et al. 2023
SD-v1.5
0.594
0.779
0.300
X-Eclipse (Patel al.
Kan-v2.2
0.613
0.783
0.307
MS-Diffusion (Wang et al. 2025
SDXL
0.671
0.792
0.321
39.6s
IP- ---Atppeer Ye al.
2023
SDXL
0.613
0.810
0.292
16.9s
A--Atereer Ye
2023)
FLUX.I-dew
0.561
0.725
0.351
OminiControl Tan et al.
(0225)
FLUX.1-dev
0.684
0.799
0.312
27.5s
EasyControl
Zhang et al. 2025
FLUX.I-dev
0.652
0.789
0.325
25.4s
EchoGen-O.II
Infinity-0.11
0.675
0.806
0.321
0.5s
EchoGen-2B
Infinity-2B
0.755
0.835
0.325
5.2s
Table 1: Quantitative comparisons on DreamBench (Ruiz et al. 2023).
We highlight the best
second-best, and third-best values for each metric. The results indicate that EchoGen attains performance on par with diffusion iifiusion-based approaches while delivering substantially faster sampling.
Method
Subject Fidelityf
Text Alignment
Photorealismt
OmniGen (Xiao al. 2025
0.15
0.13
0.09
IP-adapter YYe et al. 2023
0.21
0,05
0.14
OminiControl (Tan et al.
2025
0.12
0.21
0.15
EasyControl (Zhngg al.
2025
0.15
0.31
0.28
EchoGen-2B
0.37
0.30
0.34
Human evaluation. We compare our method with previous approaches based on three aspects: text alignment, subject fidelity, and photorealism.
Table 2:
annotations, includes 30 unique subjects, each paired with 25 distinct prompts. Following the evalu- ation protocol of (Pan al., (024), we select one reference image per subject, generate four images for each prompt-subject pair, yielding 3,000 generated images in total. DINO and CLIP-I scores are computed by comparing each generated image against corresponding reference image. The sampling latency is measured H20 GPU for all methods.
5.2 MAIN RESULTS
We compare EchoGen with three categories of prior works: (1) test-time fine-tuning methods that require per-subject optimization; (2) unified generation models with large-scale pre-training: and (3) feed-forward approaches that share the same paradigm as ours and constitute our most baselines.
Quantitative results. We benchmark EchoGen performance against contemporary subject-driven diffusion- based methods in the DreamBench dataset (Ruiz et al. 2023), with quantitative results summarized Table []] EchoGien achieves performance that comparable or superior leading diffusion- based approaches in the core metrics of subject fidelity and text alignment, and demon- strates balanced performance across evaluation axes. In contrast, several baselines, such as IP- Adapter (Ye et al. 2023) exhibit significant weaknesses in specific metrics. Furthermore, the adop- tion of the visual autoregressive paradigm provides a clear efficiency advantage: EchoGen's infer- ence latency for 1024x 1024 image under 6 seconds, representing significant acceleration the more than 10 seconds required by its inffuusion-based counterparts. Overall, these results indi- cate that EehoGen combines strong generative quality with markedly improved efficiency, offering

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Reference
IP-Adapter
OmniGen
OminiControl
EasyControl
EchoGen-22 2B
clay teapot top of wooden floor
A tabby cat in the snow
duck toy floating on top water
A grey sloth plushie cobblestone street
A fringed cream boot on top of green grass with sunflowers around it Figure 4: Qualitative comparison with diffusion- methods on DreamBench (Ruiz et al., 2023). For fair comparison, we adopt the default sampling settings for all baseline models.
competitive alternative for subject-driven synthesis. Since existing methods often adopt different evaluation protocols, we re- re-implement representative diffusion -ased baselines and compare them with our model under a unified setting (see Section (744). The results demonstrate that our approach achieves comparable superior performance while consistently reducing inference latency.
Qualitative results. Figure presents rigorous qualitative comparison with prominent diffusion- based frameworks, revealing substantial advantages of our model in both subject fidelity and prompt correspondence. EchoGen exhibits the ability to render high-fidelity details, such as the precise reconstruction of the teapot spout and the nuanced texture of the sloth plushie, and attribute this capability to our dual-path semantic-contem. feature injection design. In contrast, baselines including IP-Adapter (Ye et al.] (202)) and OminiControl (Tan et al., 2025) exhibit characteristic failure cases, corroborating EchoGen's robustness. EchoGen also demonstrates more consistent compliance with textual prompts, avoiding the language deviations observed in the generations of the duck toy and cat instances by IP-Adapter.
Human evaluation. To assess the perceptual quality EchoGen, we conduct human evaluation study against strong baselines that span multiple categories of subject-driven methods. We focus on three criteria: alignment, subject fidelity, and photorealism. The images generated con- ditioned on the reference images and prompts sampled from DreamBench (Ruiz et al. 2023) and DreamBench++ (Peng al. 2024) benchmarks without cherry-picking, and for each criterion, par- ticipants select their preferred generated image among the outputs from five methods. We collect 450 responses from 25 participants, all with expertise in generative models, and report preference ratios in Table 2 The results show that EchoGen is preferred for subject fidelity and photoreal- ism, surpassing all the diffusion-basse contemporary baselines on these criteria. For text alignment, EchoGen performs on par with EasyControl (Zhang et al. 2025) and exhibits clear advantage over the other compared methods.
Sampling Latency Analysis. We conduct thorough analysis evaluate the performance-latere trade-offs across all methods. Specifically, we re-produce diffusion- based methods with varying numbers of denoising steps in evaluation protocol and report their performance versus sampling
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0.70
0:335
Soate
CL.P Sooee
0.00
6.75
9:11
0.88
6059
IPAddeer
IP-Adaperr
0.90
6.725
Leren
0.45
Claptol
0.700
EcheGan-28
EchoCro-28
Eduxes-38
0.40
0675
35
20
10
25
20
25
Sumpling Laazncy
Sumpling Lakany (c)
Serpingg Latency
Figure 5: Performance v.s. sampling latency comparison among EchoGen and baselines.
Enc.
DINO
CLIP-I"
CLIP-TT
Exp.
DINOT
CLIP-I
CLIP-TT
SigLIP-2
0.438
0.720
0.320
wo prefix
0.632
0.788
0.328
FLUX.1 1-vvv
0.433
0.706
0.320
w prefix
0.670
0.798
0.322
DINOv2
0.632
0.788
0.328
Ablation study on incorporating the global semantic features of reference images.
Table 5:
Table 4: Significance of fine-grained semantic injection. "Enc." denotes the encoder type.
latency in Figure 5 For the diffusion baselines, increasing the number of denoising steps improves subject fidelity (as measured by DINO, CLIP-I scores) up to a saturation point. In contrast, the text alignment (CLIP-T) score converges much earlier. Our model consistently offers better trade-off, achieving comparable performance with significantly lower sampling latency than the diffusion- based baselines. This confirms the inherent efficiency and effectiveness of our approach.
The detailed component-wise sampling latency of our EchoGen framework is provided in Ta-
Model Component
Sampling Latency (s)
ble 3 The results confirm that the frame- work's overall efficiency is not limited by aux- iliary components such as Grounding-DINC EchoGen maintains significant speed ad- vantage over diffusion-based methods, even with the inclusion of the optional Qwen2.5-VL. model. Although this model is employed dur- ing training automate subject identification for the GroundingDINO segmentation model, it required during inference. Instead, users can provide a descriptive text prompt (akin to the DreamBench format) for specifying the subject.
Grounding- DINO
0.24
Semantic encoder
0.01
Content encoder
0.02
Infinity generator
4.95
Qwen2.5-VL(Optionna)
1.13
EchoGen (w/o Qwen2.5-VL) EchoGen (w/ Qwen2.5-VL)
5.22
6.35
Table 3: Per-component sampling latency mea-
sured on a single H20 GPU.
5.3 ABLATION STUDIES
We conduct a series of ablation studies to verify the effect of each component in EchoGen. Owing to computational constraints, all experiments are conducted on EchoGen-O.. IB, using the same training settings ensure fair ablation studies.
Significance of fine-grained semantic information injection. Fine-grained semantic conditional information is critical as it provides guidance for establishing the structure, enabling the model to synthesize stylistically and structurally coherent features consistent with the subject. Conversely, we argue that overly coarse-grained semantic features may fail to provide sufficient guidance for gener- ating visually consistent echoes. To validate the importance of incorporating fine-grained semantic information, we conducted an ablation study with three distinct feature types independently injected via cross-attention:: () coarse-grained semantic identity from SigL IIP-2 (Tschannen et al. 22225) (2) fine-grained semantic features from DINOv2 and (3) FLUXX. 1ddev VAE features, which lack enough semantic information. Table 4 demonstrates that the fine-gra inn-ggrined semantic DINOv2 features are the most suitable to represent the echo information this task, as evidenced by all criteria. The fail- of the SigLIP-2 and FLUX.1-de VAE features be attributed to their respective limitations: the former relies on features that are too coarse to guide subject generation, while the latter lacks semantic information.
Incorporating the global semantic information. To provide stronger guidance when constructing the global structure in the generation process, we prefix the global semantic C token extracted by the

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Published as a conference paper at ICLR 2026
DINOv2 semantic encoder and incorporate this token into the model via Adaptive LayerNorm. As demonstrated in Table 3 compared with solely relying on injecting fine-grained semantic features via cross-attention, the introduction of the global semantic token yields substantial performance gain in consistency of subject characteristics, validating the effectiveness of incorporating global semantic information.
Distinct semantic feature injection strate- gies. We explore the most effective method to guide the synthesis process conditioned on the semantic features of reference images. Ta- ble O presents an analysis comparing two dis- tinct feature injection modules: multi-modal at- tention and cross-attention. Our results indicate that while the multi-modal module achieves slightly better aligmment with text prompts, the cross- attention mechanism yields significantly superior subject fidelity, as evidenced by a notably higher DINO score. Based on this, we opted to utilize cross-attention for injecting the semantic features in all subsequent experiments, rather than the
Module
DINOT
CLIP-I
CLIP-TY
MM-Attn
0.646
0.792
0.325
Cross-Attn
0.670
0.798
0.322
Table 6: Different methods for incorporating semantic features.
attention.
Enhancing subject fidelity with detailed con- fent features. Considering the absence of lo- cal details in the semantic features of the refer-
Exp.
DINOt
CLIP-It
CLIP-TY
baseline
0.670
0.798
0.322
ence images, we incorporate secondary path- way that injects localized content features of the subject. These features, extracted by the FLUX.I-dev VAE, are used to guide the syn- thesis of the fine-graine local details of the subject. The ablation study detailed in Table 7 shows that employing a multi-modaa attention mechanism to infuse these content features substantially improves the subject-fidelity of the generated samples, yielding significant increase in CL.IP-I.
+Cross-Attn
0.667
0.803
0.318
+MM-Attn 0.672
0.806
0.321
Table 7: Impact of injecting subject details.
Qualitative analysis of the effect of se- mantic and content feature injection. We further qualitatively dissect the effect of each feature component to validate our design. As shown in Figure 6 starting from the base Infinity backbone, intro- ducing semantic features extracted by DI- NOv2 enables the generator to synthesize subjects that faithfully preserve the refer- ence subject's structure and style. More- over, further incorporating content fea- tures from the FLUX.1 VAE signif- icantly enhances EchoGen's capability render fine-grainede coherent details (e.g., the facial features of the robot toy and the fluffy dog, as well as the material and color of the shoe uppers). These qualitative re- sults confirm the effectiveness of our dual-path injection design, where semantic and content features play distinct yet complementary roles.
Reference
Infieity
Sementic
EcheGes
fifffy firefightor outfit
bookground
fringed top green gress with aunlowers around
Figure 6: Qualitative analysis of the effect of seman- tic and content feature injection.
6 CONCLUSION
This paper presents EchoGen, novel framework for efficient, feed-forward subject-driven image synthesis based on a visual autoregressive paradigm, aiming to inherit the properties of high-quality generation and fast inference speed. Central to our methodology is dual-path injection mecha- nism, meticulously designed to integrate both the semantic attributes and the precise textural details of reference images. Comprehensive evaluations corroborate the superiority of our design, revealing that EchoGen achieves generative performance on par with leading diffusion models while exhibit- ing substantially lower sampling latency. By pioneering forw-fo, autoregressive solution for subject-driven synthesis, this research charts a new trajectory for the future development and application of visual autoregressive generative models.
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7 APPENDIX
7.1 DATASET
We train EchoGen on the filtered combination of the Subjects200K and UNO-IM synthetic datasets. The Subjects200K datase introduced by (Tan et al., (025), contains approximately 256,000 triplets, each comprising a reference image, text prompt, and a corresponding generated target image. It is specifically established for subject-driven generation. The dataset is constructed by first using GPT-40 to produce over 30,000 diverse subject descriptions that each description represents the same subject across multiple scenes. These descriptions are then reformulated into structured prompts, each specifying a single subject two different scenes, which are fed into FLUX.1 I -ddv text-to-imase model to synthesize paired images. Finally, GPT-4o filters the generated pairs to sure subject consistency and overall image quality. All images have the resolution above 500 pixels, providing sufficient detail for training.
The UNO-1M datase Wu et al., 2025) comprises approximately 1M data triplets and is built in similar manner. It leverages the LLM to generate diverse subject instances and scenes guided by a taxonomy tree derived from the 365 general classes of Object-365, and then employs FLUX.1 model to synthesize image pairs. After that, DINO-y2 and Vision-Language Models (VLMs) further used to score and filter the generated data. To obtain a high-quality corpus, we use their single-object subset and additionally filter it based on the VLM scores, resulting approximately 394,000 triplets.
7.2 PSEUDO-CODE OF THE ECHOGEN BLOCK
We provide a Py PyTorch-style pseudocode for our EchoGen Block in ducibility and clarity.
Algorithm Tto facilitate repro-
7.3
IMPLEMENTATION DETAILS
7.3.1 TRAINING DETAILS
Data Preprocssing. All training images undergo standardized pre-processing pipeline. Initially, images are resized so that their shorter edge matches the model's target resolution, which is 256 pixels for EchoGen-O. IB and 1024 pixels for EchoGen-2B. followed by a central crop to achieve a square aspect ratio.
To maintain data quality for high-resolution image generation within the SchoGen-2BB model, we circumvent the quality degradation induced by naive bilinear upsampling. Instead of using simple integrate super-resolution step. Specifi-
bilinear scaling on images smaller than 1024 pixels, cally, we leverage the PiSA-SR model (Sun et al. 2025) to upscale these images, method chosen to preserve ine-grained textures and prevent the introduction of common interpolation artifacts. This ensures that the model is trained exclusively on high-quality and high-resolution exemplars.
Training parea-parameters. We follow the Infinity-2B standard training recipe (Han al. 2025 and the detailed hyperparameter configurations used to train our EchoGen are provided in Table g To mitigate error accumulation, mentioned in Infinity, employ bitwise self-correction by ran- domly flipping bits in the input sequence with a probability of 0.3. To improve robustness against variations in instruction length, prompts are randomly truncated to single sentence with a probabil- ity of 0.5 during training. The EchoGen models are trained using 32 H20 GPUs, requiring 2 weeks for the longest schedule (training our EchoGen-22 model for 20 epochs).
7.3.2 EVALUATION DETAILS
For our quantitative evaluation, utilize the DreamBench dataset (Ruiz et al. 2023 The dataset comprises 30 distinct subjects, categorized into 9 animate pets (cats and dogs) and 21 diverse inani- mate objects (e.g., toys, sunglasses, backpacks). Each subject is associated with 25 textual prompts specifically designed to test the model's abilities recontextulizzttio, property modification, and
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class BchoGenBlock (nn (on.
def iiiinit__ (dim, mask):
Multi-modal attention QKV jecttrs for the image boken sequence self.qkv _m nn m..ieeer (cim, 3+dim)
# Multi attention QKV pro jectors for the detail led led content feature self.qkv. mm in.eine (dim, 3adim)
mask
Cross attention query projecters for the image token sequence self. cea nn.Linear (dim, dim)
# Cross actention KV projectors for the semantic feature self.ky_ca__ nb. Linear (din, 2+dim) Cross attention
projectors for the text embedding Linear (din, 2vdin)
selffky_ca_t
# FFN for the image token sequence self.ffn MLP (dim)
# FPN for the detailed contert feature self.ffn. = MLP (dim)
def fsrward X, ce, cs, ct):
# Multi-modal attention
g, k, v self.qkv mm (x) qo, ke, vc
se f. qkv_ (cc)
g torch, concat (q, qe))
& torch.concat. (k, kc))
v torch. (v, vc))
X, cc attention (q, k, V, self.mask)
# Cross attention
q self.q_caíx)
self self.kv_ca_stes) kt, vt self.kv_ca_ttee
k torch, concat (ks, kt))
v torch, concat (vs, vt))
x attention (g, k, mask=None)
Feed-forward network self self.ffe (x)
cc - felc.ffn_c(cc)
return X, cc, cs,
Pseudo-code illustrating the EchoGen Block. Here, denotes the image token sequence, and the generation process is conditioned the semantic feature and detailed content feature cs extracted from the reference image, along with the text embedding c..
accessorization. Our data preparation protocol is adapted from (Pan al. 2024). which involves selecting single reference image per subject and augmenting its subject identity phrase with de- scriptive keywords. The correspondence between the DreamBench dataset directory and the augmented subject description is summarized as follows:
backpack, backpack
backpack dog, dog shaped backpack
bear-plushie, bear plushie
can, 'Transatlantic IPA' can
candle, jar candle cat, tabby cat
cat2, grey cat
clock, number '3' clock
colorful colorful sneaker
dogl, fluffy dog
dog2. fluffy dog
dog3, curly-haired dog
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Config
value
Bitwise Self-correction Flip Ratio
0.3
Bitwise Self-correction Apply Layers
13
Dynamic Truncate Prompt Ratio
0.5
Infinity Image Encoder Channel
16(0.1B) /2((B)
Text Encoder
Flan-(5-xl
Text Embedding Channels
2048
Maximum Text Tokens Length
512
Semantic Image Encoder
DINO-v2-Base
Semantic Feature Channels
768
Semantic Downsample ratio
14
Content Image Encoder
FLUX, VAE
Content Feature Channels
16
Content Downsample ratio
Reweight Loss by Scale
True
Gradient clipping by norm
5.0
Optimizer
Adamw
Betal
0.9
Beta2
0.97
Decay
0
Base Learning rate
3e-5
Multi-Modal Modules Learning rate
3e-6
Learning rate warmup iterations
Training epochs
20
Total Batchsize
128
GPU
H20
Table 8: Detailed hyper-parameters for training our EchoGen.
dog5, long-haired dog
dog6, puppy
dog7, dog
dog8, dog
duck toy, duck toy
fancy_ fringed cream boot
grey_sloth www.sloot.ppuuiic.. grey sloth plushie
monster_toy, monster toy
pink_sunglassee sunglasses
poop_emoji, poop-emoji shaped toy
rc_car, car toy
red_carte caarton,, cartoon character
robot_toy, robot toy
shiny -neaker, sneaker
teapot, clay teapot
vase, tall vase
plussp, wolf plushic
We compute DINO and CLIP-I scores by comparing each generated image with its corresponding
single reference image in main experiments and ablation studies. Note that some methods (Li
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et al. 2023) especially test-time fine-tuning methods (Ruiz al. 2023 instead compute DINO and CLIP-I by comparing a generated image against all images the same entity in DreamBench. To enable more fair comparison under both protocols, evaluate EchoGen and several leading baselines using unified implementation and report all results in Section 7.4
To augment the diversity and rigor of our human evaluation, we incorporate curated set of instances from the DreamBench++ benchmark. DreamBench++ includes 150 subjects, each paired with nine prompts.
7.4 MORE ABLATION STUDIES
In this section, we present additional ablation studies to analyze the individual components of EchoGen. These ablation studies also conducted based EchoGen-O. I. model with fair train- ing settings.
Importance of injecting global semantic in- formation. Injecting global semantic informa- tion serves prepended condition, en- sure global structural coherence during gener- ation. Our ablation study Table [ confirms the significant benefits of incorporating global semantic features. Moreover, we conduct tar- geted experiment comparing the injection of global semantic versus content features to the Image Token. As shown in Table | the results clearly indicate that prepending semantic fea- tures rather than content information into the image token significantly enhances the subject fidelity. This confirms that our choice to inject global semantic guidance into image tokens is both effective and well-justified.
Exp.
DINO
CLIP-I
CLIP-T
Content
0.663
0.795
0.322
Semantic
0.672
0.806
0.321
Table Analysis of different information types prepended to the Image Token. Content repre- sents prepending the global content feature, while Semantic denotes prepending the global semantic feature.
Subject Segmentation. To mitigate the influence of irrelevant background noise and fo- on the primary subject, we leverage the Qwen2.5-VL vision language model (Bai al. 2025) and the GroundingDINO segmentation model (Liu et al. 2024) to segment the subject from the reference image. We conducted an ab- lation study, detailed in Table [00] to validate the efficacy of the echo segmentation proto- col. The results confirm that the introduction of subject segmentation significantly enhances the generation performance, which is observed in the preservation of subject features, and demon- strate that isolating the main subject is critical to producing more accurate and faithful out- puts.
Exp.
DINO
CLIP-I1
CLIP-T
w/o SS
0.663
0.796
0.321
w/ SS
0.672
0.806
0.321
Table 10: Enhancement by subject segmenta- tion (denoted by SS) to mitigate background noise.
Exp.
DINO
CLIP-I+
CLIP-T
To further analyze the sensitivity of our method conduct
to the quality of segmentation, an ablation study on about EchoGen's robust- ness segmentation quality during inference. Specifically, to simulate segmentation imper- fections, we design three variants to simulate disturbances and compare with employing sub- ject segmentation without imperfection during inference: 1. Enlarging Bounding Box: en- larging the subject's 's bounding box by 10%; 2.
w/o SS
0.737
0.829
0.324
Shift
0.739
0.833
0.321
Enlarge 0.735
0.831
0.321
w/ SS 0.755
0.835
0.325
Table 11: Analysis of the sensitivity of EchoGen to the segmentation quality. SS denotes the sub- ject segmentation; Enlarge and Shift denote en- larging and shifting bounding box, respectively.
Shifting Bounding Box: shifting the bounding box by 10%; 3. No Segmentation: completely moving the subject segmentation step. As shown in Table our model exhibits remarkable robust- ness its performance degrades only slightly under these disturbances. Moreover, model still produces strong results even without any segmentation. demonstrating its powerful generalization capability. In summary, EchoGen is highly robust imperfect segmentation.
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32.75
75
12.30
63
90.50
66.5
80
12/01
31.75
2.5
Y:
11-
4= 3.5
Y: 3.5
71- 3.5
4,3
0450
Y- 4.0
Y1 4.0
1100
Figure 7: Visualization of the effect of classifier-free guidance scale coefficient.
Method
Base Model
DINOT
CLIP-If CLIP-Tf
Latency
IP-Adapter Ye et al. 2023)
SDXL
0.713
0.851
0.293
16.9s
OminiControl Tan et al. (025)
FLUX.I-des
0.644
0.800
0.323
27.5s
EasyControl Zhhng el al. 2025
FLUX. 1-dev
0.666
0.804
0.330
25.4s
EchoGen-22B
Infinity-2B
0.755
0.835
0.325
5.2s
(a) Multi-reference scoring (compare against all references of the same entity).
Method
Base Model
DINOT
CLIP-If CLIP-TT
Latency
IP-Adapter Ye et al. 2023)
SDXL
0.626
0.807
0.295
16.9s
OminiControl Tan al., (0255)
FLUXX. 1-dev
0.575
0.771
0.323
27.5s
EasyControl /Zhng et al. 2025
FLUX. 1-dev
0.600
0.773
0.330
47.6s
EchoGen-2B
Infinity-2B 0.663
0.802
0.325
5.2s
(b) Single-referenc scoring (compare against the corresponding reference image).
Table 12: Quantitative comparisons on DreamBench (Ruiz et al., 2023) under two evaluation
protocols. Baselines are reproduced from official repositories and evaluated with the same code. EchoGen achieves strong performance with lower latency.
Subject-text classifier-free guidance. As detailed in Figure 7 our experiments reveal clear trade- off governed by the CFG hyperparameters within a proper scope. As the subject guidance weight Y increases, subject fidelity improves, indicated by higher CLIP-I and DINO scores. Conversely, this gain is accompanied by reduced text alignment, reflected in lower CLIP-T. The inverse rela- tionship is observed when increasing the text condition scaling coefficient "/- This empirical result demonstrates the efficacy and flexibility of our CFG design, enabling users to dynamically adjust the balance between preserving reference image features and adhering to the text prompt.
Different evaluation protocols. We compare our model with several leading baselines under two different evaluation protocols: (i) each generated image is compared against the single correspond- ing reference image, and (ii) each generated image is compared against all reference images of the same entity in DreamBench. We reproduce the baselines using their official repositories and evaluate all methods with the same evaluation code. As shown in Table 12, our method achieves compara- ble superior subject preservation, as measured by DINO and CLIP-I, well comparable text alignment (CLIP-T), while maintaining faster sampling speed.
7.5 MORE VISUALIZATION RESULTS
We further showcase additional qualitative re- sults DreamBench in Figure 8 Moreover, we provide additional visual results from the EchoGen-2B model on real-world subject per- sonalization in Figure 9 These results demon- strate that our model, trained exclusively on filtered combination of the large-scale synthetic
Reference Image
A noccoon is stondling by stream
raccoon is perched free branch
Subjects200K Tan et al. 2025) and UNO- IM (Wu et al.) 2025) datasets, our model ex- hibits strong generalization to real-world see- marios, including the generation of live animals and diverse objects under complex conditions. EchoGen-2BB consistently maintains high sub-
penguin plushie on white Figure 9; More visualization of EchoGen-2B on real-world subject personalization.
Reference Image
penguin plushie on the beach
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Reference
IP- IP-Adapter
OmniGen
OminiControl
EasyControl
EchoGen-28
jar candle with a mountain in the background
robot toy with city in the background
robin perched on a frost-covered branch wintry landscape
A fluffy dog in a firefighter outfit
A purple bear plushie
Figure 8: Additional qualitative comparisons between our EchoGen model and competing methods.
ject fidelity and strong text alignment during these real-world personalization tasks, demonstrating effectiveness of our training strategy and the proposed dual-path semantic-content injection de- sign.
7.6 LIMITATION & FAILURE CASE ANALYSIS
Our EchoGen takes a new step toward VAR-based feed-forward subject-driven generation to inherit the strong capability of next-scale prediction and bidirectional modeling within scales. However, we know that the feed-forward subject-driven image generation is highly dependent on the capability of base models. The performance of our EchoGen models is fundamentally dependent upon the ca- pability of the base models Infinity-O.I. -..1 and Infinity-2BB The Infinity-2B architecture still exhibits performance gap compared to state-of- of-tee-f-thearr art generation models such as Stable-Diffusion 3 and FLUX, particularly in generating high-fidelity details. This inherited constraint limits FchoGen's 's efficacy resolving fine-grained features, such as the faithful rendering of facial characteristies, the synthesis of coherent text, and the reproduction of intricate material textures. Due to significant GPU computational and temporal constraints, our experiments are confined to these specific backbones, precluding empirical investigation of larger models such as Infinity-8B. We hypothesize that mi- grating the EchoGen architecture to a more potent VAR foundation model would unlock substantial performance gains.
Additionally, the DINOY2 vision encoder operates on relatively low-resolution inputs (e.g., 224x224), which limits its ability capture fine-grained appearance cues and tiny textual elements.
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(a) Synthesize complex details
(b) Render tiny text
A motorcycle is parked beside bustling city street at night, illuminated by street lights
A steam locomotive is chugging through a dense forest
Reference Image
Reference Image
Figure 10: Failure cases generated by EchoGen.
We believe seeking an effective high-resolution semantic encoder presents a promising avenue for further improvement in complex applications.
Due to the aforementioned limitations, illustrated in Figure [0] the model exhibits reduced relia- bility subjects that have highly intricate structures or in scenarios requiring precise text rendering. We will continue investigate and address these challenges in future work.
7.7 THE USAGE OF LARGE LANGUAGE MODEL
We utilized the large language models Qwen-3 and GPT-5 to improve the clarity, grammar, and formal tone of the writing in the method and experiment sections. Nevertheless, all technical content, such conceptual formulas and experiments remain own; the large language models used solely as tools for linguistic enhancement and stylistic polishing.
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Reviews:
[{"review_id": "xnOZ24YbQx", "reviewer": "Reviewer_YBoY", "summary": "This paper introduces EchoGen, a novel feed-forward framework for subject-driven image generation built upon a Visual Autoregressive (VAR) model. Motivated by the significant latency of diffusion-based approaches and the high computational cost of per-subject fine-tuning , EchoGen leverages the inherent fast sampling of VAR models to address this efficiency gap. The core technical contribution is a dual-path injection strategy that disentangles a subject's high-level semantic identity from its fine-grained textural details, enabling high-fidelity preservation and control. Experimental results validate that EchoGen achieves subject fidelity and text alignment competitive with state-of-the-art diffusion-based methods, while being significantly faster at inference.", "questions": "See the weakness section above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper’s primary strength lies in its novel and successful exploration of an autoregressive architecture for subject-driven generation. This approach achieves performance that is highly competitive with state-of-the-art diffusion-based methods. Its most notable advantage is that it delivers strong results while drastically reducing inference latency, offering a significant improvement in efficiency and practicality compared to the slow, iterative sampling required by diffusion models. Moreover, the empirical evaluation is thorough and rigorous, supporting the validity of the proposed approach.", "weaknesses": "1. The model's generative quality is fundamentally limited by the capabilities of its Infinity-2B backbone. This backbone restricts its ability to render fine-grained details like coherent text or complex textures, meaning its peak quality is constrained even though its efficiency is high.\n2. The method relies on a complex pre-processing pipeline using Qwen2.5-VL and GroundingDINO, which adds computational overhead. It is unclear if the reported inference latencies (5.2s for EchoGen-2B ) include this segmentation step. If not, the true \"wall-clock\" efficiency comparison is ambiguous.\n3. The paper lacks an analysis of the model's sensitivity to the quality of the segmentation. While an ablation confirms segmentation is beneficial, it is unknown how performance would be impacted by a less accurate (or more accurate) segmentation model or by common bounding box errors. The system's robustness to imperfect pre-processing remains untested.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "DMpw190dk2", "reviewer": "Reviewer_aSDF", "summary": "This paper introduces EchoGen, the first subject-driven image generation framework built on a VAR model rather than diffusion models. The method enables fast, feed-forward subject-driven generation by injecting both high-level semantic identity and low-level visual details through a dual-path design. EchoGen leverages a semantic encoder and a content encoder to disentangle identity and appearance, achieving controllable and high-fidelity results. Experiments show that EchoGen reaches state-of-the-art subject fidelity and image quality while significantly reducing sampling latency compared to diffusion-based approaches.", "questions": "1. The pipeline relies on several large pretrained components (Qwen2.5-VL, DINOv2, FLUX.1-dev VAE, and the base VAR model). Do the latency numbers reported in the paper reflect the full end-to-end runtime, including all feature extraction modules, or only the VAR sampling stage?\n2. Is there a comparison of computational resource usage (e.g., GPU memory, inference throughput) against diffusion-based subject-driven methods? Such information would clarify the practical efficiency advantages claimed in the paper.\n3. Why is the semantic feature injected into the Image Token instead of the content feature? Is there a conceptual or empirical advantage to this setting, and were alternative designs explored?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents the first subject-driven image generation framework based on a visual autoregressive model, expanding the design space beyond the diffusion-based paradigm.\n2. The dual-path injection strategy—separating semantic identity from fine-grained visual content—is conceptually meaningful and empirically shown to improve subject fidelity and controllability.\n3. The method achieves competitive or superior generation quality compared to diffusion-based baselines while offering substantially lower inference latency, addressing a clear practical limitation of existing approaches.", "weaknesses": "1. Although the framework claims to disentangle semantic and content features, the paper does not provide explicit mechanisms or empirical evidence demonstrating effective disentanglement between these two feature types.\n2. The method depends on multiple large pretrained models (Qwen2.5-VL, DINOv2, FLUX.1-dev VAE), which increases system complexity and may limit applicability in real-world or resource-constrained settings.\n3. The motivation for adopting VAR models feels somewhat shallow—mainly framed as “VAR is faster than diffusion”—without deeper justification for why VAR is inherently suitable for subject-driven generation beyond latency.\n4. Some ablation studies and qualitative analyses are missing, such as the effect of removing DINOv2 or FLUX.1-dev, or visual comparisons showing the contribution of semantic vs. content features.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "YKvEqta6Ff", "reviewer": "Reviewer_6ZGd", "summary": "EchoGen is proposed as the first feed-forward, subject-driven image generation framework based on Visual Auto-Regressive (VAR) models, overcoming the limitations of existing methods that trade off controllability for speed. By employing a dual-path injection mechanism—decoupling high-level semantics (via DINOv2) and low-level details (via FLUX.1-dev VAE) through specialized attention modules—EchoGen enables fast, non-iterative sampling. Efficiently trained on Subjects-200k and evaluated on DreamBench, EchoGen achieves subject fidelity and text alignment on par with or better than leading diffusion models, while reducing inference time for 1024×1024 images to under 6 seconds.", "questions": "Please refer to the weaknesses. If the issues I raised are addressed, I will increase my rating.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The separation of semantic identity and content details is technically sound. Leveraging DINOv2 for semantic abstraction and FLUX VAE for detail encoding, complemented by distinct attention mechanisms, demonstrates appropriate feature representation in subject-driven image generation.\n2. EchoGen demonstrates comparable or improved results relative to strong diffusion-based feed-forward methods (e.g., IP-Adapter, OminiControl) on DreamBench benchmarks (CLIP-I, DINO, CLIP-T). Its superiority in subject fidelity and photorealism is further corroborated by human evaluation.\n3. A latency of under 6 seconds for high-resolution image generation represents a significant practical advantage, making EchoGen particularly well-suited for real-time and user-facing applications where responsiveness is critical.", "weaknesses": "1. The authors mention related work [1] on fine-tuning VAR for subject-driven generation, but this is not included in experiments. A comparison would help clarify whether feed-forward is truly superior to lightweight fine-tuning in the VAR regime.\n2. Although Subjects-200k (~256k triplets) is substantial in scale, it is synthetically generated using FLUX.1-dev and GPT-4o, which may introduce distributional biases and limit real-world subject diversity. Furthermore, the paper does not provide a detailed analysis of failure cases involving challenging real-world subjects, such as humans in complex poses or transparent objects.\n3. While Table 2 reports preference ratios, important methodological details are missing, such as the evaluation format (single-choice vs. multi-select), the number of images per trial, and participant expertise. Including this information would strengthen the reliability of the human study findings.\n \n[1] Fine-tuning visual autoregressive models for subject-driven generation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "1smvGfESYt", "reviewer": "Reviewer_uSYJ", "summary": "The paper introduces EchoGen, a feed-forward subject-driven image generation framework that replaces the diffusion backbone with a visual autoregressive (VAR) architecture. The model integrates a dual-path feature injection mechanism: (1) a semantic path using DINOv2 features to capture high-level identity and structure, and (2) a content path using FLUX.1-dev VAE features to preserve low-level details. This design enables EchoGen to synthesize novel scenes containing a given subject without per-subject fine-tuning. On benchmarks such as DreamBench, EchoGen is competitive with diffusion-based approaches in performance, while offering faster inference.", "questions": "1. The Subjects-200k dataset appears largely synthetic (generated with GPT-4o and FLUX.1-dev). How well does EchoGen generalize to real-world subject personalization?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. This paper presents the first subject-driven feed-forward framework on a VAR backbone with a well-motivated dual-path injection design to capture both the high-level and low-level information from subject images.\n2. EchoGen achieves competitive subject-driven image generation performance to state-of-the-art diffusion methods while cutting sampling latency to 5.2 seconds (vs. 10-50 seconds for feed-forward diffusion models).", "weaknesses": "1. The efficiency comparison in Table 1 may not be fully fair. The paper reports using default sampling configurations for all diffusion baselines, yet sampling time in diffusion models is highly dependent on sampling parameters such as the number of denoising steps. This can potentially overstate EchoGen’s relative speed advantage. A more rigorous analysis showing performance-compute tradeoffs across multiple sampling settings of the baselines would make the efficiency claims of EchoGen more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)