E dit ID : Training-Free Editable ID Customization for Text-to-Image Generation (2025)

Abstract: We propose EditID, a training-free approach based on the DiT architecture, which achieves highly editable customized IDs for text to image generation. Existing text-to-image models for customized IDs typically focus more on ID consistency while neglecting editability. It is challenging to alter facial orientation, character attributes, and other features through prompts. EditID addresses this by deconstructing the text-to-image model for customized IDs into an image generation branch and a character feature branch. The character feature branch is further decoupled into three modules: feature extraction, feature fusion, and feature integration. By introducing a combination of mapping features and shift features, along with controlling the intensity of ID feature integration, EditID achieves semantic compression of local features across network depths, forming an editable feature space. This enables the successful generation of high-quality images with editable IDs while maintaining ID consistency, achieving excellent results in the IBench evaluation, which is an editability evaluation framework for the field of customized ID text-to-image generation that quantitatively demonstrates the superior performance of EditID. EditID is the first text-to-image solution to propose customizable ID editability on the DiT architecture, meeting the demands of long prompts and high-quality image generation.

## introduction id customization generation @xcite @xcite @xcite , as a personalized type of text-toimage generation, integrates ids with prompts to create specific appearances. it offers significant application value in scenarios such as story generation and character creation, and it is one of the * equal contribution. id customization methods include fine-tuning @xcite , tuning-free @xcite @xcite , and training-free @xcite approaches. fine-tuning requires timeconsuming, id-specific training. tuning-free methods pretrain an id fusion module on a large portrait dataset, avoiding id-specific customization during inference. training-free methods enhance tuningfree approaches, eliminating retraining in both training and inference. our training-free framework enables id editability in any id customization model with a character feature branch. current id customization methods generally prioritize character consistency, thereby overlooking character editability. we define character editability as the ability to generate multidimensional control relative to the input id in response to changes in text prompts, including variations in facial orientation and limb positioning, as well as the flexible modification of related attributes of the input id, such as hairstyle, accessories, and even age and gender. under our definition, current consistencyfocused methods generally lack editability. taking the state-of-the-art consistency generation method pulid @xcite as an example, the model achieves fidelity through id loss but also introduces semantic and layout losses to control the diversity of the input id during generation. however, in practical applications, it is nearly impossible to induce significant pose changes in the input id through prompt words. this prompted us to investigate the reasons behind the loss of editability. we found that the model is essentially performing an id reconstruction task. during the pre-training of tuning-free methods, the id and prompt descriptions remain almost identical, and the model leverages feature information from the id to bias the training parameters toward the distribution of the training set. this implies that the model intends to directly replicate the character's features. however, during inference, when the prompts and id are inconsistent, excessively strong feature constraints from the character branch result in the output id lacking the ability to adapt to changes in the prompt. in some cases, this even leads to a "copypaste" effect between the input id and output id in the facial region, as shown in figure 1 . the core contribution of this paper lies in modulating the control strength of the character branch within a training-free framework, achieving a text-to-image model that significantly enhances id editability while maintaining id consistency. the core of our approach lies in enhancing editability while maintaining character consistency, achieving a stable balance between the two. we deconstruct the text-to-image model for id customization, dividing it into an image generation branch and a character feature branch. the character feature branch is further decoupled into three modules: feature extraction, feature fusion, and feature integration. in feature extraction, we isolate five layers of identity-aware features from the fine-grained local feature extractor evaclip @xcite , which we term "mapping features." in feature fusion, due to the one-to-one correspondence between mapping features and five groups of neural networks, we designate features outside the five-layer mapping features of evaclip as "shift features." we discovered that these two types of features essentially perform semantic compression of local features across network depths, forming an editable feature space. the combination of these features enables predictable editability variations. in feature integration, when the fused features interact with the image generation branch, we introduce a dynamic information fusion mechanism in cross-attention to further enhance editability, ultimately achieving state-of-the-art (sota) results in the ibench evaluation framework. additionally, most current models are based on the unet architecture, with sd @xcite and sdxl @xcite serving as foundational models. in practical applications, such as story generation scenarios, there are typically two requirements: 1) the input of long prompts, and 2) higher aesthetic quality for the generated images. consequently, image generation based on the dit (peebles and xie, 2023) architecture, such as flux @xcite , becomes the preferred choice. however, there is scarcely any literature discussing methods to enhance character editability while preserving character consistency in dit-based image generation. to the best of our knowledge, we are the first to explore improving character editability within the dit architecture. considering the current lack of unified datasets and metrics for evaluating both id consistency and editability in the field of id customization, we propose a configurable and modular automated evaluation framework. we designed multiple sets of evaluation character images, accounting for real ids and generated ids, and adapted them to various types of prompts, including editability-measuring prompts, short prompts, and more. for the first time, we comprehensively introduce a variety of editability verification metrics to quantitatively assess model performance. in summary, our contributions are as follows: ## method our approach focuses on enhancing character editability while maintaining consistency. figure 2 shows the architecture, splitting the id customization into an image generation main branch and a character feature branch. the feature branch is divided into three modules: feature extraction, fusion, and integration. this framework identifies editability sources as: 1) local id features from extraction; 2) id shift features from fusion; and 3) embedding strength for integrating id information into the dit backbone. the third aspect affects both editability and consistency. by carefully combining local id features, we achieve optimal editability. ## ibench to address the lack and inadequacy of evaluation metrics for character consistency and editability in the field of personalized character image generation, and to quantify the improvement effects under a training-free architecture, we propose a configurable and modular automated evaluation framework, ibench. this framework comprehensively introduces and implements a variety of editability verification metrics. ## conclusion this paper proposes editid, a training-free id customization method for text-to-image generation. we are the first to explore enhancing editability within the dit architecture, achieving state-of-theart performance with long prompts. taking the pulid model as an example, we deconstruct it into a character feature branch and an image generation main branch, further decoupling the character feature branch into three major modules: feature extraction, feature fusion, and id integration. we analyze the sources of editability from the combination of mapping features and shift features, as well as dynamic id integration, thereby improving the editability of id customization. our approach requires no training, demonstrating its potential for flexible and efficient character-customized image generation. moreover, this training-free framework can be adapted to enhance any id customization generation algorithm equipped with a character feature branch. in future work, we will continue to explore and investigate the dynamic id integration module with the introduction of a training mode. we believe that dynamic id integration holds great vitality, but it still requires the design of loss functions incorporating richer multi-angle facial information to further achieve simultaneous improvements in both character consistency and editability.