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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0fc9c7e9be4f0997eb3c283c639730ddCVF · CVPR 2026

SIMPLEPOSTER: A SIMPLE BASELINE FOR PRODUCT POSTER GENERATION

Cui, Benlei, Zeng, Fangao, Jiang, Weitao, Zhai, Yuwen, Hong, Haiwen, Huang, Longtao, Xue, Hui, Shang, Wenxiang 等 9 位作者

原文摘要

Product poster generation presents unique challenges beyond general-purpose de-sign: it demands not only aesthetic composition and accurate text rendering, butalso strict preservation of the product subject and precise control over dense,multi-line text layouts. While general image editing models struggle with text lay-out control and subject consistency, existing specialized approaches--often builtupon inpainting frameworks--still suffer from unintended subject extension andinaccurate text synthesis. A common solution involves integrating auxiliary mod-ules such as ControlNet to condition on subject structure and text layout, but theseapproaches introduce significant architectural complexity and training overhead.In this work, we challenge the necessity of such complexity and demonstrate thatminimalist adaptation is sufficient. We introduce SimplePoster, a minimalist yetpowerful inpainting-based framework that enables faithful subject preservationand position-controllable text rendering--entirely without external controllers likeControlNet. SimplePoster rests on two key insights: (1) full-parameter fine-tuningalone effectively suppresses subject extension by aligning the model's internalrepresentations with domain-specific priors; and (2) a lightweight character-levelposition encoding strategy enables end-to-end, spatially grounded text generation.Experiments show that SimplePoster achieves near-perfect subject preservation(98.7% of cases with strict subject preservation), significantly outperforming boththe state-of-the-art editing model SeedEdit3.0 (55.2%) and the specialized ap-proach PosterMaker (85.3%). It further demonstrates superior text rendering ac-curacy, even in challenging scenarios with complex multi-line layouts. We believeSimplePoster establishes a simple yet strong baseline for product poster genera-tion. We question the necessity of such complexity and demonstrate that minimalist designs suffice. We propose SimplePoster, a simple yet effective inpainting-based framework that achieves faithful subject preservation and position-controllable text rendering without relying on external controllers like ControlNet. SimplePoster is based on two key insights: (1) full-parameter fine-tuning effectively suppresses subject extension; and (2) a training-free character-level position encoding strategy enables end-to-end, geometry-aware text generation. Remarkably, SimplePoster achieves a near-perfect subject preservation rate (98.7%), significantly outperforming SOTA models SeedEdit 3.0 (55.2%) and PosterMaker (85.3%). It also excels in text rendering accuracy. We believe SimplePoster establishes a simple yet strong baseline for product poster generation. Code, models and benchmark will be released upon acceptance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:34aea3f269735ce04554607a5f36672bCVF · CVPR 2026

TC-Pade: Trajectory-Consistent Pade Approximation for Diffusion Acceleration

He, Shaoxuan, Cui, Benlei, Huang, Bukun, Ye, Zhizeng, Sun, Yunyun, Huang, Longtao, Xue, Hui, Yang, Yang 等 11 位作者

原文摘要

Despite achieving state-of-the-art generation quality, diffusion models are hindered by the substantial computational burden of their iterative sampling process. While feature caching techniques achieve effective acceleration at higher step counts (e.g., 50 steps), they exhibit critical limitations in the practical low-step regime of 20-30 steps. As the interval between steps increases, polynomial-based extrapolators like TaylorSeer suffer from error accumulation and trajectory drift. Meanwhile, conventional caching strategies often overlook the distinct dynamical properties of different denoising phases. To address these challenges, we propose Trajectory-Consistent Pade(TC-Pade) approximation, a feature prediction framework grounded in Pade approximation. By modeling feature evolution through rational functions, our approach captures asymptotic and transitional behaviors more accurately than Taylor-based methods. To enable stable and trajectory-consistent sampling under reduced step counts, TC-Pade incorporates (1) adaptive coefficient modulation that leverages historical cached residuals to detect subtle trajectory transitions, and (2) step-aware prediction strategies tailored to the distinct dynamics of early, mid, and late sampling stages. Extensive experiments on DiT-XL/2, FLUX.1-dev, and Wan2.1 across both image and video generation demonstrate the effectiveness of TC-Pade. For instance, TC-Pade achieves 2.88xacceleration on FLUX.1-dev and 1.72xon Wan2.1 while maintaining high quality across FID, CLIP, Aesthetic, and VBench-2.0 metrics, substantially outperforming existing feature caching methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9c8a8d7b85037b1f7d29652ed0e80a77CVF · CVPR 2026

Making Training-Free Diffusion Segmentors Scale with the Generative Power

Meng, Benyuan, Xu, Qianqian, Wang, Zitai, Cao, Xiaochun, Huang, Longtao, Huang, Qingming

原文摘要

As powerful generative models, text-to-image diffusion models have recently been explored for discriminative tasks. A line of research focuses on adapting a pre-trained diffusion model to semantic segmentation without any further training, leading to training-free diffusion segmentors. These methods typically rely on cross-attention maps from the model's attention layers, which are assumed to capture semantic relationships between image pixels and text tokens. Ideally, such approaches should benefit from more powerful diffusion models, i.e., stronger generative capability should lead to better segmentation. However, we observe that existing methods often fail to scale accordingly. To understand this issue, we identify two underlying gaps: (i) Cross-attention is computed across multiple heads and layers, but there exists a discrepancy between these individual attention maps and a unified global representation. (ii) Even when a global map is available, it does not directly translate to accurate semantic correlation for segmentation, due to score imbalances among different text tokens. To bridge these gaps, we propose two techniques: auto aggregation and per-pixel rescaling, which together enable training-free segmentation to better leverage generative capability. We evaluate our approach on standard semantic segmentation benchmarks and further integrate it into a generative technique, demonstrating both improved performance and broad applicability. Codes are at https://github.com/Darkbblue/goca.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a0f83273576ed70f45435f3b613d3aa3CVF · CVPR 2026

Diffusion Probe: Generated Image Result Prediction Using CNN Probes

Huang, Bukun, Cui, Benlei, Ye, Zhizeng, Dong, Xuemei, Chen, Tuo, Xue, Hui, Yang, Dingkang, Huang, Longtao 等 10 位作者

原文摘要

Text-to-image (T2I) diffusion models currently lack an efficient mechanism for early quality assessment, forcing costly random trial-and-error in scenarios requiring multiple generations (e.g., iterating on prompts, agent-based image generation, flow-grpo). To address this, we first reveal a strong correlation between the attention distribution in the early diffusion process and the final image quality. Building upon this insight, we introduce Diffusion Probe, a pioneering framework that leverages the model's internal cross-attention maps as a predictive signal. We propose a lightweight predictor, trained to establish a direct mapping from statistical properties of these nascent cross-attention distributions--extracted from the initial denoising steps--to the final image's comprehensive quality. This allows our probe to accurately forecast various aspects of image quality, regardless of the specific ground-truth quality metric, long before full synthesis is complete. We empirically validate the reliability and generalizability of Diffusion Probe through its consistently strong predictive accuracy across a wide spectrum of conditions. On diverse T2I models (e.g., SDXL, FLUX, Qwen-Image), throughout broad early-denoising windows, across various resolutions, and with different quality metrics, it achieves high correlation (PCC > 0.7) and classification performance (AUC-ROC > 0.9). This intrinsic reliability is further demonstrated in practice by successfully optimizing T2I workflows that benefit from early, quality-guided decisions, such as Prompt Optimization, Seed Selection, and Accelerated RL Training. In these applications, the probe's early signal enables more targeted sampling strategies, preempting costly computations on low-potential paths. This yields a dual benefit: a significant reduction in computational overhead and a simultaneous improvement in final outcome quality, establishing Diffusion Probe as a model-agnostic and broadly applicable tool poised to revolutionize T2I efficiency.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗