LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair

• URL: http://arxiv.org/abs/2411.19156v3
• Date: Mon, 09 Dec 2024 02:14:55 GMT
• Title: LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair
• Authors: Xue Song, Jiequan Cui, Hanwang Zhang, Jiaxin Shi, Jingjing Chen, Chi Zhang, Yu-Gang Jiang,
• Abstract summary: We propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. We learn an instruction-specific LoRA to encode the "change" in a before-after image pair, enhancing the interpretability and reusability of our model. Our model produces high-quality images that align with user intent and support a broad spectrum of real-world visual instructions.
• Score: 116.48684498656871
• License:
• Abstract: In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the "change" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visual instructions typically require quad data, i.e., a before-after image pair, along with query and target images. Due to the scarcity of such quad data, existing models are limited to a narrow range of visual instructions. To overcome this limitation, we introduce the LoRA Reverse optimization technique, enabling large-scale training with paired data alone. Extensive qualitative and quantitative experiments demonstrate that our model produces high-quality images that align with user intent and support a broad spectrum of real-world visual instructions.

Related papers

• A LoRA is Worth a Thousand Pictures [28.928964530616593]
  Low Rank Adaptation (LoRA) can replicate an artist's style or subject using minimal data and computation. We show that LoRA weights alone can serve as an effective descriptor of style, without the need for additional image generation or knowledge of the original training set. We conclude with a discussion on potential future applications, such as zero-shot LoRA fine-tuning and model attribution.
  arXiv  Detail & Related papers  (2024-12-16T18:18:17Z)
• Compositional Image Retrieval via Instruction-Aware Contrastive Learning [40.54022628032561]
  Composed Image Retrieval (CIR) involves retrieving a target image based on a composed query of an image paired with text that specifies modifications or changes to the visual reference. In practice, due to the scarcity of annotated data in downstream tasks, Zero-Shot CIR (ZS-CIR) is desirable. We propose a novel embedding method utilizing an instruction-tuned Multimodal LLM (MLLM) to generate composed representation.
  arXiv  Detail & Related papers  (2024-12-07T22:46:52Z)
• Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations [45.800383191637785]
  We introduce LeafInstruct, the first open-sourced interleaved instruction tuning data with over 30,000 high-quality instances across more than 10 domains. We propose Lateralization LoRA, a novel modality-specialized adaptation method inspired by the concept of brain lateralization. We perform instruction tuning of the VLG (i.e., EMU2) using Lateralization LoRA on the LeafInstruct dataset.
  arXiv  Detail & Related papers  (2024-07-04T03:28:22Z)
• A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models [117.77807994397784]
  Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs.
  arXiv  Detail & Related papers  (2024-06-20T17:58:52Z)
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension [131.14381425260706]
  We introduce Self-Training on Image (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference for image descriptions using unlabeled images. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data.
  arXiv  Detail & Related papers  (2024-05-30T05:53:49Z)
• Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval [92.13664084464514]
  The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments. We propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning.
  arXiv  Detail & Related papers  (2024-03-03T07:58:03Z)
• Reference-based Image Composition with Sketch via Structure-aware Diffusion Model [38.1193912666578]
  We introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the edge-level controllability using sketches, our method enables a user to edit or complete an image sub-part. Our framework fine-tunes a pre-trained diffusion model to complete missing regions using the reference image while maintaining sketch guidance.
  arXiv  Detail & Related papers  (2023-03-31T06:12:58Z)
• InstructPix2Pix: Learning to Follow Image Editing Instructions [103.77092910685764]
  We propose a method for editing images from human instructions. given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. We show compelling editing results for a diverse collection of input images and written instructions.
  arXiv  Detail & Related papers  (2022-11-17T18:58:43Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)

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.