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ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Yuxi Ren, Jie Wu, Yanzuo Lu, Huafeng Kuang, Xin Xia, Xionghui Wang, Qianqian Wang, Yixing Zhu, Pan Xie, Shiyin Wang, Xuefeng Xiao, Yitong Wang, Min Zheng, Lean Fu
TL;DR
Generative image editing faces challenges in quality, consistency, instruction adherence, and efficiency. ByteEdit combines aesthetic, alignment, and pixel-level coherence rewards with adversarial and progressive feedback learning. User evaluations report that it surpasses leading editing products across several performance dimensions.
Problem
Diffusion-based image editing still faces shortcomings in quality, consistency with the original image, instruction adherence, and generation efficiency.
Source: §1 · pp. 2–3Method
ByteEdit combines aesthetic, image-text alignment, and coherent reward models with perceptual, adversarial, and progressive feedback learning.
Source: §1 · p. 3Results
User studies report ByteEdit's advantages over competitive products in quality, consistency, efficiency, and speed, while the conclusion says it surpasses leading editing products.
Source: §1 · p. 4, §6 · p. 15Takeaways & Limitations
The paper presents feedback learning as a framework for improving generation quality, consistency, and inference speed in diffusion-based image editing.
Source: §6 · p. 15The discussion identifies performance as an area for further improvement.
Source: §5 · p. 14
Abstract
from arXiv · showhide
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ByteEdit, an innovative feedback learning framework meticulously designed to Boost, Comply, and Accelerate Generative Image Editing tasks. ByteEdit seamlessly integrates image reward models dedicated to enhancing aesthetics and image-text alignment, while also introducing a dense, pixel-level reward model tailored to foster coherence in the output. Furthermore, we propose a pioneering adversarial and progressive feedback learning strategy to expedite the model's inference speed. Through extensive large-scale user evaluations, we demonstrate that ByteEdit surpasses leading generative image editing products, including Adobe, Canva, and MeiTu, in both generation quality and consistency. ByteEdit-Outpainting exhibits a remarkable enhancement of 388% and 135% in quality and consistency, respectively, when compared to the baseline model. Experiments also verfied that our acceleration models maintains excellent performance results in terms of quality and consistency.
1 Introduction
Generative image editing still faces quality, instruction-adherence, consistency, and efficiency challenges, while prior approaches largely address individual problems. ByteEdit combines feedback-based reward models with adversarial and progressive learning, and user studies report advantages over competitive products.
1 Introduction
- Diffusion-based image editing faces shortcomings in realism, aesthetics, detail fidelity, instruction alignment, and coherence between generated and original regions.The paper also identifies sluggish generation as an efficiency challenge for large-scale editing.Source: §1 · pp. 2–3
- Existing methods address challenges such as detail capture, mask control, background preservation, or output diversity, but the authors argue they do not provide a comprehensive solution.The paper asks whether human feedback can guide generative image editing toward superior generation outcomes.Source: §1 · p. 3
- ByteEdit combines aesthetic, alignment, and coherent reward models with perceptual feedback learning, adversarial feedback, and progressively reduced optimization steps.The authors present the framework as addressing generation effects, instruction adherence, consistency, and inference speed.Source: §1 · p. 3
- User studies report ByteEdit's advantages in quality, consistency, efficiency, and speed over competitive products, and describe it as the fastest and most superior image-editing solution available.The paper also reports that acceleration maintains a minimal compromise on output quality.Source: §1 · p. 4
2 Related Work
Prior work addresses image editing through diffusion, masks, and text-image controls, while human-feedback learning has been explored in text-to-image generation. ByteEdit extends feedback learning with separate rewards for editing aesthetics, alignment, and pixel-level coherence.
Generative Image Editing
- Image-editing methods include text-to-image diffusion, visible-pixel conditioning, multimodal similarity, segmentation-based masks, and text-driven object removal.The paper also compares ByteEdit with industry systems Adobe, Canva, and MeiTu.Source: §2 · p. 4
Human Feedback Learning
- Prior human-feedback work uses preference data and reward models for text-to-image generation, while ByteEdit adds separate rewards for aesthetics, image-text alignment, and pixel-level coherence in editing.The paper notes that ImageReward [39] focused primarily on overall image quality and overlooked the complexity of human perception.Source: §2 · pp. 4–5
3 ByteEdit: Boost, Comply and Accelerate
ByteEdit supports outpainting and inpainting by preserving unmasked image regions while generating content aligned with prompts and visual attributes. Its framework combines perceptual feedback, alignment and pixel-level coherence rewards, and adversarial and progressive training.
3 ByteEdit: Boost, Comply and Accelerate
- ByteEdit extends images beyond their boundaries or fills and erases regions, aiming to preserve unmasked content while matching prompts and image attributes.The framework is organized around Boost, Comply, and Accelerate components.Source: §3 · p. 5
3.1 Boost: Perceptual Feedback Learning
- PeFL fine-tunes image editing with aesthetic feedback, pixel-level and VGG perceptual regularization, and a stagewise procedure that skips heavy-noise steps and evaluates intermediate denoising outputs.The aesthetic reward model is trained on preferred and non-preferred image pairs, using BLIP features, cross-attention, and an MLP score.Source: §3.1 · pp. 6–7
3.2 Comply: Image-Text Alignment with Coherence
- The alignment reward model learns preferences between better- and worse-matched captions, while a ViT-based coherent reward model scores image pixels for authenticity.The alignment training uses image-caption triplets; pixel-level discrimination targets inconsistencies such as color, style, and texture differences.Source: §3.2 · p. 8
3.3 Accelerate: Adversarial and Progressive Training
- The coherent reward model is trained online as a discriminator, and progressive training reduces optimization steps from T = 20, T1 = 15, T2 = 10 to T = 8, T1 = 6, T2 = 3 without distillation.The authors report no significant quality degradation under reward-model supervision and describe a balance between performance and speed.Source: §3.3 · pp. 8–9
4 Experiments
The experiments assess ByteEdit through expert, volunteer, and objective evaluations across image-editing tasks, then compare it with other systems and test its feedback-learning and acceleration components.
4.1 Implementation Details
- Fine-tuning uses 7,562,283 images and four mask types, with instance-level mask dilation for inpainting-editing; evaluation includes 100 UserBench pairs and 240 EditBench images.PeFL training uses a 2e-06 learning rate and 0.9999 EMA decay.Source: Dataset · pp. 9–10, Training Setting · p. 10
4.2 Evaluation Principles and Criteria
- Experts rate coherence, structure, and aesthetics from 1 to 5; volunteers choose Good, Same, or Bad, and CLIPScore and BLIPScore measure text-image alignment.More than 6,000 image-text pairs are sampled per task for expert averages; Fig. 3 collects more than 12,000 samples per task.Source: §4.2 · p. 10, Table 1 · p. 11, Figure 3 · p. 11
4.3 Comparisons with State of the arts
- ByteEdit significantly outperforms state-of-the-art systems across outpainting metrics and achieves 105%, 163%, and 112% GSB superiority across the three tasks.Users generally found ByteEdit’s images more natural than Adobe’s.Source: Table 1 · p. 11, Figure 3 · p. 11, §4.3 · p. 11
- On UserBench inpainting-editing, ByteEdit leads second-place Canva by 5.8%(+0.014) in CLIPScore and 47.1%(+0.22) in BLIPScore.On EditBench, ByteEdit is reported to achieve state-of-the-art performance against DiffEdit, BLD, and EMILIE.Source: Table 2 · p. 12, §4.3 · p. 12
- Qualitative samples show coherent, instruction-following inpainting with better image-text alignment, while outpainting supports both prompt-driven and prompt-free generation.The comparisons highlight key regions in the inpainting and outpainting examples.Source: Figure 4 · p. 12, §4.3 · p. 12, Figure 5 · p. 13
4.4 Ablation Studies
- PeFL improves generation quality over baseline on all tasks, exceeding it by about 60% in outpainting structure and aesthetics.The authors describe the corresponding output as more realistic and consistent with realistic physics.Source: Figure 6 · p. 13, §4.4 · p. 13, Figure 7 · p. 14
- Acceleration shows no significant loss in consistency, structure, or aesthetics, while increasing speed and quality in outpainting and inpainting-editing.The authors attribute this to trainable discriminator rewards stabilizing PeFL and adversarial supervision extending it to high-noise step 999.Source: Figure 6 · p. 13, §4.4 · p. 14
5 Discussion
The discussion identifies task-specific reward models, faster inference, and expansion beyond image editing as directions for further exploration. It also connects human feedback with real-world practicality and usability.
5 Discussion
- Task-specific reward models are proposed as a route to potentially higher performance and more precise, desirable outputs.The discussion identifies reward refinement as an avenue for improvement.Source: §5 · p. 14
- Integrating ByteEdit with LCM and SDXL-Turbo is proposed for further acceleration.The discussion presents this as an area for investigation.Source: §5 · p. 14
- Video editing and instruction editing are identified as potential extensions beyond image editing.Source: §5 · p. 14
- The authors connect human feedback with greater practicality and usability in real-world scenarios.Source: §5 · p. 14
6 Conclusion
ByteEdit is a feedback-learning framework designed to enhance quality, consistency, and inference speed in diffusion-based generative image editing. User evaluations report that it surpasses leading editing products, and the authors position it as state of the art.
6 Conclusion
- User evaluations show ByteEdit surpassing leading generative image editing products.Source: §6 · p. 15
- ByteEdit is designed to enhance generation quality, consistency, and inference speed in diffusion-based image editing.Source: §6 · p. 15
- The authors position ByteEdit as a state-of-the-art tool in generative image editing.Source: §6 · p. 15