Title: BADiff: Bandwidth Adaptive Diffusion Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Bandwidth-aware generation

diffusion models

image compression
Abstract: In this work, we propose a novel framework to enable diffusion models to adapt their generation quality based on real-time network bandwidth constraints. Traditional diffusion models produce high-fidelity images by performing a fixed number of denoising steps, regardless of downstream transmission limitations. However, in practical cloud-to-device scenarios, limited bandwidth often necessitates heavy compression, leading to loss of fine textures and wasted computation. To address this, we introduce a joint end-to-end training strategy where the diffusion model is conditioned on a target quality level derived from the available bandwidth. During training, the model learns to adaptively modulate the denoising process, enabling early-stop sampling that maintains perceptual quality appropriate to the target transmission condition. Our method requires minimal architectural changes and leverages a lightweight quality embedding to guide the denoising trajectory. Experimental results demonstrate that our approach significantly improves the visual fidelity of bandwidth-adapted generations compared to naive early-stopping, offering a promising solution for efficient image delivery in bandwidth-constrained environments. Code is available at: https://github.com/xzhang9308/BADiff.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework that enables diffusion models to adapt output quality based on bandwidth constraints. The approach involves joint end-to-end training, where the diffusion model is conditioned on the desired quality level.\n\nSTRENGTHS AND WEAKNESSES:\nOverall, the architecture in the paper has some disconnected components where their relations to each other are unclear. The loss functions in the paper are treated carelessly and there are several over-simplifications in the main algorithms of the paper. The detailed comments are given in the following:\n\n\n1) A significant issue with the proposed architecture is the disconnection between its components. For instance, Section 3.3 introduces a loss based on entropy, while Section 3.4 defines a policy network. However, the relationship between the policy network and the earlier loss function is not discussed. Later, Section 3.5 introduces yet another loss function comprising four terms (Eq. 16), which appears unstructured and ad hoc. It is unclear what the training is intended to achieve. Also, training and inference phases are somehow mixed up with each other.\n\n2) While the overall framework is complex, the implementation appears overly simplified in some aspects. For example, Algorithm 1 includes four loss terms, and one would expect the model to be trained by jointly optimizing over all these terms. However, the process seems sequential—first computing L_{DEN}, then L_{stop}—which may be an oversimplification of a potentially interdependent optimization.\n\n3) The background on diffusion models in Sections 3.1 and 3.2 is too long and should be significantly shortened, as it takes considerable space (approximately one page) without contributing proportionally to the core contributions.\n\n4) The definition of image entropy is unclear. In particular, Eq. (8) appears to rely on a definition of entropy that is only formally introduced later in Eq. (10), which is very confusing.\n\n5) The justification for measuring compute based on the stopping point in Eq. (12) is unclear. Compute is typically quantified in terms of FLOPs or related metrics, and this alternative interpretation does not have a proper justification.\n\nQUESTIONS:\nCan the authors clearly separate and describe the training and inference phases, including the associated loss functions and evaluation protocols for each?\n\nLIMITATIONS:\nThe core ideas of the paper are not clearly articulated. Several loss functions lack adequate justification and may reflect flawed design decisions.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a bandwidth-aware framework for diffusion models that adapts image generation quality based on bandwidth constraints. \nUnlike standard diffusion models with fixed denoising steps, the proposed method uses a quality embedding to guide adaptive early-stopping during sampling. Trained end-to-end, the model maintains perceptual quality while reducing computation. Experiments show improved visual fidelity over naive early-stopping, offering an effective solution for image generation in bandwidth-limited settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The method introduces a lightweight policy network that predicts when to terminate sampling based on a trade-off between bitrate, perceptual distortion, and compute cost. This enables runtime-efficient sampling aligned with bandwidth targets.\n\n- By conditioning the model on target bitrate levels, BADiff enables flexible image generation across a wide range of bandwidth budgets, without requiring separate models.\n\n- The framework is lightweight, making it broadly compatible with standard diffusion models and sampling schemes.\n\nWeaknesses:\n\n- The framework assumes a fixed bandwidth constraint during sampling. In practical deployment, bandwidth may vary dynamically, and the current setup does not support adaptation mid-generation.\n\n- The stopping policy is trained on trajectories influenced by the bitrate target (via entropy conditioning), potentially limiting its ability to learn a robust, generalizable stopping criterion independent of specific training paths.\n\n- Although entropy prediction is spatially resolved, the policy makes a global stop decision, which may fail to account for spatial variation in content complexity. Region-wise stopping could offer finer control and better resource utilization.\n\nQUESTIONS:\n1) Have the authors considered modeling dynamic bandwidth conditions during sampling? For instance, can BADiff respond to bitrate shifts mid-generation?\n\n2) How well does BADiff generalize when only the policy and entropy heads are trained atop a frozen pretrained diffusion model, without retraining the full UNet?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces BADiff, a Bandwidth-Adaptive Diffusion model designed to generate high-quality images that respect explicit entropy (bitrate) constraints, addressing the inefficiencies and quality degradation of conventional \"generate-then-compress\" pipelines used in cloud-to-user image transmission. Instead of relying on post-hoc lossy compression, BADiff directly integrates a target entropy condition into the generation process, allowing the diffusion model to dynamically modulate the level of detail in accordance with bandwidth limitations. Specifically, authors propose (1) entropy conditioning in which each reverse denoising step is conditioned on a continuous entropy embedding derived from the target bandwidth. (2) entropy regularization loss based on a differentiable entropy estimator that ensures outputs conform to the desired bitrate. (3) a lightweight policy network learns when to stop the denoising process, balancing perceptual quality, bitrate adherence, and computational cost.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well-written.  \n2. The idea of integrating entropy coding, inspired by learned image compression, into adaptive bandwidth-aware diffusion is interesting.  \n3. BADiff eliminates the need for expensive post-generation compression, reducing redundancy and preserving visual fidelity.  \n4. BADiff achieves up to 2× speed-up in inference under low-bandwidth settings via early stopping, while maintaining image quality.\n\nWeaknesses:\n\n1. In Figure 1, the authors refer to the method as \"lossless compression.\" Is this truly lossless, meaning it produces an output identical to the original image? If not, this term may be misleading and should be reconsidered.  \n2. Since the entropy coding used in this work is similar to that in learned image compression (as noted in line 172), relevant prior work should be included in Section 2.  \n3. Early stopping is only one approach to adaptive acceleration under varying bandwidth. Could other model-efficient techniques, such as pruning or quantization, also be applied to this setting?  \n4. Section 3.1 (Background on Diffusion Models) is largely standard and may be unnecessary. It could either be removed or significantly condensed.  \n5. In Equation 7, conditioning is implemented by adding the entropy embedding to the timestep embedding. Have the authors considered other conditioning strategies, such as concatenation? Which performs better in practice?  \n6. In Section 3.5 and Equation 16, three hyperparameters are introduced for training. How sensitive is the model to these values? Tuning them seems time-consuming. Are there practical guidelines or automated methods for finding optimal settings?  \n7. The experiments do not include ImageNet, which is a widely used benchmark in generative modeling. Including it would strengthen the empirical validation.  \n8. Since text-to-image models are more widely adopted in real-world applications, it would be beneficial to include experiments on text-conditioned diffusion models to demonstrate BADiff’s effectiveness in this setting.\n\nQUESTIONS:\nWeaness 6, 7, 8. It's better to be verified in the large-scale model, datasets, and text-to-image diffusion models.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBADiff  is a novel framework designed to make diffusion models aware of network bandwidth constraints during the image generation process. The core idea is to condition the diffusion model's denoising process on a target entropy level, which is derived from the available bandwidth. This allows the model to generate images of a quality appropriate for the transmission channel, thereby avoiding the computational waste and quality degradation associated with the standard practice of generating a high-quality image and then aggressively compressing it.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths:\n\n- It's quite interesting and practical to perform research on the mismatch between high-fidelity generation and bandwidth-limited transmission. The proposed solution of integrating bandwidth awareness directly into the generative process is inspiring. The problem formulation is clear, and the potential impact on cloud-based AI services is substantial. \n- The methodology is grounded in theoretical underpinnings, with explicit integration of entropy conditioning into the reverse diffusion process and a joint loss formulation that enforces adherence to bandwidth constraints through a differentiable entropy estimator and regularizer.\n- Authors use two different backbones (DDPM and LDM) to demonstrate the general applicability of the method. The ablation study effectively validates the contribution of each component of the proposed framework (conditioning, hinge loss, calibration loss). \n\n## Weaknesses:\n\n- The experiments are conducted on relatively small datasets or resolutions.  While the authors acknowledge this limitation,  it is not guaranteed that the method will scale effectively, as generating fine details in high-resolution images might be difficult to control with a single scalar entropy target.\n- In line 209, the \"teacher\" labels are generated by running a full, long-step sampler and finding the optimal stopping point offline based on a cost function.  This seems to imply that for every training sample, a full generation is required to create the label, which would cost lots of computation. Are these labels pre-computed for a fixed set of images or generated in real-time in some efficient manner?\n- Besides the quantitative metrics such as FID, LPIPS, and runtime, can authors provide some user studies or task-specific downstream evaluation about the application potential of BADiff?\n\nQUESTIONS:\n1. Could the authors provide insight or preliminary results on BADiff's relative performance when the baseline is not a 1000-step DDPM/200-step LDM, but a fast DPM-solver running in, for instance, 20 steps?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}