LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation

Abstract

Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, such as text-conditioned image editing, enabling the modification of existing images based on textual guidance. This capability allows for the generation of diverse images that convey highly complex visual concepts. However, existing methods primarily focus on generating new images from text-image pairs and struggle to produce fine-grained animations from existing images and textual guidance without fine-tuning. In this paper, we introduce LASER, a tuning-free LLM-driven attention control framework that follows a progressive process: LLM planning, feature-attention injection, and stable animation generation. LASER leverages a large language model (LLM) to refine general descriptions into fine-grained prompts, guiding pre-trained text-to-image models to generate aligned keyframes with subtle variations. The LLM also generates control signals for feature and attention injections, enabling seamless text-guided image morphing for various transformations without additional fine-tuning. By using the same initial noise inversion from the input image, LASER receives LLM-controlled injections during denoising and leverages interpolated text embeddings to produce a series of coherent animation frames. We propose a Text-conditioned Image-to-Animation Benchmark to validate the effectiveness and efficacy of LASER. Extensive experiments demonstrate that LASER achieves impressive results in consistent and efficient animation generation, establishing it as a powerful tool for producing detailed animations and opening new avenues in digital content creation.