One-Step Diffusion-Based Image Compression with Semantic Distillation
Authors: Naifu Xue
Zhaoyang Jia
Jiahao Li
Bin Li
Yuan Zhang
Yan Lu
Venue: NeurIPS 2025 poster ()
Keywords: image compression
generative compression
diffusion models
one-step diffusion
extremely low bitrate
Primary area: applications

Abstract: While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasant latency. In this work, we revisit the design of a diffusion-based codec and argue that multi-step sampling is not necessary for generative compression. Based on this insight, we propose OneDC, a One-step Diffusion-based generative image Codec—that integrates a latent compression module with a one-step diffusion generator. Recognizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior as a semantic signal, overcoming the limitations of text prompts in representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowledge from a pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt a hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offering over 39% bitrate reduction and 20× faster decoding compared to prior multi-step diffusion-based codecs. Project: https://onedc-codec.github.io/

One-Step Diffusion-Based Image Compression with Semantic Distillation
Naifu Xue' Zhaoyang Jia Jiahao Li Bin Li Yuan Zhang', Yan Lu
Communication University of China 2 University of Science and Technology of China
3 Microsoft Research Asia faaronxuenf, yzhang)@euc... edu. cn, (jzy_ustchmmail. ustc.edu. {li.jiahas libin, yanlu)(microsoof
Abstract
While recent diffusion-basee generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasant latency. In this work, we revisit the design diffusion- codec and argue that multi- step sampling is not necessary for generative compression. Based this insight, we propose OneDC, a One-step Diffusion-basee generative image Codec-ther that integrates latent compression module with one-step diffusion generator. Recog- nizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior semantic signal, overcoming the limitations of text prompts representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowl- edge from pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offer- ing over 39% % bitrate reduction and 20x faster decoding compared to prior multi- step diffusion- codecs. Project: hattps://onede: conneddcodec...... git.gg.......... io/
Introduction
The rapid growth in image data has led to increased storage and transmission costs, heightening the
need for efficient, high-quality image compression techniques. While VAE-based learned image
compression (LIC) [36] [1 has surpassed traditional codecs (e.g., VVC (8) in rate-distortion (RD)
performance, it often produces blurry details at low bitrates [41] since they are typically optimized for objective distortion rather than human perception 77 To mitigate this, some approaches [4] 44
focus on optimizing visual quality by incorporating perceptual metrics and adversarial losses to enable generative image compression. Despite these advances, such generative models tend to introduce artifacts under extreme compression, degrading the realism of reconstructed images.
Recently, diffusion brafusiion-based generative codecs [9 33 have been introduced enhance reconstruction quality by leveraging the powerful content synthesis capabilities of pretrained models. While these methods significantly improve perceptual realism, they may occasionally generate content that deviates from the original input, thereby compromising reconstruction fidelity. addition, their inherently iterative sampling process leads substantial computational overhead, making them notably slower than conventional VAE-based codecs.
In standard diffusion-based image generation, the model progressively refines noisy signal through iterative denoising, beginning with coarse structures and gradually synthesizing high-froe figh-frequency details (65). However, when applied to image compression, the task changes: at low bitrates, codecs typically
Naifu Xue and Zhaoyang Jia are visiting students at Microsoft Research Asia.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Original
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Mutti-Step ditfusion codec
Ours: One-stop
961
D
Faster better!
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Original
PerCo (SD)
DIMEIC
DIfCC
OneDC (Ours)
(1449))
LPIS
0.271 (17 XX
0.236 (.256 x]
(.255
0.156 (1.0())00)
OneDC
0.029 x20
0014(1000
0.014[1.0
Figure 1: Top: multi-step sampling is not essential for image compression; intermediate results are
from DIfEII 33
Bottom: Visual comparisons including existing open-sourced multi-step diffusion
codecs 9 33 57 and our proposed one-step codec. Our method achieves the highest visual quality at the lowest bitrate while offering significantly faster decoding.
retain coarse structures in the compressed latents. As a result, the decoder is primarily responsible for plausibly reconstructing high- high-frequency content from the preserved low- cow-frequency information. This observation motivates our central hypothesis: given the compressed latent, multi-step sampling is not necessary for decoding, and carefully designed one-step alternative could suffice. Although recent advances in one-step diffusion [53 66 25 offer promising tools for accelerating inference, their potential remains underexplored in the context of image compression.
In this paper, we introduce OneDC (One-step Diffusion-based generative Codec), novel framework for ultra-low bitrate image coding. OneDC integrates a latent compression module with one-step diffusion generator: the former encodes the image into compact latents, while the latter synthesizes details conditioned on the latent. Since the pixel-domain training insufficient for guarantecing perceptual quality at low bitrates (22), we adopt hybrid-domain training strategy. Specifically, combine a pixel-domain perceptual loss to promote fidelity with latent-domain diffusion distillation objective [66] to enhance realism. This design allows OneDC to effectively leverage the pretrained diffusion model while achieving balanced trade-off among compression ratio, perceptual quality, and decoding efficiency, as illustrated in [
Moreover, we further explore the role of semantic guidance (i.e., the input of the cross-attention layers (50) within our framework. one-step diffusion, such guidance essential to compensate for the absence of multi-step refinement (see ablation in Section 4.3). While existing diffusion models have utilized text prompts for conditioning, we argue that text is suboptimal for image compression due to two limitations: (1) natural language struggles to capture fine-grained localized visual semantics, and (2) generating high-quality captions typically requires large-scale vision-langual models (e.g., the large BLIP2 [32) used in [9), introducing substantial computational overhead. This raises a central question: Can we design more effective semantic representations than text for guiding one-step diffusion- based codecs-without incurring excessive computational cost?
Recent studies have shown that the hyperprior VAE-based codees can capture high-level se- Compared to textual prompts. hyperpriors provide more precise and spatially aligned semantic cues, making them well-suited for representing localized semanties in high-resolution images (see Fig This insight motivates us to further enhance the representational
mantic information 22 48

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Original Imagle
Textual
Reconstruction
Hyperprior Feelurres
Reconstruction from Hyperprior
Semantic Distillation
Prompts
Text Only
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LPIPS: 6,2838
Figure 2: Reconstructions from different semantic guidance. (a) Text prompts (from GPT-4o struggle to capture complex visual semantics, resulting in severe distortions when using a pretrained texi-to-image one-step diffusion model [66]. (b) We finetune the model [66] for pyperpriorttoimmaee generation. Hyperprior guidance yields more faithful reconstructions. (c) Our proposed semantic
distillation further improves object-level accuracy, particularly in the highlighted regions.
capability of the hyperprior. Inspired by generative tokenizers 13] where codebooks encode rich semantic content, we propose hyperprior semantic distillation a training strategy that transfers semantic knowledge from a pretrained tokenizer to the hyperprior through code prediction module 72]. As illustrated Fig [ this approach results reconstructions that are more semantically accurate and visually coherent.
Experiments show that OneDC achieves state-of-the-arr (SOTA) performance in generative image compression, delivering over 39% bitrate reduction and 20x faster decoding compared to existing multi-step diffusion codees. Ablation studies further validate the effectiveness of our optimization strategy and the semantic distillation method for hyperprior. Our contributions are as follows:
We propose OneDC, one-step diffusion codec comprising latent compression module for compact feature encoding and one-step diffusion generator for fast, high-quality decoding. A hybrid-domain training strategy further enhances both fidelity and perceptual realism.
We identify the importance of high-level semantic guidance in one-step diffusion and highlight the limitations of text embeddings. To improve reconstruction quality, we introduce hyperprior features alternative and enhance them through semantic distillation.
Extensive experiments show that OneDC achieves SOTA compression performance while offering significantly faster decoding than existing diffusion- codecs, demonstrating the potential of one-step diffusion generative compression.
Related Work
VAE-based Learned Image Compression. Learned image compression has made rapid progress in RD performance. Ballé et al. [4] introduced an end-to-end framework with analysis/synthesii transforms and a factorized entropy model. The hyperprior model [5] enhanced entropy modeling by encoding side information (hyperprior) to predict distribution parameters, substantially improving RD performance. Subsequent work combined hyperpriors with spatial context 43] and adopts more expressive distribution models such as Gaussian mixtures [I]. Recent studies further refined transform architectures for stronger nonlinear representation (36 and developed advanced entropy models for more compact symbol coding [23] together surpassing the VVC-Intta baseline [8]. Nevertheless, these models are typically optimized with distortion-oriemter objectives such as MSE or MS-SSIM (60), which often over-smooth textures and degrade perceptual quality at low bitrates 4
even with SOTA transforms and entropy models [36] (1] 58).
VAE-based Generative Image Compression. To improve perceptual quality at low bitrates, GAN- based approaches such as Agustsson al. (2 and HiFiC 41 train VAE codecs to synthesize high-frequency details using perceptual losses 24 and adversarial discriminators. MS-ILLM 44

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Latent Compression Module
Decodar side
Encader side
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Figure 3: Overview of the OneDC framework. Q denotes scalar quantization, and FSQ stands for finite scalar quantization. AE and AD refer to the arithmetic encoder and decoder, respectively. hatz and haem represent the context and semantic decoders used in the hyperprior branch.
improves realism through a local likelihood model, while EGIC [7 introduces semantic- aware feedback via
discriminators. Other methods enhance compression by integrating generative tokenizers: Mao et al. 440 utilize VQGAN [1]] for compression, Xue et al. 63 unify token generation and entropy coding, and Jia et al. [22 propose GLCC that improves token compression via transform coding. More recently, to support more compact and flexible latent representations. While these methods highlight the strong feature learning capacity of VAE-based codecs, their reconstruction quality at extremely low bitrates remains limited by model capacity and training scale.
codecs [39 64 have been introduced
Diffusion-based Generative Image Compression. Diffusion models have rapidly evolved in recent years, progressing from theoretical foundations [51] 20 to practical high-quality inplementations [50].
have explored the use of generative priors from large-scale pretrained diffusion models in image compression. demonstrating improved perceptual realism compared to conventional VAE-based approaches. Eric et al. (2 combine pretrained diffusion with ControlNet [69 to reconstruct images from captions and compressed sketches. PerCo p fine-tunes diffusion models using captions and quantized features, with captions generated by large BLIP2 model
Building on these advances, recent studies [29 9
33
57 62
[32]. DiffEIC [33] conditions diffusion models on VAE latents, showing strong performance even without text, and their subsequent work (34) improves encoder and introduces relay fine-tuning. DifIC [57] demonstrates zero-shot compression with diffusion models using reverse-channel coding. DDCM [46] replaces the continuous Gaussian noise in sampling with discrete codebook selections for compression task. While these approaches improve realism at ultra- low bitrates, they often struggle to maintain high fidelity. Moreover, multi-step sampling in DDIM introduces substantial decoding overhead, limiting their practicality. By contrast, OneDC achieves high fidelity and realism with significantly faster decoding via one-step sampling. While prior work 33 suggests high-level semanties (e.g., text) may be optional, we find such guidance remains crucial in our one-step diffusion for image decoding task, as detailed in Section
[2
One-step Diffusion Models. To reduce the computational cost, recent efforts have focused on distilling multi-step models into efficient one-step generators. Song et al. (53) introduce Consistency Models, which achieve few-step generation by enforcing self-consistency along diffusion trajectories. Yin et al. [67] propose Distribution Matching Distillation (DMD), which minimizes an approxi- mate KL divergence between real and generated image distributions modeled by multi-step teacher networks. Their subsequent work, DMD2 [66]] further improves generation quality by introducing diffusion-GANN framework and enhanced training strategies. Based on DMD, Song et al. [54] introduce class-specific student models to improve performance across diverse categories. SwiftBrush series 45 12 adopt LoRA adaptation [2 for variational score distillation and further optimize diversity by integrating a clamped CLIP loss. Independently, Kang et al. (25] employ perceptual loss supervision and multi-scale U-Net discriminator for one-step distillation. These models have also been extended to low-level vision tasks such as super-resolution [14] 61 (59 26) and image restoration (1], highlighting the potential of one-step diffusion beyond generation. Inspired by these advances, we explore its application in generative image compression, a setting that remains underexplored.
3 Methodology
We propose OneDC, one-step diffusion-based generative image compression framework that achieves high-quality reconstruetion with low- low-latency decoding under ultra-low bitrate constraints. Section 3.I introduces the coding pipeline of our method, consisting of latent compression module

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Figure 4: Two stage training pipeline of OneDC. The codebook in semantic distillation is initialized from the pretrained tokenizer, and the discriminator in diffusion distillation is abbreviated as Disc.
that encodes the input image into compact latent representation and a one-step diffusion generator that synthesizes reconstructions with realistic details. Given the critical role of semantic guidance in our one-step diffusion and the limitations of textual prompts in compression scenarios, Section 3.2 introduces the use of the hyperprior as an alternative conditioning signal. To further tap the potential of the hyperprior, we propose a semantic distillation strategy that transfers knowledge from pretrained generative tokenizer the hyperprior codee, thereby improving semantic accuracy. Finally, Section 3.3 presents our training scheme, which combines pixel-domain and latent-domain objectives to jointly optimize reconstruction fidelity and perceptual realism.
3.1 Framework Overview
Fig. shows the overview of our OneDC framework. On the encoder side, an analysis transform encodes the input into a compact latent representation y = (a(x), which is then quantized ÿ. A hyper encoder further processes the latent into a hyperprior 2 = (yno quantized as å. Then, the entropy model takes decoded hyperprior context с₁ heee (2) and spatial context c, predict the distribution of y, enabling bitrate estimation during training and entropy coding during inference.
On the decoder side. the received is passed through synthesis transform to produce an initial latent Vin = (r) for generation. Given the distinct roles of entropy modeling and semantic processing, where the former estimates low-dimensional distribution parameters and the latter represents high- dimensional visual contents, we introduce an additional semantic decoder for the hyperprior to better address their different requirements. It extracts semantic guidance hyperprior, which is injected into the cross-attention layers of the one-step diffusion generator. The diffusion generator refines the initial latent in only one step: Yout e (Win, c). which is then decoded by pretrained VAE decoder to produce the final reconstruction f = D(oout).
heem (2) from the quantized
3.2 Semantic Guidance with Hyperprior
3.2.1 From Text to Hyperprior
In the one-step diffusion setting, semantic guidance (the input of the cross-attention layers (50) plays an even more critical role than in conventional multi-step diffusion. Unlike multi-step methods, which iteratively refine the output and can gradually correct semantic inconsistencies, one-step diffusion models rely entirely on single forward pass, making accurate semantic conditioning essential. While existing diffusion models typically rely on textual prompts to provide high-level semantic guidance, such prompts suboptimal in the context of natural image compression. Text struggles to capture localized semantics and often fails to describe fine-grained visual attributes- as object boundaries and textures-parte parture--patiiclarrly in high-resolution imagery. In addition, generating text descriptions typically requires large models 32.. introducing significant computational overhead.
Recent work [22 suggests that the categorical distributed hyperprior with vector quantization (VQ) can capture semantic information. Building on this insight, OneDC replaces textual prompts with categorical hyperprior é as semantic guidance for the diffusion model. To compress the hyperprior, we adopt finite scalar quantization (FSQ) 42), simpler yet more effective alternative to VQ (15). to learn categorical & with semantic information. The hyperprior is compressed into 7 channels
5

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Original
MS-ILLM
PerCo (SD)
DiffEIC
DiffC
OneDC (Ours)
Bop /
0.013/0.11 175
0.032/ 0.337
0,014/ 0.254
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/
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Figure 5: Visual examples on the CLIC2020 test set. Zoom in for better view.
with 4 quantization levels each, yielding equivalent codebook size of 16,384. At 64 spatial downsampling, this design results in a bitrate of just 0.0034 hpp. incurring very low transmission overhead. To substitute for text embeddings, the quantized hyperprior 2 into a semantic context E Bx where N H'' x W' is the flattened spatial shape and D is the embedding dimension. This context is injected into the cross- attention layers of the one-step diffusion U-Net, replacing the textual embeddings used in the original design. In each cross- atssentttntionn layer, the input latent feature fin serves as the query. while the semantic context provides both keys and values:
introduce semantic decoder h.em that transforms
QKT VTT
fout = Softmax
where Q = WWWfn K WAC, V W.C
where d denotes the dimensionality of the key vectors and fout is the output feature of the cross- attention layer. This design enables every spatial location in the latent attend adaptively to semantically relevant tokens, strengthening content alignment. Compared with text conditioning, hyperprior-baat guidance offers three advantages: (1) shown in Fig. conditioning the one-step diffusion model on categorical hyperprior tokens yields more faithful reconstructions than using text prompts, validating their effectiveness semantic guidance. (2) The 64x downsampled hyperprior provides large receptive field while retaining spatial locality. thereby offering more balanced global-local semantics compared to purely global text embeddings.
(3) Unlike separately trained text encoders [49], our approach enables end-to-end optimization of semantic guidance, allowing the hyperprior to adapt jointly with the diffusion model improved content expression.
3.2.2 Semantic Distillation for Hyperprior
While the hyperprior effectively captures semantic cues for diffusion guidance, its representation capability can further improved. particular, we propose a semantic distillation mechanism aimed at fully tapping the potential of the hyperprior branch. This distillation guides the hyperprior encoder to better capture visual content and improve the decoding quality of the semantic decoder harm: Specifically,
transfer knowledge from a pretrained high-capacity generative tokenizer [15] to the hyperprior codec via an auxiliary distillation task. This choice motivated by the structural similarity between the categorical hyperprior codec and the generative tokenizers that are known to produce semantically rich discrete representations [68]. By leveraging this compatibility, we use the tokenizer teacher to guide the hyperprior toward more effective semantic encoding.
Following GLC 22
we introduce transformer-based predictor Paw. [22,, trained to predict discrete token labels from the hyperprior-derived semantics The ground truth labels gt are obtained from pretrained tokenizer encoder Faaur Since the tokenizer is optimized for perceptual reconstruction, its objectives align well with OneDC, making an effective teacher. Moreover, the smaller information

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Original
MS-ILLM
PerCo (SD)
DIfFEIC
DiffC
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0.023/1
(1.5KI CUSS 3K0
0 IT 24)4 0.424
(1.217 0,27 (1.1IFO..40 (1.
0,23 (1OK) 0.9 (1.0))
(1-45)/
(LBSS)
0.987
Figure 6: Visual examples on the MS-COCO 30K dataset. Zoom in for better view.
bottleneck in the hyperprior branch naturally filters out redundant information, distilling the most salient semantic features from the teacher. The distillation is supervised via cross-entropy (CE) loss:
1gt (auEauu (z)), Lauz CE (c)
Both Pou.r and Eaua are used only during training, introducing no inference overhead. As shown in Fig 2 our semantic distillation enhances content fidelity, and ablation results in Section4.3 further validate its effectiveness improving final reconstruction quality.
3.3 Training Strategy
To enhance training efficiency and stability, we adopt a two-stage training strategy. inspired by prior works [41] 44 As shown in Fig. 4 stage mainly trains the compression module while stage II focuses on fine-tuning the one-step diffusion model for better reconstruction quality. In both stages, the one-step diffusion model is adapted via LoRA layers (21. allowing fast convergence for the compression task while preserving rich generative priors.
Stage I: Pixel-domain Compression Learning This stage aims to: (1) train the codec to compress images into compact latent representations with high-fidelity reconstruction, (2) embed semantic in- formation into the hyperprior via distillation, and (3) adapt the one-step diffusion model to synthesize fine-grained details on the decoder side. The total loss is defined as:
Lstager
Lirecnn + XR + O.Laan where Lyenon Li (x, .t) + Eppreeptunt (x. a)
as the perceptual loss and Ln as the pixel loss. R is the bitrate loss from the quad-tree- trsed spatial entropy model [30, 31]. and Louz is the semantic distillation loss (Eq. 22 The hyperparameter À controls the compression ratio and is the weight of semantic distillation loss.
We use LPIPS 70
Stage II: Hybrid-domain Perceptual Learning. This stage fine-tunes the one-step diffusion model to enhance perceptual realism without degrading fidelity. The latent compression module is fixed to improve training stability [1I. Prior work has shown that livel-level optimization alone insufficient for optimal perceptual quality [22]. Thus, we adopt joint training strategy in both pixel and latent domains. Specifically, we integrate
diffusion-basee distillation method from [66), which transfers generation knowledge from a pretrained multi-step diffusion teacher. The training also involves both perceptual supervision from the pixel domain and adversarial alignment the latent domain, which ensures fidelity by aligning reconstruction with the original image. The total loss is:
7

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Figure 7: cate-distortion curves under the full-resolutiinn setting. Zoom in for better view.
0.30
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Figure 8: Rate-distortion curves under the resize & center-crop Kodak 512x512 and CLIC2020 768 x 768. Zoom in for better view.
L setagelI Ldistaul + BLrecon + YLadv: Ldistill = Re,, Le fake (Tl Creat (T/s ()], Tradv = (ass:=-Disc(eee (Vr, 1), ()]
where:
(4)
(5)
Here, Great and fake denote the real and fake score networks in the teacher model, and Tú is the noised latent timestep 1. Dise refers to the discriminator. and y balance pixel and adversarial
losses. Additional implementation and loss details are provided in the supplementary material.
4 Experiment
4.1
Implementation and Evaluation Settings
Model and Training. Our generator adopts the U-Net architecture from the Stable Diffusion 1.5 (SD1.5) (50) and initialized with weights from a pretrained one-step text-to-image task [66]. In Stage I, we use the pretrained tokenizer from MaskGIT [10] for semantic distillation. In Stage II, we employ the multi-step SD1.5 pretrained model as the teacher for diffusion distillation. To improve high-resolution adaptability, randomly crop patches of size 512 1024 during training. Models are optimized using AdamW [38]. Additional settings are provided in the supplementary material.
Compared methods. We compare our OneDC with generative image codecs at tow-bitrate scenario.
The compared methods include the vanilla VAE-based generative codec MS-ILLM 44 and GLC
[2], and the recent diffusion based methods: PerCo (SD) 288. DiffEIC [3]. DiffC 57 (SD2.1-
based) and DDCM [6). For most methods, we evaluate at full-resolution, consistent with previous implementations in generative neural codecs [41] 44 22). For some diffusion-based methods like
DDCM, we additionally provide results under the resize center-crop setting, following DDCM
protocol [66] for fair comparison. Further details are provided in the supplementary material.

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Table Ablation studies with BD-Rate (%) 4.
Table 2:
Comparison coding time and BD-Rate (%) 4.
CLIC2020
Times (8)
MS-COCO 30K
Settings
Methods
DISTS FID
Enc.
Dec.
LPIPS DISTS FID
Semantic guidance
VAE-based
No guidance
44.0
45.1
MS-ILLM
0.14
0.17
138.3
253.0
478.4
Text guidance
24.2
36.3
Hyperprior guidance
20.7
24.3
Multi-step diffusion
Hyperprior + Sem. Distil. Ours
0.00
0.00
DiREIC
0.32
12.4
305.0
239.1
341.0
PerCo (SD)
0158
8.80
538.8
345.8
59.6
Loss variation
DifC
3.9.15.66
6.9--10.8
234.0
196.1
690.9
Pixel-domain only
11.4
51.8
Latent-domain only
60.7
37.1
One-step diffusion
Hybrid-domain -> Ours
0.00
0.00
OneDC + Ours
0.15
0.34
0.00
0.00
0.00
Notes: Metrics the tables denote those used for BD-Rate calculation (6; Ours is the anchor (0.00%).
Evaluation datasets and metrics. We evaluate OneDC on several datasets, including Kodak Reconstruction fidelity is evaluated using perceptual metrics LPIPS 24 and DISTS 13 along with the traditional metrics PSNR and MS- SSIM [60], while generative realism measured by the no-reference perceptual metric FID [9].
[6], CLIC2020 test set [55], and AS-COCO 30K 35
Bitrate saving is measured by BD-Rate [6] It is worth noting that FID is computed on overlapping
256 x 256 patches at full-resolution CLIC2020, following previous practice 44 222. For the MS-
COCO 30K dataset with 512 x 512 images, we evaluate FID on full-images, consistent with [9| 28 However, large patch leads to unreliable FID on the small test set (Kodak with 24 images) 99. To
address this, under the resize & center-crop setting we follow the DDCM [46] and compute FID on overlapping 64 x 64 patches for Kodak. More details are provided in the supplementary material.
4.2 Main Results
Quantitative Evaluation. Fig. 7 presents the full-resolution comparison between OneDC and existing methods across multiple distortion metrics. Across all datasets, including high-resolution CLIC2020, low-resolution Kodak and MS- MS-COCO 30K, OneDC consistently achieves the best reference- perceptual fidelity, as measured by LPIPS and DISTS. Compared with the previous SOTA, multi-step diffusion-basse DiffC, OneDC achieves $5.27% % bitrate saving on Kodak and 54.60 on CLIC2020 in terms of LPIPS, demonstrating superior compression efficiency. On traditional metrics such as PSNR, OneDC also exhibits competitive performance. For generation realism, OneDC attains lower FID than all multi-step diffusion baselines when FID is evaluated on the CLIC2020 test On the MS-COCO 30K dataset, OneDC provides a 39.55% % bitrate saving over PerCo (SD), previously the highest-realism method on this dataset. Such result further underscoring our method's effectiveness. Furthermore, Fig. 1eg.88repon reports results under the resize & center-crop setting: OneDC achieves better LPIPS and PSNR than DDCM while maintaining competitive realism, demonstrating strong fidelity- oriented perceptual compression. In conclusion, OneDC delivers faithful and perceptually compelling reconstructions across wide range of resolutions and content complexities, despite relying on a single sampling step. These evaluation results support our hypothesis that iterative sampling is unnecessary for high-quality diffusion-basee generative compression.
Qualitative Evaluation. Fig. Sa. visual comparisons on the CLIC2020 test set (full-resolution). At the lowest bitrate, OneDC produces the most natural and faithful reconstructions with sharp high- frequency details, while MS-ILLM, yields noticeably blurred textures and multi-step diffusion-baas methods (DiffEIC, PerCo, and DiffC) introduce structural distortions and inconsistent details. Similar trends are observed on the MS-COCO 30K dataset (Fig. 6). where MS-ILLM, remains blurry and multi- step diffusion methods struggle to preserve visual fidelity. These qualitative results, together with the quantitative comparisons. highlight the SOTA performance of the proposed OneDC. Additional visual examples are provided in the supplementary material.
4.3 Ablation Studies and Efficiency Evaluation
We conduct ablation studies to investigate the effectiveness of semantic guidance and hybrid-domain perceptual learning. Models are evaluated on the CLIC2020 test set with BD-Rate (6].

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Original
Pixel-dorainn Only
Hybrid-domein (Ours)
Original
Latent-domein Only Hybrid-domain (Ours)
i
Figure 9: Visual examples of the ablation study in hybrid-domain perceptual learning. No latent- domain training leads artifacts (Left), while no pixel-domain supervision leads color shift (Right).
Semantic guidance. We first validate the importance of semantic guidance and then evaluate the effectiveness of our proposed semantic distillation for the hyperprior codec. As shown in Table T removing semantic guidance ("No Guidance") significantly degrades reconstruction quality by up to 40% highlightn its critical role in our one-step diffusion model. While text-based guidance improves performance, it underperforms the hyperprior guidance high-resolution CLIC2020 images. This suggests that the hyperprior's locally captured semantics offer a more precise and spatially aligned representation of the image content. Further applying semantic distillation to the hyperprior ("Hyperprior + Sem. Distill.") leads to additional gains, indicating that transferring prior knowledge from a pretrained tokenizer enhances the semantic capabiliit of our hyperprior branch.
Hybrid-domale Perceptual Learning. We evaluate the effectiveness of hybrid pixel-latent domain training in Stage II, as summarized in Table TT (1) Using the pixel-domain loss only (No Laistar and Ladv) leads to notable drops in both FID and DISTS scores. Visual examples in Fig. Left show grid- like artifacts, reflecting significant decline in perceptual quality. This underscores the importance of diffusion distillation in enforcing distributional alignment for realistic reconstruction. (2) Using the latent- loss only (No Trecon) also results in consistent degradation across all metrics, accom- panied by visible color shifts in the reconstructions (Fig. 9 Right). This highlights the importance of pixel-level supervision in image compression, which emphasizes accurate reconstruction-uun unlike the pure image generation task that focuses solely on visual realism.
Operation Efficiency. We evaluate the coding times of different methods on 1024 x 1024 images using an A 100 GPU. These runtime results are presented alongside BD-Rate results on the MS-COCO 30K dataset to offer comprehensive view of operational efficiency. As shown in Table 2 OneDC achieves a significant decoding speed ad advantage- over 20x faster- faster-compared to multi-step diffusion- based methods, while still maintaining impressive reconstruction quality. Although OneDC is slightly slower than the pure VAE-based MS- MS-ILLM due to its larger model, this added capacity is crucial for preserving perceptual quality under extreme compression, evidenced by its superior DISTS and FID performance. Overall, these results underscore the effectiveness of OneDC in balancing fidelity, realism, and efficiency ultra-low bitrates.
over
Conclusion
We present OneDC, novel one-step diffusion-based generative image codec designed for high- quality and efficient compression under ultra-low bitrate scenarios. OneDC integrates a latent compression module for efficient coding and a one-step diffusion generator for fast reconstruction. To provide the critical semantic guidance for the one-step diffusion model and address the limitations of textual guidance, we leverage the hyperprior as substitute. Furthermore, we improve the semantic capability of the hyperprior through a distillation mechanism. To enhance training efficiency and stability, adopt two-stage strategy: Stage I focuses on compression training through pixel-domain supervision, while Stage II refines reconstruction quality via hybrid-doman perceptual learning. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality with fast decoding, highlighting the strong potential of one-step diffusion models generative image compression.
Limitation. Although OneDC offers substantial speedups over multi-step diffusion methods, its decoding speed does not meet real-time requirements. In future work, we plan to explore model distillation and architectural optimization to enhance efffiiincy.
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Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
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A Experiment
Evaluation of third-party models. We evaluate MS-ILLM using the official checkpoints and fine-tune them with the provided code to support lower bitrates. For GLC [22]. we report the results directly from its paper because the code and models are not publicly available at present. For Dif- FEIC [33] and DiffC [57], we use the official implementations and released models. Specifically, we adopt the Stable Diffusion (SD) 2.1 2.l-d variant of DifiC, which shows slightly better performance compared to the SD 1.5 version. For PerCo [9. we use publicly available reimplementation PerCo (SD) 28) as the original code has not been released. We also report the comparison with original PerCo by extract data from their paper (Fig. TO). For DDCM [46], we use the number reported in their paper. For multi-stey diffusion codecs, we follow the default sampling settings provided in their code (c.g., 50 for DifiEIC, 20 for PerCo).
Test settings and FID calculation. At the full-resolution setting, we compute FID using overlapping 256 x 256 patches for the CLIC2020 and DIV2K test sets [I. following the protocol of 41]. For the MS- AS-COCO 30K dataset with 512 x 512 images, FID is evaluated on entire images, consistent with [9 At the resize & center-crop setting, we resize the short side of each image (512 for Kodak, 768 for CLIC2020 test set) and then apply center crop. In this setting, we use 64 x 64 patches for FID calculation on Kodak and 128 x 128 patches on CLIC2020 test set, consistent with DDCM [46). is worth noting that DiffEIC also evaluates under the resize & center-crop setting on CLIC2020 test set at 768 resolution, but computes FID with overlapping 256 256 patches. For completeness, we additionally report results under DiffEIC's protocol, denoted as resize & center-crop, 256 FID.
Additional datasets and metrics. To enable comprehensive comparison, we further evaluate our method on the DIV2K test set [ under the full- resolution setting, as shown in Fig. [1 We also report results on the CLIC2020 test set at 768 resolution under DiffEIC's protocol, i.e., resize & center-crop, 256 FID, as illustrated in Fig. [2] Across datasets, metrics, and evaluation settings, the OneDC consistently achieves SOTA performance, demonstrating strong robustness and generalization. The raw data used for evaluation is available at Table.
10
We also report traditional pixel-level distortion metrics (PSNR and MS-SSIM (60) to provide
more comprehensive analysis, as shown in Fig. [3 At extremely low bitrates, optimizing for PSNR often suppresses high-frequency details, resulting in blurred reconstructions [4]]. While OneDC shows slightly lower PSNR than MS-ILLM, both perceptual metrics and qualitative examples clearly demonstrate its superior visual quality. Despite prioritizing perceptual quality, OneDC still achieves competitive PSNR compared to other diffusion- based methods. Moreover, on the MS-SSIM metric, OneDC matches MS- IILM and outperforms all other baselines. These results confirm that OneDC delivers strong pixel-level fidelity alongside high perceptual realism, highlighting the overall effectiveness of our method.
Additional visual examples. We present more qualitative comparisons across four datasets: Kodak 16). CLIC2020-bolt full resolution (Figs (7) and 768 x 768 cropped [8). and MS-COCO 30K (Fig 19 OneDC consistently outperforms prior SOTA methods, delivering superior visual quality across diverse content and resolutions, yet with the lowest bitrate cost.
Effectiveness of semantic distillation. To further evaluate the proposed semantic distillation strategy, we fine-tune the pretrained text-to-image one-step diffusion model (66), replacing its textual semantic condition with the hyperprior features produced by semantic hyperprior decoder hyem- This enables reconstruction using only the hyperprior signal. Fig. 20 2rpeesents additional reconstruction results on the COC02017 validation dataset (35), extending Fig. 2 from the main paper. These results confirm that the distilled model captures richer semantic information, facilitating final reconstruction.
Bitrate allocation between hy hyperprior è and latent 2. We ablate the roles of hyperprior 2 and latent á on CLIC2020 test set by fixing the hyperprior budget to 0.0035 bpp and gradually increasing the bitrate of ÿ. Table 4 reports the results and reveals a clear division of each part's 'sst's role: (a) with zero bits allocated for latent ÿ. the codec still produces coherent reconstructions (also show in Fig. 20). indicating that 2 supplies strong semantic information; (b) as bits are assigned to ÿ. fidelity and perceptual quality improve monotonically (e.g.. LPIPS drops from 0.290 to 0.119), confirming that ğ is essential for encoding fine-grained details.
Model size. We provide parameter counts, runtime comparisons (on 1024x1024 images), and BD- Rate results on the MS-COCO 30K dataset for better efficiency evaluation, as shown in Table 3
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Table Complexity analysis with model size. BD-Rate is calculated on the MS-COCO 30K dataset.
BD-Rate (Metires).
Model
Params
Enc. Time (8)
Dec. Time (s)
LPIPS
DISTS
FID
MS-ILLM
181M
0.14
0.17
138.3%
253.0%
478.4%
DifEIC
14B
0.32
12.4
305.0%
239. 1%
341.0%
PerCo (SD)
3.8B4340M-955M
0.58
8.80
538.8%
345.8%
59.6%
DifCC
950M
3.9-15.6
6.9--10.8
234.0%
196.1%
690.9%
DDCM
950M
OneDC
1.4B
0.15
0.34
0.00%
0.00%
0.00%
Open-sourced PerCo includes an additional 3.8B BLIP2 cuption model and 340M CLIP model.
Table 4: Bitrate allocation analysis the CLIC2020 dataset.
Bpp (ratio)
Bpp (ratio)
Bpp Total
PSNRT
MS-SSIM1
LPIPS!
DISTS
FIDU
0.0035 (100%)
0.0 (0%)
0.0035
19.31
0.629
0.290
0.169
14.885
0.0035 (43%)
0.0047 (57%)
0.0082
23.13
0.790
0.163
0.089
6.223
0.0035 (27%)
0.0094 (73%)
0.0129
24.20
0.826
0.139
0.077
5.560
0.0035 (18%)
0.0157 (82%)
0.0192
25.25
0.856
0.119
0.068
4.979
Compared to MS-ILLM, diffusion-based methods typically use larger models but achieve superior perceptual quality (e.g.. lower BD-Rate with FID) due to stronger generative capacity. Unlike other diffusion-based codecs, OneDC avoids external caption models and multi-step sampling, enabling over 20x faster decoding while also achieving better rate-distortion performance.
Memroy usage. We also report memory usage under the resize & center-crop setting 512 x 512 Kodak images. PerCo (SD) requires about 22,220 MB of GPU memory, whereas DDCM uses 4,186 MB and OneDC uses 8,038 MB. These results highlight that incorporating large language model, as in PerCo, substantially increases computational burden. DDCM achieves lower memory consumption by employing the SD model in zero-shot manner. Although OneDC requires more memory than DDCM, its single-step design reduces inference cost compared to both multi-step PerCo and DDCM.
B Training Details
Stage training. This stage focuses training the compression module and fine-tuning the one-step diffusion model (66] for the image reconstruction task. The training loss is defined as:
Lstagel = Lrecon + XR + aLaurr where Lyecon L1 .) + Eperceptual (z.
(6)
We use the LI as the pixel-level loss and the LPIPS [24] as the perceptual-level loss. To support various bitrates, the rate-distortion trade-off parameter is set to {0.6, 1.0, 1.8, 2.6,4.6, 7.4, 12.2].
An auxiliary code prediction loss Laur is included with weighting factor of a 0.001. We train our model on the dataset introduced in [17]. Training is performed on 4xA100 GPUs for 800,000 steps, using a three-stage learning rate schedule with Adam W 338 a) 5e-5 for the first 500,000 steps; b) le-5 for the next 200,000 steps; c) lc-6 for the final 100,000 steps. During training. image patches of size (512, 1024) are randomly cropped with probabilities of (0.6, 0.4), respectively. The batch size is set to 32 for 512 x 512 crops and 8 for 1024 x 1024 crops (across 4 GPUs). This stage takes approximately 6 days as we use high-resolution patches for training.
Stage II training. This stage fine-tunes the one-step generator to improve reconstruction realism by better aligning the distribution of its outputs with that of real images. The training objective is:
Lstagest = Ldistill + BLrreon + ILadve where:
Ldistill = EE,,g le fake (Hc, - Ereat Sreat(hh, Ladv = D-Dccl-Dssc fake (V/, t),
(8)
Here, Laistall represents the diffusion distillation loss [67. and Lude is the adversarial loss in the latent space, following [66]. Disc denotes the discriminator network, which takes the mid feature in the diffusion U-Net as the input [66). The variable ÿo is the latent output generated by the one-step diffusion model, and 1%, its noised version at timestep f.. We uniformly sample E [20, 640), since synthesizing
details does not require large noise levels. The weighting parameters
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Aec's MS-COCD 30K
W5-CICO
0.0
3.50
012
0.10
0.22
033
072
0.30
1.1
0.00
0.22
0.33
672
0.30
0.75
Figure 10: Comparison with original PerCo (9] on the MS-COCO 30K dataset.
BIRCC
JCMP4
BIC
CeADC
DIV2K
0.2
022
0.37
0.27
6.2
1.30
3.22
9/4
9.30
8.2
0.04
0.2
3.3
3.64
0.04
9.2
Figure Rate-distortion curves on the DIV2K dataset at full ressortttott setting.
set as follows: 3 = 0.625 balance the reconstruction and distillation terms, and 7 = 0.001 for the adversarial loss (y follows (66))
Specifically, diffusion distillation minimizes the expected Kullback-Leibler (KL) divergence between the tume-dependent distributions of the target Preal,t and the generator output Preal,a. thereby effectively transferring knowledge from the multi-step diffusion model to the one-step generator. The gradient used to update the one-step generator parameters @ is given by the difference between the score functions of the real and fake distributions:
V Lodistall = Et io
(PPreat,t)))))
(9)
-EE ((Sreat 8) 8 fakeeW'i f)) dea des
(10)
-E, [(Efake (it, Great((i t)) d0
(11)
Here, Sreal and Staee are the score functions learned by multi-step diffusion model G and Gaae respectively. To ensure the fake score network Clake accurately tracks the evolving distribution of the one-step diffusion model, we update it using a standard denoising loss:
Liae Br,g [lffake (T's () 1o012
(12)
The improved version of diffusion distillation proposed in [66] introduces adversarial training in the latent space further enhance distribution alignment. A discriminator is trained to differentiate between features extracted from real and generated images, using the following objective:
L Disc = [Discle fake (7l. ()) Disc(e yake
fake (Ev + m4. 1))
(13)
Here, EVAE denotes the encoder of the diffusion model, and is the sampled noise in timestep t. The fake branch is then optimized with an additional adversarial loss:
Lfake LLDis
(14)
We follow [66] and set a = 0.01. During training, the fake branch and discriminator are each updated 10 times for every update of the one-step generator, ensuring stable adversarial optimization.
Training is conducted on 4xA 100 GPUs for 1,000,000 steps. This stage requires around 12 days, as diffusion distillation introduces additional cost in addition to high-resolution training. The learning rate is fixed le-6 (with Adam for the one-step generator, fake network, and discriminator. The batch size, cropping strategy and training data identical to those used in Stage I.
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OPTICC 20291
0.3
1.2
0.30
230
322
0.30
1120
3.2
0.2
1.20
0.32
0.55
2.20
012
Figure 12: Rate distortion curves on the CLIC2020 at resize center-crop, 256 FID setting, with resized resolution 768.
PSHILMM ICN, 20030
Parc
DIISE ITSST 2014.
2024420241
DIC 23251
DieDC
5.50
3.75
8.455
1000
3.8
0.50
3.2
3.5
0.15
300
1p0
Figure 13: Comparison of methods measured by PSNR and MS-SSIM at full-resllutionn setting,
C Model Details
The overall architecture is shown in Fig 14 To ensure better alignment with the latent space of the diffusion model, we extract features from pretrained VAE encoder. Additionally, inspired by 333 we incorporate embeddings from the original input image enrich the encoder with complementary spatial and semantic information. To jointly learn compressible latents and capture high-level semantics, we introduce a lightweight U-Ner within the analysis transform. Its multi-scale design facilitates effective aggregation of both local textures and global structures. The extracted semantic features integrated into the hyperprior branch, enhancing its representational capability.
For parameter-efficiest adaptation, we insert LoRA (1 layers across all modules of the pretrained one-step diffusion U-Net, setting the LoRA rank to 64. The combined parameter count of the encoder and decoder components (ga, hene, heem and g.) 394M, while the adapted diffusion U-Net contributes an additional 928M parameters (about 860M for SD1.5 and 68M for LoRA).
Further details of the semantic distillation mechanism are provided in Fig. 15 We adopt a Swin Transformer (37 to improve adaptability across different image resolutions during training.
D Social Impact
Positive Aspects. Generative codecs offer substantial benefits by significantly reducing the storage and transmission demands of high-resolution imagery through the synthesis of perceptually convinc- ing content. This improvement in efficiency can help broaden access to high-quality visual media, particularly in handwidth-limite or resource-constrained environments. The resulting gains in data economy and speed of delivery have promising implications for applications social communication and personal entertainment.
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ga
Petx
ACe Dockk
X
Encoder
No's
NactBock
Rayer
Denc
Testrack
Da Mar
PLA M
RICH Blok
Toe
De
Figure 14: Details of model architecture. The pratrained VAE encoder is from SD 1.5. For the
U-Net used in Da. we use the implementation from the diffusers library [56]
Codebook
lobel
Eaur
Cross
Entropy
Predictor Pasox
Logits
Figure 15: Details of the auxiliary code predictor module. For Swin Transformer block [37 we set window size 16, head dim 64. W means normal window, SW means shifted window.
Negative Aspects. Despite these advantages, generative compression introduces synthesized content that may deviate from the original input, raising concerns about the fidelity and authenticity of reconstructed images. Ongoing research into multi-realise codecs 3 offers potential path forward, enabling more transparent control over the trade-off between realism and fidelity.
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Original
MS-ILLM
PerCo
Bpp LPIPS DISTS
0.021 (1.2x) 0.280 11.22)00.1544 (1.3x)
0.032 (1 / (1. /1.6x//0.166 (1.4x)
DiffEIC
DiffC
OneDC
0.021 (1.2x)/0... (1.7))).16611.4x)
0.026 (1.5x] (1.291(1131100010 0.13
(1017 0.29 (1.0x) (1.0)
Original
MS-ILLM
PerCo
Bpp L/ DISTS
0.058 (1.6x) /.276 (1. (1.1x) 0.211
0.126 (3.6)/ 0.277 (1.1g/ / (1.1
DiffEIC
DiffC
OneDC
4x] (1.33
(1.0x (..18
Figure 16: Visual results the Kodak dataset. The VAE-based MS-ILLM exhibits noticeable artifacts. Compared with our OneDC, the previous SOTA multi-step diffusion codec DiffC requires least 1.4x bitrate these examples. Zoom in for better view.
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Original
MS-ILLM
PerCo
Bop / DISTS
0.020 (1.2x) 108 (1.4x) 0.078
0,031 (2.18) 0.378 (4. (4.9k) 0.5 (2.4x)
DiffEIC
DifIC
OneDC
0.015 (1.0x) 0.215 (2.8x) 0.124 (2.44
0.015(1.2x)/ 0.11 (1,4x) 0.065 (1.3x)
0.015 (1.0x)/ 0.077 (1.0x) 0.052 (1.0%)
Original
MS-ILLM
PerCo
0.015 0..33 / 0.096
0.032 0.354 0.186
Bop L LLIPS DISTS
1.4x /1.1 /1.xx
2.9x 3.00x /3.1x
DiffEIC
DiffC
OneDC
0.011 / 0.22110..774 /0.174 20x0x/0x/22 /2.9x
0.018 0.143 0.088
0.011 0.118 0.060
/1.5x
Figure 17: Visual results on the CLIC2020 set. Zoom for better view.
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Original
MS-ILLM
PerCo
DiffEIC
DiffC
OneDC
0.026 0..36/ 0.162
0.032 /.345/ 0.188
0.022 0.370 0.2006
0.029/ /0.24/ /0.125
0.019/0.195/0 0.109
Bop
1.4x 1.2x 1.5x
1.7x 11.xx 1.7x
1.2x / 1.9x 1.9x
1.5x 1.3x /111x
1.0x / 1.0x 110x
0.021 0.250 0.137
0.032/0 0.353/ 0,208
0.017 0017/0.366 0.365 0.198
0.028 / 2.24//222///// 0.118
0.017/0.202 0.097 1.0 /1,0x x100x
1.2x 1.2x 1.4x
1.9x /1 /1.7x 2.1x
1.0x / 1.8 2.00
1.5x 1.3x /122*
Sop LPLLLPPPI//SSSSSS
0.021 / 0.208 / 0.119
0.032 0.305 00166
0.017 00017/0.50 0.321 061
0.023 / 0.227 0.088
0,015 00.16 0.086
1,4x 1.2x 114xx
2.1x /1 1111181/111. 1.9x
1.1x / 1.9x /19x
1.5x 1.4x /1.0x
1.0x 1.0x
0.022 0.032/0.337/0
0.019/0.335 1.4x / 1.8x / /155*
0.025 0.014/0.185/0..
Bop / LPIPS DISTS
1.6x 112x 1,4x
2.3x / 1.8x 1.8x
1.8x /
1.0x / 1.0x 1 1.0x
0.032/ 0.264/0
0.032
0.082 1.0x / 1.0x
Bop // LPISS 1/DITS
1.9x 1.2x 1.5x
1.9x / 1.1x/ 1.8x
1.4x / 1.6x /xx/11/ 1
1.8x / /1.1
0.016/ 0.222 0.122
0.032 /../3/022//////// 0.189
0.015 0.363 0.221
0.023 / 0.243 0..124
0.012/0.180/ 0.100
Bop LLPIPS//DISTSS
1.3x 1.2x 1.2x
/ 1.7x
1.3x / 22.xx /2.2x
1.9x 1.4x 1.2x
1.0x / 1.0x / 1.0x
Figure 18: Visual results on the CLIC2020 test set (768 x 768). Compared with OneDC, the previous SOTA multi-step diffusion method DiffC requires 1.5 higher bitrate while still producing slightly lower reconstruction quality on these examples. Zoom in for better view.
30

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Original
MS-ILLM
PerCo
DiffEIC
DifCC
OneDC
B LLIIS D
0.123 1.4x 1.4x 1,2x
/..76/
00181
1.6x / 2.2x 1.4x
1.1x 2x11 11.5x
1.1x /22x 1.8x
1.0x 1.0x 1.0x
Bop / L D
0.032 0.146
0.033 0.210
00.37
0.025 00.271
0.019/0.220/4 0.030
1,7x /111
1.7% / 11111/11111.
1.4x 1.7x 113xx
1.3x /.00 /2.1x
1.0x / 1100 1.0x
Bpp LPIPS / DISTS
0.051 0.229/ 0.147
0.033 0.393/ 0.194
0.032 0.346 14140
0.027 / 00.51 00.65
0.026 2026/0.2066 2..00/0...... D.111
2.0x 11x/1111/11.
1.3x 11.7x
1,2x 1.7x /133x
1,0x 1,7x 1.5x
1.0x 110x 1.0x
Bop LIIIPS DISTS
0.042 / 0.250/ 0.170
0.033 /0.00000 0.160.186
0.029/0.375 0..1//..77
0.025 0.365 0.178
0.024/0.200 /0.11
1.8x 1.2x 1.5x
1.4x 11.6
1.2x 1.8x /1.5x
1.7x 1.5x
1.0X 1.00X
Bop LLLIS D
0.023/0.280 0.29// 0. 0.220
0.033/0.400 /.000/0.222 00.222
0.024/0.421 002209
0.023 0.434/ 0.293
0.022/0.100 0.14///../..... 0.126
120K /.7x
1.5x 11.88/1.77..
/4.0/ /1.7x
1.0% /4.2x /2.3x
1.0% 11.0 1.0%
Bap // L
0.039 0.231 0.147
0.033 0.347 00147
0.025 0.34//0344//// 0.187
0.022 / 0.360 / 0.222
0.021 000087 0.099
1.9% /27x 1.6X
1.8X 4.0% 1.6X
1.2x 4.0% 2200x
1.0% 4. /24x
1.0% 1 1.0x
Figure 19: Visual results on the MS-COCO 30K dataset. PerCo, the previous best-performing method in terms of FID,, requires 1.4 bitrate OneDC, but still results in suboptimal fidelity. Zoom in for better view.
31

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Original
Reconsttractionioos Text Only (8)
Resonstruction from Hyparprior Only [b]
Semantic distillation
(c)
STOP
SPOP
STOP
Figure 20: Reconstructions from different semantic guidance. (a) Text prompts struggle to capture complex visual semantics, resulting in severe distortions when using a pretrained text-to-image one-step diffusion model [66]. (b) We finetune the model [66] for hyperprior-to-image generation. Hyperprior guidance yields more faithful reconstructions. (c) Our proposed semantic distillation further improves leject-level accuracy.
32

---

Table 5: Compression quantitative evaluations, for the Kodak dataset with full-resolution.
Method
BPP
PSNR
MS-SSIM
LPIPS
DISTS
MS-ILLM
0.0066
20.478
0.611
0.438
0.280
0.0156
22.224
0.722
0.292
0.177
0.0250
23.155
0.782
0.228
0.147
0.0447
24.731
0.846
0.158
0.135
0.0809
25.922
0.890
0.110
0.109
0.1535
27.532
0.928
0.073
0.081
0.2962
29.634
0.960
0.045
0.061
PerCo (SD)
0.0031
15.662
0.418
0.529
0.234
0.0324
19.344
0.673
0.299
0.162
0.1261
22.967
0.847
0.141
0.080
DiffEIC
0.0200
19.308
0.653
0.326
0.169
0.0375
20.970
0.741
0.242
0.134
0.0610
22.631
0,809
0.173
0.098
0.0916
24.147
0.861
0.129
0.077
GLC
0.0247
21.320
0.749
0.196
0.113
0.0286
21.729
0.768
0.180
0.104
0.0331
22.071
0.782
0.168
0.098
0.0374
22.279
0.791
0.161
0.095
DifCC
0.0063
18.494
0.545
0.450
0.201
0,0102
19.926
0.633
0.363
0.165
0.0155
21.212
0.705
0.288
0.139
0.0242
22.453
0.770
0.225
0.112
0.0423
23.823
0.832
0.163
0.087
0.0522
24.304
0.851
0.144
0.080
0.0625
24.704
0.865
0.130
0.074
0,0795
25.207
0.883
0.113
0.065
0.1227
25.949
0.905
0.091
0.055
OneDC
0.0034
17.141
0.472
0.380
0.204
0.0101
20.631
0.685
0.220
0.131
0.0165
21.513
0.741
0.183
0.110
0.0245
22.220
0.779
0.154
0.097
0.0354
22.979
0.817
0.133
0.085
0.0506
23.620
0.845
0.114
0.076
0,0775
24.396
0.877
0.096
0.066
0.1115
25.248
0.899
0.083
0.057
33

---

Table 6: Compression quantitative evaluations, for the CLIC2020 test set with full-resolution.
Method
BPP
PSNR
MS-SSIM
LPIPS
DISTS
FID
MS-ILLM
0.0045
22.815
0.731
0.365
0.216
44.954
0.0120
25.100
0.813
0.225
0.131
15.329
0.0195
26.275
0.853
0.173
0.106
8.748
0,0359
27.875
0.898
0.114
0.086
6.261
0,0654
29.229
0.928
0.080
0.066
4.481
0.1240
30.865
0.953
0.054
0.048
2.645
0.2377
32.833
0.972
0.035
0.035
1.660
PerCo (SD)
0.0022
15.340
0.473
0.589
0.326
76.658
0.0315
16.733
0.537
0.389
0.194
18.039
0.1249
17.419
0.554
0.306
0.091
5.013
DifIEIC
0.0142
20.152
0.739
0.308
0.185
18.427
0.0275
22.173
0.808
0.222
0.132
11.039
0,0487
24.656
0.865
0.153
0.089
7.278
0.0776
26.673
0.905
0.109
0.060
4.942
DiffC
0.0047
21.571
0.717
0.311
0.179
20.076
0.0079
22.914
0.771
0.253
0.144
12.332
0.0123
24.040
0.811
0.210
0.117
8.957
0.0197
25.241
0.849
0.171
0.092
6.813
0.0362
26.745
0.889
0.128
0.067
5.252
0,0556
27.772
0.911
0.103
0.053
4.414
0,0724
28.357
0.923
0.090
0.046
3.929
0.0872
28.733
0.930
0.082
0.042
3.685
OneDC
0,0035
19.305
0.629
0.290
0.169
14.885
0.0083
23.129
0.789
0.164
0.089
6.223
0.0130
24.201
0.826
0.139
0.077
5.560
0.0192
25.248
0.856
0.119
0.068
4.979
0.0279
26.096
0.879
0.103
0.060
4.234
0.0401
26.912
0.899
0.090
0.053
3.581
0,0617
27.804
0.921
0.076
0.044
2.977
0.0902
28.596
0.936
0.065
0.037
2.410
34

---

Table 7: Compression quantitative evaluations, for the MS-COCO 30K dataset at 512 x212xx1222 resoutiin..
Method
BPP
PSNR
MS-SSIM
LPIPS
DISTS
FID
MS-ILLM
0.0092
20.411
0.641
0.397
0.255
72.693
0.0196
22.708
0.766
0.257
0.169
17.992
0.0296
23.948
0.821
0,200
0.145
9.041
0,0488
25.517
0.875
0.138
0.122
4.100
0.0835
26.972
0.912
0.095
0.099
2.032
0.1510
28.734
0.944
0,063
0.075
0.990
0.2850
30.930
0.969
0.039
0.058
0.457
PerCo (SD)
0.0036
14.126
0.388
0.545
0.245
4.467
0.0329
18.124
0.676
0.311
0.159
2.748
0.1267
22.802
0.869
0.134
0.080
1.152
DifIEIC
0.0217
18.778
0.676
0.318
0.171
6.151
0.0407
20.564
0.770
0.229
0.133
3.929
0,0653
22.710
0.840
0.159
0.099
2.578
0.0975
24.606
0.886
0.116
0.077
1.911
DiffC
0.0083
16.170
0.495
0.524
0.271
90.989
0.0133
18.306
0.611
0.427
0.229
57.542
0.0198
20.159
0.705
0.334
0.187
28.909
0.0303
22.029
0.786
0.241
0.140
9.763
0.0514
24.170
0.859
0.155
0.095
2.805
0.0628
24.915
0.879
0.132
0.082
2.135
0.0745
25.520
0.894
0.115
0.074
1.809
0.094
26.264
0.910
0.098
0.064
1.518
OneDC
0,0034
16,083
0.460
0.376
0.211
13.607
0.0112
20.696
0.721
0.201
0.128
3.496
0.0179
21.924
0.779
0.166
0.111
2.817
0.0260
22.924
0.820
0.139
0.097
2.379
0.0371
23.809
0.852
0.118
0.086
2.044
0.0521
24.650
0.878
0.101
0.076
1.719
0,0789
25.674
0.905
0.082
0.064
1.365
0.1137
26.662
0.924
0.069
0.055
1.043
35

---

Table 8: Compression quantitative evaluations, for the DIV2K test set at full-resolution.
Method
BPP
PSNR
MS-SSIM
LPIPS
DISTS
FID
MS-ILLM
0.0054
20.647
0.647
0.396
0.234
79.455
0.0159
22.741
0.767
0.254
0.143
33.303
0.0262
23.837
0.822
0.198
0.116
22.428
0,0467
25.493
0.880
0.138
0.102
18.537
0.0837
26.878
0.917
0.097
0.080
13.574
0.1547
28.503
0.946
0.065
0.055
8.070
0.2895
30.545
0.969
0.040
0.040
5.123
PerCo (SD)
0.0022
14.529
0.381
0.570
0.320
95.345
0.0316
15.411
0.432
0.417
0.218
36.671
0.1249
15.340
0.405
0.355
0.105
11.314
DifIEIC
0.0182
18.615
0.663
0.327
0.190
34.527
0.0349
20.532
0.759
0.241
0.132
21.656
0,0575
22.604
0.831
0.172
0.089
14.026
0.0876
24.415
0.883
0.125
0.061
9.644
DiffC
0.0056
19.091
0.623
0.353
0.198
45.767
0.0096
20.345
0.695
0.291
0.158
32.086
0.0149
21.422
0.750
0.244
0.127
24.270
0.0238
22.599
0.803
0.199
0.098
18.541
0.0427
24.113
0.859
0.149
0.071
14.098
0.0640
25.158
0.890
0.120
0.056
11.859
0.0820
25.755
0.905
0.104
0.048
10.622
0,0976
26.143
0.914
0.096
0.044
9.938
0.1273
26.645
0.925
0.085
0.039
8.983
OneDC
0.0097
20.615
0.715
0.200
0.103
15.874
0.0161
21.719
0.771
0.168
0.086
13.680
0.0239
22.703
0.813
0.144
0.077
12.365
0.0345
23.546
0.846
0.124
0.067
11.242
0.0495
24.351
0.873
0.108
0.060
10.096
0.0749
25.257
0.901
0.090
0.050
8.325
0.1064
26.086
0.920
0.077
0.040
6,822
36

---

Table 9: Compression quantitative evaluations, for the Kodak dataset at resize & center-crop setting with 512 512 resolution.
Method
BPP
PSNR
LPIPS
FID
MS-ILLM
0.085
25.683
0.11
36.08
0.159
27.296
0.072
28.556
0.304
29.395
0.044
24.448
PerCo (SD)
0.033
19.017
0.307
37.019
0.127
22.325
0.145
26,418
DDCM
0.03
22.066
0.222
32.031
0.038
22.551
0.19
29.117
0.05
23.013
0.161
25.647
0.095
23.606
0.138
24.215
0.149
24.069
0.124
23.199
OneDC
0.0109
20.259
0.223
36.862
0.0177
21.219
0.183
33.075
0.0257
21.970
0.154
30.947
0,0369
22.718
0.132
29.103
0.0524
23.363
0.113
27.428
0.0795
24.202
0.095
25,452
0.1139
25.021
0.082
23.448
Table 10: Compression quantitative evaluations, for the CLIC2020 test set at resize & center-crop setting with 768 768 resolution.
Method
BPP
PSNR
L.PPS
FID
MS-ILLM
0.006
21.304
0.447
62.327
0.009
22.703
0.34
41.479
0.072
27.808
0.084
7,852
0.134
29.567
0.055
5.966
PerCo (SD)
0.003
15.339
0.517
30.409
0.032
19.018
0.269
12.869
0.126
23.387
0.122
5,419
DDCM
0.007
19.672
0.404
23.862
0.008
20.532
0.354
19.521
0.01
21.207
0.314
15,559
0.014
22.116
0.262
11.362
0.017
22.722
0.227
8.722
0.022
23.366
0.192
6.753
0.042
24.136
0.156
5.051
0,066
24.739
0.133
4.549
0.137
25.65
0.108
4.132
OneDC
0.0034
17.619
0.326
15.325
0.0098
22.378
0.172
7.953
0,0155
23,600
0.140
7.390
0.0228
24.641
0.117
7.011
0.0326
25.532
0.099
6,538
0.0461
26.416
0.084
5.874
0.0694
27.463
0.069
4.915
0.0993
28.471
0.058
4.046
37


Reviews:
[{"review_id": "om2rfGAAoZ", "reviewer": "Reviewer_5a5J", "summary": "This paper proposes a novel method for ultra low rate image compression. It merges hyper prior latents as semantic conditioning, one-step diffusion decoding, and a semantic distillation mechanism. Performance comparisons at the ultra low rate regime are provided.", "questions": "See weaknesses", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths\n- This paper provides a mature diffusion-based compressor at the ultra low rate regime. To me, it fulfills some of the promises of rate-distortion-perception. It also seems to integrate some of the latest techniques in deep learning in a successful fashion for image compression, which is a non-trivial accomplishment. \n- The combination of the chosen components allow for great tradeoffs in speed and performance.\n- Experiments are fairly extensive, with ablation studies showing the benefit of each component.\n\n\nWeaknesses:\n- a discussion on training time would be helpful\n- Performance could potentially be improved by doing full fine-tuning, rather than LoRA. I suppose it would be more resource intensive, but I am not sure if this was compared to.", "was_revised": true, "final_justification": "I think this is a strong paper and valuable contribution to the conference. I maintain my original positive score.", "rebuttal": "Many thanks for your valuable suggestions and the recognition on our contributions. We hope that the following responses can address your comments.\n\n**1. Discussion of training time**\n\nDuring training, image patches of size {512×512, 1024×1024} are randomly cropped with probabilities {0.6, 0.4} to enhance generalization to high-resolution inputs. We use a two-stage training strategy and 4×A100 80G GPUs to train our models.\n\n- Stage I trains the latent compression module and adapts it to the pre-trained one-step diffusion model for the reconstruction. It takes approximately 6 days as we use high-resolution patches for training.\n    \n- Stage II further fine-tunes the one-step diffusion model to enhance the realism via latent-domain distillation and preserves fidelity through pixel-domain supervision. This stage requires around 12 days, as diffusion distillation introduces additional computational cost in addition to high-resolution training.\n\nThanks for your great suggestion. We will add these details into our revision, and will also release our training code for reproducibility. In addition, we will explore accelerated training strategies, such as the improved distillation method, to reduce training cost in the future. \n\n\n**2. Full fine-tuning vs. LoRA**\n\nWe conducted additional experiments using full fine-tuning (FT) starting from the λ = 7.4 checkpoint originally used in our LoRA-based model. We denote this variant as LoRA + FT in below table. Owing to the significantly slower convergence of full fine-tuning compared to LoRA, we were limited to roughly 15% of the Stage II training iterations during the rebuttal period.\n\nThe preliminary results on the CLIC2020 dataset are below:\n\n| Method      | Bpp   | LPIPS ↓ | DISTS ↓ | FID ↓ |\n| ----------- | ----- | ------- | ------- | ----- |\n| LoRA        | 0.013 | 0.139   | 0.077   | 5.56  |\n| LoRA + FT   | 0.013 | 0.138   | 0.076   | 5.48  |\n\nThese early findings suggest that full fine-tuning can yield some improvements over LoRA under limited training conditions. In future work, we plan to explore more efficient training methods and further improve generation performance."}, {"review_id": "8hynivMTh4", "reviewer": "Reviewer_mw2X", "summary": "The paper proposes OneDC, a one-step diffusion-based image codec designed for high-quality compression at ultra-low bitrates. The method combines a latent compression module with a one-step diffusion generator, guided by a hyperprior enhanced via distillation. A two-stage training strategy is used to improve efficiency and reconstruction quality, and experiments show competitive perceptual performance with fast decoding.", "questions": "- What’s the difference between scalar quantization and finite scalar quantization in Figure 3? Why not adopt a unified choice—either using finite scalar quantization throughout or sticking to general scalar quantization?\n- What is the connection between Figure 3 and Figure 4? Several symbols used in Figure 4 are not defined in the main text, which makes it difficult to follow.\n- What is the input to the model during inference, and where does it come from?\n- The values for DISTS, LPIPS, and FID are all 0.00 in Table 1 and 2. Is this normal?", "limitations": "One limitation of the proposed method is that it involves multiple training stages and multiple loss functions, which can make tuning the loss-related hyperparameters particularly challenging in practice. This increases implementation complexity and may hinder reproducibility or practical deployment.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Pros:\n\n- The writing is generally fluent, making the paper easy to read and follow.\n- Figure 3 is clear and well-designed, which helps readers understand the proposed method more easily.\n\nCons:\n\n- The proposed approach seems overly complicated, as it involves a large number of modules and a two-stage training paradigm where each stage requires coordinating multiple loss functions. As a result, the conceptual clarity of the work is reduced, and readers may find it difficult to extract clear insights from such an engineering-heavy solution.\n- Writing could be further improved. I suggest the authors better articulate the key contributions. It is somewhat unclear whether the main novelty lies in proposing a new overall architecture or in the design of certain modules. I recommend explicitly stating this and providing more comparisons with existing work to help position the paper more clearly in the context of related literature.\n- In lines 54–55, the authors mention that text-based guidance often leads to significant computational overhead, and that their method is more efficient. However, the experiments do not provide any comparison in terms of model size. I suggest including a parameter comparison (as done in DiffC) to better support the claimed efficiency.\n- Some equations are incomplete. For example, in Equation (2), loss inputs are not specified.", "was_revised": true, "final_justification": "I updated my score based on the authors' reply.", "rebuttal": "Many thanks for your valuable comments. We hope that the following responses can address your concerns.\n\n**1. Complexity of Architecture & Training**\n\nOur design philosophy balances performance with simplicity. Actually, many recent generative codecs require sophisticated design, but we have streamlined our architecture and training to be more integrated.\n\n- Architectural Simplicity: Our OneDC integrates a one-step diffusion generator into a standard hyperprior codec framework. This design is self-contained and avoids the heavy external dependencies or complex add-ons seen in other methods. For instance:\n  - PerCo [1] relies on large, separate vision-language (BLIP2) and text-embedding (CLIP) models for conditioning.\n  - DiffEIC [2] incorporates a ControlNet and a custom Latent Feature-Guided Compression Module, increasing structural complexity.\n  - DiffC [3] uses reverse-channel coding over many steps, requiring specialized CUDA kernels and complex scheduling.\n\nIn contrast, OneDC replaces external conditioning with a simple yet semantic-aware hyperprior.\n\n- Training Strategy: We adopt a two-stage training strategy, a well-established and effective paradigm used by leading methods like HiFiC[4] / MS-ILLM[5]. \n  - Stage I focuses on learning the compression part.\n  - Stage II refines the decoding for perceptual realism.\n\nThis principled approach avoids the more intricate multi-stage pipelines of other models like GLC[6], which involves a three-stage process of training a VQGAN, transform coding, and then end-to-end fine-tuning.\n\nWe believe our approach represents a proper trade-off between innovation and practicality. We will *release our full training code* to ensure transparency.\n\n**2. Novelty & comparison**\n\nWe appreciate the opportunity to clarify our contributions. The primary novelty lies in the *design of a new codec architecture that successfully integrates a one-step diffusion generator with a hyperprior-based latent compressor.* This unified design achieves SOTA performance while being significantly more efficient and self-contained than previous diffusion-based codecs.\n\nOur key technical contributions that enable this framework are:\n\n- *Hyperprior as Semantic Guidance:* Unlike some prior works that rely on heavy external models (e.g., BLIP2, CLIP) for text-based conditioning, we are the first to leverage the *hyperprior as a direct source of semantic guidance* for the diffusion model. This approach is not only more efficient but also provides spatially-aware context that is better aligned with the image content, eliminating the need for textual prompts.\n\n- *Semantic distillation:* To tap the potential of the hyperprior for its new role, we introduce a *semantic distillation* mechanism. During training, we distill knowledge from a powerful pretrained generative tokenizer into the hyperprior branch. This novel step enriches the hyperprior’s semantic representation, making it an effective conditioning signal for the generator.\n\n- *Integrated Architecture and Training:* Our framework is designed for simplicity and reproducibility.\n  - Architecture: While methods like DiffEIC or PerCo bolt on complex modules like ControlNet or large language models, our design is a self-contained system. It replaces complex conditioning with a learnable, integrated hyperprior.\n  - Training: We adopt a principled two-stage training strategy (similar to HiFiC/MS-ILLM) that first establishes a robust latent representation and then refines for perceptual realism. This hybrid-domain approach (pixel and latent) ensures stable optimization and balances fidelity with realism, avoiding the more complex multi-stage pipelines of other generative codecs.\n\nIn summary, while we build upon established concepts like hyperpriors and diffusion models, our novelty stems from their *synergistic integration into a new, streamlined framework* specifically designed for efficient, high-fidelity, *one-step* generative compression.\n\n**3. Model size & parameter**\n\nWe provide parameter counts, runtime comparisons (on 1024×1024 images), and BD-Rate results on the MS-COCO 30K dataset:\n\n|Model|Params|Enc. Time (s)|Dec. Time (s)|BD-Rate (LPIPS)↓|BD-Rate (DISTS)↓|BD-Rate (FID)↓|\n|-|-|-|-|-|-|-|\n|MS-ILLM|181M|0.14|0.17|76.9%|225.3%|247.9%|\n|DiffEIC|1.4B|0.32|12.4|247.8%|100.1%|163.3%|\n|PerCo (SD)|3.8B+340M+955M\\*|0.58|8.80|410.8%|265.9%|75.2%|\n|DiffC|950M|3.9\\~15.6|6.9\\~10.8|114.9%|96.2%|144.2%|\n|**OneDC**|**1.4B**|**0.15**|**0.34**|**0.00%**|**0.00%**|**0.00%**|\n\n\\* Open-sourced PerCo (SD) includes additional 3.8B BLIP2 caption model and 340M CLIP text embedding model.\n\n0.0 in BD-Rate means we use the OneDC as anchor for comparison. See our response for question 8 for more details.\n\nCompared to MS-ILLM, diffusion-based methods typically use larger models but achieve superior perceptual quality (e.g., lower BD-Rate with FID) due to stronger generative capacity. Unlike other diffusion-based codecs, OneDC avoids external caption models and multi-step sampling, enabling over 20× faster decoding (0.34 s vs. 6.9\\~12.4 s) while also achieving better rate-distortion performance. We will include these parameter and runtime comparisons in the revision to support the claimed efficiency.\n\n**4. Equation input**\n\nThanks for your suggestions. We will clarify the input of each equation in the revision.\n\n**5. Choice of Scalar quantization (SQ) / finite scalar quantization (FSQ)**\n\nOur model uses FSQ for the hyperprior branch and standard SQ for the main latent branch. This design is intentional and crucial for balancing semantic representation and reconstruction fidelity.\n\n- *FSQ for Hyperprior (Semantic Representation):* The hyperprior is designed to capture high-level semantic information. FSQ, an improved variant of vector quantization, excels at learning a discrete, categorical representation from a large codebook. This makes it ideal for modeling the semantic-rich features in the hyperprior, providing effective guidance for the generative model.\n\n- *SQ for Main Latent (Low-Level Detail Preservation):* The main latent must preserve fine-grained visual details for high-fidelity reconstruction. SQ provides a larger, more continuous-like quantization space that is better suited for this task, minimizing information loss for low-level textures and structures.\n\nTo validate it, we compare our approach against two alternatives: using SQ for both branches (Dual SQ) and FSQ for both branches (Dual FSQ).  All models, including the compared OneDC, are trained from scratch under the same training duration in Stage I for fair comparison. The results below show performance degradation (BD-Rate increase) relative to our OneDC model.\n\n|Variant|BD-Rate (DISTS)↓|BD-Rate (FID)↓|\n|-|-|-|\n|Dual SQ|48.3%| 45.9%|\n|Dual FSQ|79.1%| 39.8%|\n\nThe results confirm our hypothesis:\n- Using SQ for the hyperprior (Dual SQ) weakens its semantic modeling capability, leading to a significant drop in perceptual quality (higher BD-Rate with FID).\n - Using FSQ for the main latent (Dual FSQ) harms its ability to preserve fine details, resulting in a substantial loss of fidelity (higher BD-Rate with DISTS).\n\nIt verifies our hybrid quantization strategy, which effectively assigns the proper quantization method to each branch based on its specific role.\n\n**6. Connection between Fig. 3~4 & symbol definitions**\n\nThe two figures are complementary and serve different purposes:\n\n- Fig. 3 shows the **inference pipeline** about the interaction between latent compression module and the one-step diffusion generator. It highlights two key points:\n\n  - The integration of a latent compression module and a one-step diffusion model.\n\n  - The use of hyperprior-based semantic guidance instead of text-based conditioning for the diffusion U-Net.\n\n- Fig. 4 presents the **training pipeline**, detailing the two-stage process and indicating which components are updated or used only during training:\n\n  - Stage I trains the compression module and the one-step diffusion model for reconstruction, with a distillation mechanism to enhance the hyperprior’s semantic representation.\n        \n  - Stage II fine-tunes the one-step diffusion model using latent-domain diffusion distillation to improve generation realism, combined with a pixel-domain loss to preserve fidelity.\n\nWe will improve cross-referencing between the figures and explicitly define all symbols in the revision. \n\n**7. Input during inference**\n| |Input|Output|\n|-|-|-|\n|Encoding|uncompressed RGB image|compressed bit-stream|\n|Decoding|compressed bit-stream|reconstructed RGB image|\n\n**8. The 0.0 value in Table 1 / 2**\n\nIn Table 1 / 2, we report Bjontegaard Delta-Rate (BD-Rate)[7] to reflect bitrate change under a specific quality metric when compared the anchor method. A negative BD-Rate value means bitrate savings over the anchor method, while a positive value means bitrate increase. The BD-Rate value over anchor itself is 0.0%.\n\nIn our paper, we use the final OneDC model as the anchor. Accordingly:\n\n- In Table 1, BD-Rate reflects performance degradation when individual components of OneDC are ablated.\n- In Table 2, BD-Rate reflects the degradation of other codecs compared to OneDC.\n\nIn the revision, we will clarify it and annotate the table headers as “BD-Rate (Quality Metric)” for improved clarity.\n\n[1] Towards image compression with perfect realism at ultra-low bitrates, ICLR 2024.\n\n[2] Towards extreme image compression with latent feature guidance and diffusion prior, TIP 2024.\n\n[3] Lossy compression with pretrained diffusion models, ICLR 2025.\n\n[4] High-fidelity generative image compression, NeurIPS 2020.\n\n[5] Improving statistical fidelity for neural image compression with implicit local likelihood models, ICML 2023.\n\n[6] Generative latent coding for ultra-low bitrate image compression, CVPR 2024.\n\n[7] Calculation of average PSNR differences between RD-curves, VCEG-M33."}, {"review_id": "ygLtNVtKXZ", "reviewer": "Reviewer_ukYU", "summary": "This paper introduces a one-step diffusion-based generative image codec designed for ultra-low bitrate scenarios. The authors argue that multi-step sampling is not essential for generative image compression and propose a lightweight alternative that leverages a semantic distillation mechanism from a pretrained tokenizer into the hyperprior. The system combines latent compression with one-step conditional generation using a pretrained diffusion backbone, and further employs hybrid-domain optimization to improve perceptual realism and fidelity. Experimental results demonstrate that OneDC achieves state-of-the-art visual quality and over 40% bitrate reduction with 20× faster decoding compared to existing multi-step diffusion codecs.", "questions": "One important request I would like to make is that, for the sake of the broader advancement of our research field, I strongly encourage the authors to release the training code in a reproducible form. Some state-of-the-art methods in neural compression—such as the DCVC family in video compression—have demonstrated strong performance, but their lack of publicly available training code has severely hindered reproducibility. Please keep in mind that reproducibility is a essential attribute of any high-quality research paper.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "[Strength]\n\nThis paper is well-written and clearly structured, maintaining coherence throughout in articulating its main claims. The contributions are clearly stated, and the authors support their claims with a comprehensive set of experiments.\n\nIn particular, the discussion on the limitations of using text as a global context for generative models is compelling, and the proposed alternative of utilizing hyperprior for semantic guidance introduces a meaningful novelty. Even considering that some training techniques are adopted from prior works, such as GLC and DMD2, the use of code prediction loss for semantic context in the first training phase, and the balanced integration of latent space distillation and pixel-level fidelity optimization in the second phase are notably impressive.\n\nAs a result, the proposed approach not only significantly outperforms existing generative compression methods but also largely mitigates the complexity overhead typically associated with multi-step diffusion models. The reviewer believes that this method could serve as a significant milestone in the field.\n\nA few points of concern that arose during the review process are detailed in the following weakness section.\n\n[Weakness]\n\n(1)\tOne of the key questions is how much bitrate is allocated to the low-level representation \\(\\hat{\\bm{y}}\\), and how its exclusion would affect the reconstruction. While the paper focuses heavily on the use of semantic context—particularly as demonstrated in the results of Fig.~2, which suggest that a significant portion of the reconstruction is determined by the semantic context derived from the hyperprior—the influence of the \\(\\hat{\\bm{y}}\\) (or \\(\\tilde{\\bm{y}}_t\\)) representation is comparatively underemphasized. Providing the relative bitrate portion of the two bitstreams at different rates, along with an ablation study that shows the impact of removing the \\(y\\)-branch both quantitatively and qualitatively, would further improve the completeness of the paper.\n\n(2)\tIn a similar context, it is unclear why the subscript \\(t\\) is used in \\(\\tilde{\\bm{y}}_t\\). Since \\(\\tilde{\\bm{y}}_t\\) is reconstructed from \\(\\hat{\\bm{y}}\\), it should follow a distribution distinct from random noise. However, there appears to be no time-dependent operation involved in the process of reconstructing \\(\\tilde{\\bm{y}}_t\\). It would be helpful for the authors to revisit this notation and, if possible, visualize how this representation differs from random noise.\n\n(3)\tThe paper would benefit from a more detailed explanation of how the semantic context (C) is injected spatially into the model via cross-attention. Since this process is critical to overcoming the limitations of textual tokens, as claimed by the paper, it should be explicitly described in the main text or, if necessary, included in the supplementary material.\n\n(4)\t(Minor) In Fig.~4, the semantic context (C) is not depicted as being injected into the one-step U-Net, which may lead to confusion. It would be helpful to revise this figure to reflect the actual flow.\n\n(5)\t(Minor) In Eq.~(1), the expression “\\text{logits} = P_{\\text{aux}}(c)” is somewhat misleading, as logits generally refer to pre-softmax activations rather than actual probabilities. Simply using \\(P_{\\text{aux}}(c)\\) would likely be sufficient.\n\n(6)\t(Minor) In Table~1, it would be helpful to include results for a setting without second-stage training (“w/o second-stage training”) to better understand the contribution of the hybrid-domain fine-tuning step.", "was_revised": true, "final_justification": "I maintain the original rating, taking into account the reviewer’s withdrawal and the subsequent discussion.\n\nThe authors have sincerely responded to the inquiry regarding the remaining semantic context injection method.", "rebuttal": "Many thanks for your valuable comments and the recognition on our contributions. We hope the following responses could address your concerns.\n\n**1. On the role and bitrate portion of $\\hat{y}$**\n\nThe hyperprior latent $\\hat{z}$ provides high-level semantic guidance, while the main latent $\\hat{y}$ preserves low-level details for reconstruction. To analyze their respective contributions, we conducted an ablation study on the CLIC2020 dataset. We fixed the bitrate for the semantic hyperprior $\\hat{z}$ at 0.0035 bpp and progressively increased the bitrate allocated to the main latent $\\hat{y}$.\n\n| Bpp $\\hat{z}$ (ratio)    | Bpp $\\hat{y}$ (ratio)    | Bpp Total | PSNR↑| MS-SSIM↑| LPIPS↓| DISTS↓| FID↓|\n|------------------|------------------|-----------|-------|----------|--------|--------|--------|\n| 0.0035 (100%)    | 0.0 (0%)         | 0.0035    | 19.31 | 0.629    | 0.290  | 0.169  | 14.885 |\n| 0.0035 (43%)     | 0.0047 (57%)     | 0.0082    | 23.13 | 0.790    | 0.163  | 0.089  | 6.223  |\n| 0.0035 (27%)     | 0.0094 (73%)     | 0.0129    | 24.20 | 0.826    | 0.139  | 0.077  | 5.560  |\n| 0.0035 (18%)     | 0.0157 (82%)     | 0.0192    | 25.25 | 0.856    | 0.119  | 0.068  | 4.979  |\n\nThe results demonstrate the division of roles:\n-   *Semantic Foundation ($\\hat{z}$):* Even with zero bits for $\\hat{y}$, the model reconstructs a coherent image (FID=14.89), confirming that $\\hat{z}$ provides the core semantic structure.\n-   *Fidelity Enhancement ($\\hat{y}$):* Allocating more bits to $\\hat{y}$ leads to substantial improvements across all fidelity and perceptual metrics (e.g., LPIPS drops from 0.290 to 0.119). This confirms that $\\hat{y}$ is crucial for encoding fine-grained details.\n\nThanks for your great suggestion; we will include this analysis in the revision.\n\n\n**2. Regarding the notation**\n\nThanks for your great suggestion. To clarify, we will revise the notation $\\tilde{y}_t, \\tilde{y}_0$ to better reflect the actual data flow:   \n- subscript  t→in : the latent decoded from the compressed bitstream, serving as the input to the one-step diffusion model.\n- subscript  0→out : the denoised latent produced by the one-step diffusion model.\n\nThe new notation removes the unnecessary implication of a timestep and more clearly represents the flow of inference in our one-step framework. We will revise the figure and text accordingly in the revision.\n\n\n**3. Regarding how semantic context is injected via cross-attention**\n\nThe semantic context $c\\in R^{B \\times N \\times D}$, extracted from the quantized hyperprior $\\hat{z}$ via a semantic decoder $h_{\\text{sem}}$，is injected into the cross-attention layers of the one-step diffusion U-Net. Here $N = H \\times W$ corresponds to the spatially flattened hyperprior feature map and $D$ is the embedding dimension.\n    \nIn each cross-attention block, the latent feature serves as the query $Q$, while the semantic context $c$ provides the key $K$ and value $V$:\n    \n$\\text{Attention}(\\mathbf{Q}, \\mathbf{K}, \\mathbf{V}) = \\text{softmax}\\left( \\frac{\\mathbf{Q} \\mathbf{K}^\\top}{\\sqrt{D}} \\right) \\mathbf{V}$\n    \nThis design allows every spatial location in the latent to reference relevant semantic context adaptively, improving reconstruction quality.\n\nThanks for your great suggestion, we will revise the paper to clarify this process in the final version.\n\n\n**4. Open-sourcing**\n\nWe fully agree with the reviewer's opinion on open-sourcing. We make the commitment that we will release the both training and testing code for our paper.\n\n\n**5. Minor issues**\n\nThank you for these kind reminders.\n    \n- We will revise Fig. 4 to explicitly show the injection of semantic context $c$ into the one-step U-Net for clarity.\n  \n- We will revise Eq. (1) to use $P_{aux}(c)$ directly, avoiding the potentially misleading use of “logits.”\n  \n- We will include the “w/o second-stage training” setting in Table 1 to clarify the impact of hybrid-domain fine-tuning."}, {"review_id": "KeNH6G104m", "reviewer": "Reviewer_hWZW", "summary": "The paper introduces OneDC, a one-step diffusion-based image compression method. The authors distill a multi-step diffusion model for single-step generation, yielding very fast compression. Additionally, the authors replace text-based conditioning with semantic conditioning from a (further distilled from a generative tokenizer) VQ module called the hyperprior. They train LoRAs on the diffusion model, using a hybrid training for both pixel-space and latent-space. Experiments show that OneDC achieves state-of-the-art performance.", "questions": "- Considering the model’s size, despite it being fast – how much memory does it take at inference time? \n- the results of the fine-tuned MS-ILLM and PerCo baffle me. The original MS-ILLM checkpoints include vlo1 and vlo2 which produce much worse results, while PerCo here shows very bad performance to their own reported numbers. Could you please explain this inconsistency with prior work?\n- Why focus on such a low bitrate range?\n- Did you indeed add 928M parameters using the LoRA on a 860M model?\n- Can the model maybe be finetuned easily and fast to support additional bitrates?", "limitations": "No.\nThe paper does not discuss the implications of training a new model per bitrate (L203), which is a significant limitation for real-world use, especially considering the size of the model and the training time. Additionally, as mentioned, any latent space-based method suffers from limitations with regards to pixel space distortion.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths \n\n - The visual results seem very strong quality wise, and very compelling. \n\n- Practically speaking, the move towards one-step diffusion compression is very meaningful due to the decreased latency. \n\n- The ablation studies are thorough and supports the authors’ claims. \n\n## Weaknesses \n\n- The evaluated bitrate range is narrow (between around 0.01 and 0.05), and the method is *not* (L232) tested at extremely low bitrates and higher bitrates. For example, PerCo SD[1] test BPPs of 0.0019-0.12, DDCM[2] test BPPs of around 0.2-0.003 for the same sized images, DiffC test between 0.002->1. This raises questions about generalizability across compression regimes. \n\n- From the appendix, the authors claim (L93) that the rank 64 LoRA on the U-Net added 928M parameters (in addition to the 394M parameters for the other decoder parts). However, the used base model is SD1.5 (L219) includes only 860M parameters. The authors should explain how using LoRA here is faster and preserving rich priors (L195), since it seems like the added weight trained an entirely new denoiser, and the weights are no longer low rank. Additionally, how long did the training actually take? (The 800K stage 1 training steps & the following 1M stage 2 training steps). The size of this heavy model should also be compared to previous works, e.g. PerCo only fine tunes the model, while DDCM use it in a zero-shot manner.  \n\n- The paper does not discuss the implications of training a new model per bitrate (L203), which is a significant limitation for real-world use, especially considering the size of the model and the training time. \n\n- Some of the writing is unclear and not very easy to follow.  \n\n  - The hyperprior is not a very commonly used module, and therefore the authors should give more background on it.  \n\n  - Figure 3 is confusing. $g_s$, which is part of the decoder, is marked as part of the compression module. I understand that it is trained, however the visualization is confusing. An additional block might help here. \n\n  - The paper includes multiple comparisons to PerCo, which cannot be done as their internal model was not released. The authors only state it’s \"Perco (SD)\" in the supplementary text. This should be clearly stated in the main text. \n\n- The authors claim in several parts (e.g. L2, L28) how traditional codecs often produce blurry details at low bitrate or focus on perception rather compromising reconstruction fidelity. This is known and expected due to the rate-perception distortion tradeoff [3] and should be cited accordingly. \n\n- Several claims are overstated or misleading.  \n\n  - The “40% bitrate reduction” is benchmarked against a single metric at a time, which gives little information with regards to the known rate-distortion-perception tradeoff [3] (E.g., maybe OneDC is better at optimizing distortion but substaintialy hurts the perception while other methods hurt it less?). Additionally, these numbers are not compared to SOTA baselines (L246) (e.g. [2]). Table 1 and 2 include percentages changes but this is not clearly conveyed to the reader and can be confusing. As the scale of the changes seems small from the graphs, a table of concrete numbers would help here. \n\n  - L8,L54 seem to imply that no other works have compressed using diffusion models without text, which is untrue (as you mention yourself, [4] do not use them, neither do [2], and I’m sure many other works). \n\n- Important metrics like PSNR are missing from the main text (Fig. 5) and only appear in the appendix, limiting fair comparison to non-latent-space methods such as MS-ILLM. This would expose the big limitation in using latent space models in compression due to the incurred loss in pixel space.\n\n- Regarding L229 - FID on kodak can be done using smaller patches (which is quite commonplace, see e.g. [2]). Your FID is already calculated using patches, as noted in the appendix. This should be noted in the main text. \n\n- Missing citation: \n\n  - Foundational diffusion works like DDPM and Sohl-Dickstein’s original paper are not cited \n\n  - Consistency distillation can be considered for citation here as well due to the distillation of a diffusion model for single-step generation. \n\n \n\n[1] N Körber. “PerCo (SD): Open Perceptual Compression.” , Workshop on Machine Learning and Compression, NeurIPS 2024. \n\n[2] Ohayon, Guy, Hila Manor, Tomer Michaeli, and Michael Elad. \"Compressed Image Generation with Denoising Diffusion Codebook Models.\" arXiv preprint arXiv:2502.01189 (2025). \n\n[3] Blau, Yochai, and Tomer Michaeli. \"Rethinking lossy compression: The rate-distortion-perception tradeoff.\" In International Conference on Machine Learning, pp. 675-685. PMLR, 2019.  \n\n[4] Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge, and Jingwen Jiang. Towards extreme image compression with latent feature guidance and diffusion prior. IEEE Transactions on Circuits and Systems for Video Technology, 2024.", "was_revised": true, "final_justification": "The authors responded to my concerns. If they fix/mention what was discussed, the paper should be accepted in my opinion. For example:\n- They trained on a wider bitrange.\n- They clarified details such as the parameters count and promise to clarify details in the final version (such as the use of percentages which seems to have confused even another reviewer, etc.).\n- They promise to expand the limitation section\n- They promise to include concrete numbers of evaluation.\n- They promise to add missing citations and comparison to SotA related works.", "rebuttal": "Many thanks for your valuable comments and the recognition on our contributions. We hope the following responses could address your concerns.\n\n**1. Wider bitrate range**\n\nWe have trained additional OneDC models spanning a wider bitrate range (0.0034–0.1115) and compare them with PerCo (SD) on full-resolution Kodak images:\n\n|Bpp(OneDC)|LPIPS(OneDC)↓|PSNR(OneDC)↑|Bpp(PerCo SD)|LPIPS(PerCo SD)↓|PSNR(PerCo SD)↑|\n|-|-|-|-|-|-|\n| 0.0034 | 0.380 | 17.141 | 0.0031 | 0.529 | 15.662 |\n| 0.0101 | 0.220 | 20.631 | | | |\n| 0.0165 | 0.183 | 21.513 | | | |\n| 0.0245 | 0.154 | 22.220 | 0.0324 | 0.299 | 19.344 |\n| 0.0354 | 0.133 | 22.979 | | | |\n| 0.0506 | 0.114 | 23.620 | | | |\n| 0.0775 | 0.096 | 24.396 | | | |\n| 0.1115 | 0.083 | 25.248 | 0.1261 | 0.141 | 22.967 |\n\nThese results confirm that OneDC maintains strong performance across a wide bitrate range, including both extremely low and moderately high bitrate settings.\nWe will include results on extended bitrate range in the final version.\n\n\n**2. Comparison with SOTA DDCM**\n\nTo ensure a fair *comparison with DDCM* and enable *FID calculation on Kodak dataset*, we follow DDCM's evaluation protocol: center-cropping and resizing images to certain resolution. The test datasets include Kodak (512x512) and CLIC2020 (512x512,768x768). All DDCM data numbers are obtained from their paper.\n\n- The table below shows the absolute numbers on the Kodak (512x512 resolution, FID calculated on 64x64 patches).\n\n    | Bpp(OneDC) | FID(OneDC)↓| LPIPS(OneDC)↓| PSNR(OneDC)↑| Bpp (DDCM) | FID(DDCM)↓| LPIPS(DDCM)↓| PSNR(DDCM)↑|\n    |-|-|-|-|-|-|-|-|\n    | 0.0034| 54.292 | 0.394 | 16.756 | | | | |\n    | 0.0109| 36.862 | 0.223 | 20.259 | | | | |\n    | 0.0177| 33.075 | 0.183 | 21.219 | | | | |\n    | 0.0257| 30.947 | 0.154 | 21.970 | 0.030| 32.031 | 0.222 | 22.066 |\n    | 0.0369| 29.103 | 0.132 | 22.718 | 0.038| 29.117 | 0.190 | 22.551 |\n    | 0.0524| 27.428 | 0.113 | 23.363 | 0.050| 25.647 | 0.161 | 23.013 |\n    | 0.0795| 25.452 | 0.095 | 24.202 | 0.095| 24.215 | 0.138 | 23.606 |\n    | 0.1139| 23.448 | 0.082 | 25.021 | 0.149| 23.199 | 0.124 | 24.069 |\n\n    The absolute numbers on CLIC2020 are omitted here due to rebuttal character constraint.\n\n- For more comprehensive comparison, we also calculate the relative BD-Rate with PerCo as an anchor:\n\n    - Kodak (512x512 resolution, FID calculated on 64x64 patches):\n\n        |       |BD-Rate (FID)↓|BD-Rate (LPIPS)↓|BD-Rate (PSNR)↓|\n        |-|-|-|-|\n        | PerCo | 0.00%   | 0.00%   | 0.00%   |\n        | DDCM  | -60.18% | -57.32% | -73.76% |\n        | OneDC | -65.30% | -81.92% | -77.13% |\n\n    - CLIC2020 (512x512 resolution, FID calculated on 128x128 patches):\n\n        |       |BD-Rate (FID)↓|BD-Rate (LPIPS)↓|BD-Rate (PSNR)↓|\n        |-|-|-|-|\n        | PerCo | 0.00%   | 0.00%   | 0.00%   |\n        | DDCM  | -65.97% | -61.58% | -77.37% |\n        | OneDC | -69.94% | -84.51% | -86.11% |\n\n    - CLIC2020 (768x768 resolution, FID calculated on 128x128 patches):\n\n        |       |BD-Rate (FID)↓|BD-Rate (LPIPS)↓|BD-Rate (PSNR)↓|\n        |-|-|-|-|\n        | PerCo | 0.00%   | 0.00%   | 0.00%   |\n        | DDCM  | -52.20% | -53.04% | -86.23% |\n        | OneDC | -93.37% | -85.98% | -89.16% |\n\nWe will add all absolute numbers and the relative BD-rate comparisons in the revision.\n\n\n**3. LoRA parameter count**\n\nWe appreciate the detailed observation of the reviewer and would like to clarify a misunderstanding. The base U-Net from SD1.5 indeed contains ~860M parameters. Our LoRA tuning (rank 64) adds only ~68M trainable parameters, bringing the total to ~928M during training. However, since LoRA can be merged into the base model at inference, the final U-Net can be ~860M.\nWe will revise the paper to clarify this point.\n\n\n**4. Training time**\n\nStage I is trained for 6 days, and Stage II for 12 days using 4×A100 GPUs, since we use high-resolution patches for training.\n\n\n**5. Comparison on model size && memory usage**\n\nWe compare OneDC with PerCo and DDCM on the Kodak dataset under DDCM’s test setting (center crop and resize to 512×512, FID on 64×64 patches). The memory usage of PerCo (SD) and DDCM is calculated with their open-sourced code.\n\n|Method|Params|BD-Rate(FID)↓|BD-Rate(LPIPS)↓|BD-Rate(PSNR)↓|Dec Time steps|Memory|\n|-|-|-|-|-|-|-|\n| PerCo (SD) |3.8B + 340M + 955M\\*| 0.00% | 0.00% | 0.00% | 20 | 22220 MB |\n|   DDCM     |    950M    |  -60.18% | -57.32% | -73.76% |1000| 4186 MB  |\n|   OneDC    |    1.4B    | -65.30% | -81.92% | -77.13% | 1 | 8038 MB  |\n\n\\* Open-sourced PerCo (SD) includes additional 3.8B BLIP2 caption model and 340M CLIP text embedding model.\n\nWe will include the model size comparison and memory usage into our final version. \n\n\n**6. On training a separate model per bitrate**\n\nWe appreciate the reviewer’s comment. This paper focuses on establishing an effective one-step diffusion framework for compression, so we currently use fixed-rate models for clarity and controlled analysis, like many other generative codecs [1][2][4][5]. We believe that OneDC is compatible with existing techniques for variable bitrate (e.g., bottleneck scaling[7] and adjustable quantization[8]), which we leave as future work.\n\n\n**7. Hyperprior Usage**\n\nThe hyperprior is introduced by Ballé et al. [6] and widely adopted in methods such as HiFiC [1], MS-ILLM [2], GLC [3], and DiffEIC [4]. In OneDC, we extend its role to also provide semantic guidance for generative decoding. We will add a brief background and proper citation in the final version to improve clarity.\n\n\n**8. On the modification of Figure 3**\n\nThanks for your great suggestion. We will separate $g_s$ into its own module and update Figure 3 accordingly in the final version.\n\n\n**9. About PerCo (SD)**\n\nWe will clarify the details regarding the use of PerCo (SD) in the revised main paper.\n\n\n**10. On the rate–perception–distortion tradeoff**\n\nWe fully agree reviewer's opinion. We will cite the rate–perception–distortion tradeoff when discussing the limitations of traditional codecs at low bitrates.\n\n\n**11. On missing PSNR in the main text**\n\nWe will include PSNR results in the revised main paper to enable a fair comparison with pixel-domain methods like MS-ILLM does.\n\n\n**12. On missing citations**\n\nWe will add the appropriate citations in the revision.\n\n\n**13. Regarding the results of fine-tuned MS-ILLM and PerCo**\n\n- MS-ILLM:\n\n    Our paper focuses on the 0.015–0.05 bpp range on the Kodak dataset, while official MS-ILLM vlo1/vlo2 checkpoints target <0.01 bpp. To ensure a proper comparison under similar bitrate range, we fine-tuned MS-ILLM using the official code to produce models operating between 0.015–0.045 bpp. These fine-tuned models outperform the original vlo1/vlo2 checkpoints due to their higher bitrate. For bitrates above 0.045 bpp, we use the official MS-ILLM checkpoints.\n\n    We will include BD-Rate comparisons against all official MS-ILLM checkpoints (vlo1/vlo2 and quality levels 1–4) in the revision, using OneDC extended to a broader bitrate range.\n\n- PerCo (SD):\n\n    Our results align with the PerCo (SD) technical report, which notes slightly degraded performance to the original PerCo (not publicly released) on the Kodak and MS-COCO 30K datasets. For results on CLIC2020 (full resolution), PerCo performs worse due to limited resolution adaptability.\n\n    We will clarify the use of PerCo (SD) in the main paper and add the comparisons to the original PerCo using data extracted from their paper.\n\n\n**14. On focusing on the low bitrate range**\n\nWe focus on the extremely low bitrate range because existing generative codecs often fail to preserve acceptable visual quality in this setting. It poses challenges in maintaining semantic content and synthesizing fine details, making it a key research frontier as explored in works like DiffEIC, PerCo, and GLC. Our aim is to advance compression capabilities specifically under these challenging conditions. To broaden applicability, we have also extended OneDC to cover higher bitrate range, and will include these results in the final version.\n\n\n**15. On adapting the model to additional bitrates**\n   \nYes, we fine-tune the extra bitrate points from the existing model, which converges faster than training from scratch.\n\n\n**16. On claims**\n\nThanks for your reminders. In the revision, we will carefully modify these claims according to reviewer's comments. Specifically, we will:\n\n- Revise our expression about bitrate reduction and the text usage of existing methods.\n- Clarify the meaning of each number in tables and supplement concrete numbers.\n\n\nCitations:\n\n[1] High-fidelity generative image compression, NeurIPS 2020\n\n[2] Improving statistical fidelity for neural image compression with implicit local likelihood models, ICML 2023\n\n[3] Generative latent coding for ultra-low bitrate image compression, CVPR 2024\n\n[4] Towards extreme image compression with latent feature guidance and diffusion prior, TCSVT 2024\n\n[5] Towards image compression with perfect realism at ultra-low bitrates, ICLR 2024\n\n[6] Variational image compression with a scale hyperprior, ICLR 2018\n\n[7] Asymmetric gained deep image compression with continuous rate adaptation, CVPR 2021\n\n[8] EVC: Towards Real-Time Neural Image Compression with Mask Decay, ICLR 2023"}]

Author rebuttal:

Meta-review: This paper introduces OneDC, a novel framework for one-step diffusion-based image compression that leverages a hyperprior for semantic guidance. The approach is elegant and highly effective, demonstrating state-of-the-art perceptual quality and a dramatic 20x decoding speedup. The authors engaged commendably during the rebuttal period, providing substantial new experiments that successfully addressed the major initial concerns, including the limited bitrate range and the lack of direct SOTA comparisons.

However, despite these strengths, the initial reviews raised several critical questions that could have undermined the work, centering on the evaluation protocol, model complexity, and comparisons against the latest SOTA methods. The authors' comprehensive rebuttal proved decisive, providing extensive new quantitative results, clarifying all ambiguities around the model's parameters and training, and ultimately resolving every key concern raised by the review panel.

This successful rebuttal was reflected in the final reviewer stances; it not only satisfied but successfully swayed reviewers who were initially borderline, leading them to raise their scores and forge a strong, unanimous consensus. Therefore, because the work presents a significant contribution and the authors have thoroughly resolved all critical points raised during the review process, my recommendation is to Accept.
Decision: Accept (poster)