FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content (2308.14256v2)

Abstract: Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection of portrait images. However, existing solutions are vulnerable in producing truthful details, and usually suffer from several defects such as (i) The generated face exhibit its own unique characteristics, \ie facial shape and facial feature positioning may not resemble key characteristics of the input, and (ii) The synthesized face may contain warped, blurred or corrupted regions. In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input. Concretely, we inject several SOTA face models into the generation procedure, achieving a more efficient label-tagging, data-processing, and model post-processing compared to previous solutions, such as DreamBooth ~\cite{ruiz2023dreambooth} , InstantBooth ~\cite{shi2023instantbooth} , or other LoRA-only approaches ~\cite{hu2021lora} . Besides, based on FaceChain, we further develop several applications to build a broader playground for better showing its value, including virtual try on and 2D talking head. We hope it can grow to serve the burgeoning needs from the communities. Note that this is an ongoing work that will be consistently refined and improved upon. FaceChain is open-sourced under Apache-2.0 license at \url{https://github.com/modelscope/facechain}.

• The paper presents FaceChain, a framework using dual LoRA models to preserve identity in AI-generated portraits.
• It integrates advanced face embedding, detection, and an innovative inpainting pipeline to optimize image authenticity.
• The approach shows practical benefits in virtual try-on and digital content creation by reducing visual artifacts and enhancing detail.

A Critical Examination of "FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content"

The paper introduces FaceChain, a sophisticated framework designed to address deficiencies in generating personalized portrait images using pre-trained text-to-image models. Existing solutions often fail to capture the necessary identity-preserving details, resulting in outputs that lack facial feature accuracy and contain visual artifacts, such as blurring and distortion. FaceChain's architecture effectively mitigates these limitations by integrating state-of-the-art (SOTA) face perception models into the generation process.

Technical Contributions

FaceChain distinguishes itself by using a modular framework that combines multiple SOTA techniques:

• LoRA-Based Fine-Tuning: The framework introduces two Low-Rank Adaptation (LoRA) models into the Stable Diffusion model, enhancing the capacity to integrate personal style and identity information concurrently. This approach offers a notable improvement over previous frameworks by constructing separate face-LoRA and style-LoRA models, which are trained both offline and online to ensure high accuracy.
• Data and Label Processing: Utilizing deep face embedding extraction, face detection, and attribute recognition models from ModelScope, FaceChain optimizes input images for training. This ensures that data fed into the model meets essential quality standards such as correct orientation and suitable tagging, which are pivotal for accurate text-to-image training.
• Innovative Inpainting Pipeline: The framework extends functionality by offering an inpainting alternative. This procedure supports face replacement within images, ensuring high identity fidelity without compromising image realism, thus enabling practical applications such as virtual try-on.

Numerical and Practical Implications

The incorporation of post-processing steps like face fusion and similarity ranking, alongside the use of Random Temperature Scaling (RTS) for out-of-distribution facial similarity, underscores FaceChain's dedication to producing high-quality and realistic outputs. The application's potential reaches into commercially relevant fields including virtual reality, digital content creation, and retail, particularly for virtual try-on applications that require realistic simulations of garment fitting on generated avatars.

Theoretical Implications and Future Directions

Theoretically, FaceChain broadens the approach to personalized multimedia generation by introducing a modular and extendable framework. Through its open-source nature, FaceChain invites contributions from the research community to expand its stylistic range and integrate novel model components.

Future research directions include enhancing the framework to handle multiple subjects and encode style information into a unified model. Additionally, exploring train-free frameworks for enabling scalability and addressing resource-intensiveness of current methods is anticipated. These advancements could further elevate the fidelity and adaptability of AI-generated personalized content, broadening the scope of possible applications and refining the effectiveness in identity retention and stylistic diversity.

In conclusion, FaceChain sets a notable precedent in personalized portrait generation, offering a comprehensive and adaptable solution for identity-preserving content creation. The paper delineates a clear path for ongoing development and integration into practical applications, providing software that not only meets current needs but also anticipates future demands in AI content generation.