LCM-Lookahead for Encoder-based Text-to-Image Personalization

• URL: http://arxiv.org/abs/2404.03620v1
• Date: Thu, 4 Apr 2024 17:43:06 GMT
• Title: LCM-Lookahead for Encoder-based Text-to-Image Personalization
• Authors: Rinon Gal, Or Lichter, Elad Richardson, Or Patashnik, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or,
• Abstract summary: We explore the potential of using shortcut-mechanisms to guide the personalization of text-to-image models. We focus on encoder-based personalization approaches, and demonstrate that by tuning them with a lookahead identity loss, we can achieve higher identity fidelity.
• Score: 82.56471486184252
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly, when these are distilled from existing diffusion models, they often maintain alignment with the original model, retaining similar outputs for similar prompts and seeds. These properties present opportunities to leverage fast sampling methods as a shortcut-mechanism, using them to create a preview of denoised outputs through which we can backpropagate image-space losses. In this work, we explore the potential of using such shortcut-mechanisms to guide the personalization of text-to-image models to specific facial identities. We focus on encoder-based personalization approaches, and demonstrate that by tuning them with a lookahead identity loss, we can achieve higher identity fidelity, without sacrificing layout diversity or prompt alignment. We further explore the use of attention sharing mechanisms and consistent data generation for the task of personalization, and find that encoder training can benefit from both.

Related papers

• Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency [33.35678923549471]
  FreeCure is a training-free framework that harnesses the intrinsic knowledge from the foundation models themselves to improve the prompt consistency of personalization models. We enhance multiple attributes in the outputs of personalization models through a novel noise-blending strategy and an inversion-based process.
  arXiv  Detail & Related papers  (2024-11-22T15:21:38Z)
• LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation [17.169772329737913]
  We present LocRef-Diffusion, a tuning-free model capable of personalized customization of multiple instances' appearance and position within an image. To enhance the precision of instance placement, we introduce a layout-net, which controls instance generation locations. To improve the appearance fidelity to reference images, we employ an appearance-net that extracts instance appearance features.
  arXiv  Detail & Related papers  (2024-11-22T08:44:39Z)
• Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis [7.099258248662009]
  Text-to-image (T2I) models have significantly advanced the development of artificial intelligence. However, existing T2I-based methods often struggle to accurately reproduce the appearance of individuals from a reference image. We leverage the pre-trained UNet from Stable Diffusion to incorporate the target face image directly into the generation process.
  arXiv  Detail & Related papers  (2024-09-27T19:31:04Z)
• FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior [50.0535198082903]
  We offer a novel approach to image composition, which integrates multiple input images into a single, coherent image. We showcase the potential of utilizing the powerful generative prior inherent in large-scale pre-trained diffusion models to accomplish generic image composition.
  arXiv  Detail & Related papers  (2024-07-06T03:35:43Z)
• RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance [22.326405355520176]
  RefDrop allows users to control the influence of reference context in a direct and precise manner. Our method also enables more interesting applications, such as the consistent generation of multiple subjects.
  arXiv  Detail & Related papers  (2024-05-27T21:23:20Z)
• Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval [65.43522019468976]
  We propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes. We develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors. Our models are equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities.
  arXiv  Detail & Related papers  (2023-11-21T08:20:38Z)
• Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models [59.094601993993535]
  Text-to-image (T2I) personalization allows users to combine their own visual concepts in natural language prompts. Most existing encoders are limited to a single-class domain, which hinders their ability to handle diverse concepts. We propose a domain-agnostic method that does not require any specialized dataset or prior information about the personalized concepts.
  arXiv  Detail & Related papers  (2023-07-13T17:46:42Z)
• Identity Encoder for Personalized Diffusion [57.1198884486401]
  We propose an encoder-based approach for personalization. We learn an identity encoder which can extract an identity representation from a set of reference images of a subject. We show that our approach consistently outperforms existing fine-tuning based approach in both image generation and reconstruction.
  arXiv  Detail & Related papers  (2023-04-14T23:32:24Z)
• Semantic Image Synthesis via Diffusion Models [159.4285444680301]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks. Recent work on semantic image synthesis mainly follows the emphde facto Generative Adversarial Nets (GANs)
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)

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.