IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation

2026-05-28

AI Summary

Current diffusion-based talking face generation methods rely on task-specific fine-tuning and large-scale audiovisual datasets, resulting in high computational costs and limited scalability. This work proposes the first fully fine-tuning-free generation paradigm by directly leveraging pre-trained Stable Diffusion and IP-Adapter, augmented with three parameter-free components: a Structurist module for structural disentanglement, a structure controller, and a noise-aware module. These components effectively mitigate identity drift, lip-audio asynchrony, and temporal instability. The proposed method achieves superior visual fidelity—demonstrated by an FID improvement of at least 0.7—while significantly enhancing lip-sync accuracy, with a PCLD gain of at least 0.16, outperforming current state-of-the-art approaches.

Abstract

With the rapid advancement of diffusion models, talking face generation has made remarkable progress. However, existing diffusion-based methods still require task-specific fine-tuning and large-scale audiovisual datasets, resulting in high computational costs that hinder scalability and accessibility of diffusion-based approaches across the research community. To address this, we propose a finetuning-free paradigm that directly performs talking face generation using the pretrained weights of Stable Diffusion and IP-Adapter. This backbone leverages the visual embedding capability of IP-Adapter to mine lip-related semantics from the pretrained Stable Diffusion. To address the challenges of identity drift, synchronization errors, and temporal instability, we also design three trainable-parameterfree components: (1) the Structurist, which explicitly disentangles and reassembles lip and appearance features to mitigate identity drift and appearance distortion; (2) the Structure Controller, which adaptively refines embeddings based on quasi-monotonic motion trends for precise lip synchronization; and (3) the Noise Sensor, which introduces Gaussian prior to detect and suppress flicker and jitter artifacts and enhance temporal consistency. Experimental results show that our method outperforms existing SOTA approaches in both lip-sync accuracy (at least 0.16 gain in PCLD) and visual fidelity (at least 0.7 improvement in FID), establishing a novel fine-tuning-free diffusion framework for talking face generation.

Problem

• talking face generation
• diffusion models
• fine-tuning-free
• lip synchronization
• temporal consistency

Innovation

• fine-tuning-free
• diffusion model
• talking face generation
• IP-Adapter
• temporal consistency