Object-level Visual Prompts for Compositional Image Generation

• URL: http://arxiv.org/abs/2501.01424v1
• Date: Thu, 02 Jan 2025 18:59:44 GMT
• Title: Object-level Visual Prompts for Compositional Image Generation
• Authors: Gaurav Parmar, Or Patashnik, Kuan-Chieh Wang, Daniil Ostashev, Srinivasa Narasimhan, Jun-Yan Zhu, Daniel Cohen-Or, Kfir Aberman,
• Abstract summary: We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. A key challenge in this task is to preserve the identity of the objects depicted in the input visual prompts. We introduce a new KV-mixed cross-attention mechanism, in which keys and values are learned from distinct visual representations.
• Score: 75.6085388740087
• License:
• Abstract: We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent compositions across diverse scenes and styles, similar to the versatility and expressiveness offered by text prompts. A key challenge in this task is to preserve the identity of the objects depicted in the input visual prompts, while also generating diverse compositions across different images. To address this challenge, we introduce a new KV-mixed cross-attention mechanism, in which keys and values are learned from distinct visual representations. The keys are derived from an encoder with a small bottleneck for layout control, whereas the values come from a larger bottleneck encoder that captures fine-grained appearance details. By mixing keys and values from these complementary sources, our model preserves the identity of the visual prompts while supporting flexible variations in object arrangement, pose, and composition. During inference, we further propose object-level compositional guidance to improve the method's identity preservation and layout correctness. Results show that our technique produces diverse scene compositions that preserve the unique characteristics of each visual prompt, expanding the creative potential of text-to-image generation.

Related papers

• Bringing Characters to New Stories: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting [71.29100512700064]
  We present T-Prompter, a training-free method for theme-specific image generation. T-Prompter integrates reference images into generative models, allowing users to seamlessly specify the target theme. Our approach enables consistent story generation, character design, realistic character generation, and style-guided image generation.
  arXiv  Detail & Related papers  (2025-01-26T19:01:19Z)
• Nested Attention: Semantic-aware Attention Values for Concept Personalization [78.90196530697897]
  We introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers. Our key idea is to generate query-dependent subject values, derived from nested attention layers that learn to select relevant subject features for each region in the generated image.
  arXiv  Detail & Related papers  (2025-01-02T18:52:11Z)
• Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing [47.421888361871254]
  Scene text images contain not only style information (font, background) but also content information (character, texture) Previous representation learning methods use tightly coupled features for all tasks, resulting in sub-optimal performance. We propose a Disentangled Representation Learning framework (DARLING) aimed at disentangling these two types of features for improved adaptability.
  arXiv  Detail & Related papers  (2024-05-07T15:00:11Z)
• Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation [59.78520153338878]
  Text-to-image diffusion techniques have shown exceptional capability of producing high-quality images from text descriptions. We propose a method built upon a state-of-the-art diffusion model, empowered by open-vocabulary to learn multi-scale textual-visual features for camouflaged object representations.
  arXiv  Detail & Related papers  (2023-12-29T07:59:07Z)
• Cross-Image Attention for Zero-Shot Appearance Transfer [68.43651329067393]
  We introduce a cross-image attention mechanism that implicitly establishes semantic correspondences across images. We harness three mechanisms that either manipulate the noisy latent codes or the model's internal representations throughout the denoising process. Experiments show that our method is effective across a wide range of object categories and is robust to variations in shape, size, and viewpoint.
  arXiv  Detail & Related papers  (2023-11-06T18:33:24Z)
• LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts [60.54912319612113]
  Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts. We present a novel approach leveraging Large Language Models (LLMs) to extract critical components from text prompts. Our evaluation on complex prompts featuring multiple objects demonstrates a substantial improvement in recall compared to baseline diffusion models.
  arXiv  Detail & Related papers  (2023-10-16T17:57:37Z)
• Splicing ViT Features for Semantic Appearance Transfer [10.295754142142686]
  We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image.
  arXiv  Detail & Related papers  (2022-01-02T22:00:34Z)

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.