InstantRetouch: Efficient and High-Fidelity Instruction-Guided Image Retouching with Bilateral Space

• URL: http://arxiv.org/abs/2606.05071v1
• Date: Wed, 03 Jun 2026 16:30:17 GMT
• Title: InstantRetouch: Efficient and High-Fidelity Instruction-Guided Image Retouching with Bilateral Space
• Abstract summary: We propose an efficient and fidelity-preserving retouching method using bilateral space manipulation.<n>Instead of directly editing pixels or image latents, our model predicts a low-resolution bilateral grid of affine transforms.<n>This approach yields both high fidelity and improved efficiency.
• Score: 28.786282485917678
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Language-guided photo retouching aims to adjust color and tone while preserving geometry and texture. Recently, diffusion-based retouching shows a superior visual quality, but often struggles with both fidelity issues due to its generative nature and efficiency because of its iterative sampling process. In this work, we propose an efficient and fidelity-preserving retouching method using bilateral space manipulation, which is both compact and content-decoupled. Specifically, instead of directly editing pixels or image latents, our model predicts a low-resolution bilateral grid of affine transforms, which are sliced using a learned guidance map and then applied to the full-resolution image. This approach yields both high fidelity and improved efficiency. To retain strong priors of a pretrained generative model, we distill a multi-step diffusion model into our bilateral grid framework using Variational Score Distillation, complemented by a prompt alignment loss to guide instruction-following behavior. Additionally, we introduce a new benchmark and evaluate our method across multiple dimensions: fidelity, instruction following, and efficiency. Compared to the latest retouch methods, like Gemini-2.5-Flash (Nano-Banana), our method can avoid content drift, significantly improve latency, and generate visually pleasing edits, while maintaining a high level of fidelity. Project page: https://openimaginglab.github.io/InstantRetouch/.

Related papers

• CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds [52.84219088497834]
  CopyCat is a lightweight model-refinement framework that improves fine-grained subject consistency within only a few seconds.<n>Experiments on DreamBench and XVerseBench demonstrate consistent improvements in fine-grained subject consistency across representative subject-to-image models.
  arXiv  Detail & Related papers  (2026-08-01T13:49:03Z)
• Efficient and Training-Free Single-Image Diffusion Models [17.578119446864132]
  We consider the problem of generating images whose internal structure matches that of a single reference image.<n>We integrate a patch-based denoiser into an efficient, training-free image diffusion model.<n>Our approach achieves state-of-the-art generation quality and diversity compared to trained single-image diffusion models.
  arXiv  Detail & Related papers  (2026-06-03T00:05:36Z)
• Single-Reference Text-to-Image Manipulation with Dual Contrastive Denoising Score [4.8677910801584385]
  Large-scale text-to-image generative models have shown remarkable ability to synthesize diverse and high-quality images.<n>We present Dual Contrastive Denoising Score, a framework that leverages the rich generative prior of text-to-image diffusion models.<n>Our method achieves both flexible content modification and structure preservation between input and output images, as well as zero-shot image-to-image translation.
  arXiv  Detail & Related papers  (2025-08-18T08:30:07Z)
• Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency Models [1.9389881806157316]
  In this work, we propose a novel framework that enhances image inversion using consistency models.<n>Our method introduces a cycle-consistency optimization strategy that significantly improves reconstruction accuracy.<n>We achieve state-of-the-art performance across various image editing tasks and datasets.
  arXiv  Detail & Related papers  (2025-06-23T20:34:43Z)
• LUSD: Localized Update Score Distillation for Text-Guided Image Editing [11.293199854940772]
  Recent works have introduced score distillation techniques that leverage the rich generative prior of text-to-image diffusion models.<n>We propose two simple yet effective modifications: attention-based spatial regularization and gradient filtering-normalization.<n> Experimental results show our method outperforms state-of-the-art score distillation techniques in prompt fidelity, improving successful edits while preserving the background.
  arXiv  Detail & Related papers  (2025-03-14T03:45:29Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing [66.48853049746123]
  We analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps.<n>Our method effectively minimizes distortions caused by varying text conditions during noise prediction.<n> Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios.
  arXiv  Detail & Related papers  (2024-11-29T12:11:28Z)
• ZePo: Zero-Shot Portrait Stylization with Faster Sampling [61.14140480095604]
  This paper presents an inversion-free portrait stylization framework based on diffusion models that accomplishes content and style feature fusion in merely four sampling steps. We propose a feature merging strategy to amalgamate redundant features in Consistency Features, thereby reducing the computational load of attention control.
  arXiv  Detail & Related papers  (2024-08-10T08:53:41Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models [67.68871360210208]
  Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency.<n>We propose a novel fine-tuning objective, dubbed Direct Consistency Optimization, which controls the deviation between fine-tuning and pretrained models.<n>We show that our approach achieves better prompt fidelity and subject fidelity than those post-optimized for merging regular fine-tuned models.
  arXiv  Detail & Related papers  (2024-02-19T09:52:41Z)
• Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis [15.76266032768078]
  This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries.<n>We first introduce vision guidance as a foundational spatial cue within the perturbed distribution.<n>We propose a universal framework, Layered Rendering Diffusion (LRDiff), which constructs an image-rendering process with multiple layers.
  arXiv  Detail & Related papers  (2023-11-30T10:36:19Z)
• Robust GAN inversion [5.1359892878090845]
  We propose an approach which works in native latent space $W$ and tunes the generator network to restore missing image details. We demonstrate the effectiveness of our approach on two complex datasets: Flickr-Faces-HQ and LSUN Church.
  arXiv  Detail & Related papers  (2023-08-31T07:47:11Z)
• DriftRec: Adapting diffusion models to blind JPEG restoration [16.596100244509575]
  We utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We show that our approach can escape the tendency of other methods to generate blurry images, and recovers the distribution of clean images significantly more faithfully.
  arXiv  Detail & Related papers  (2022-11-12T22:29:42Z)
• Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization [59.19214040221055]
  We propose a novel spatial-separated curve rendering network (S$2$CRNet) for efficient and high-resolution image harmonization. The proposed method reduces more than 90% parameters compared with previous methods. Our method can work smoothly on higher resolution images in real-time which is more than 10$times$ faster than the existing methods.
  arXiv  Detail & Related papers  (2021-09-13T07:20:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.