Title: One-Step Diffusion-Based Image Compression with Semantic Distillation
Venue: NeurIPS 2025
Accepted: yes
Keywords: image compression

generative compression

diffusion models

one-step diffusion

extremely low bitrate
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/


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nNo.\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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n[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.\n\nQUESTIONS:\nOne 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nOne 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths
- 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. 
- The combination of the chosen components allow for great tradeoffs in speed and performance.
- Experiments are fairly extensive, with ablation studies showing the benefit of each component.


Weaknesses:
- a discussion on training time would be helpful
- 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.

QUESTIONS:
See weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 2
DATE: 2025-07-03