  Revolutionary advancements in text-to-image models have unlocked new
dimensions for sophisticated content creation, e.g., text-conditioned image
editing, allowing us to edit the diverse images that convey highly complex
visual concepts according to the textual guidance. Despite being promising,
existing methods focus on texture- or non-rigid-based visual manipulation,
which struggles to produce the fine-grained animation of smooth
text-conditioned image morphing without fine-tuning, i.e., due to their highly
unstructured latent space. In this paper, we introduce a tuning-free LLM-driven
attention control framework, encapsulated by the progressive process of LLM
planning, prompt-Aware editing, StablE animation geneRation, abbreviated as
LASER. LASER employs a large language model (LLM) to refine coarse descriptions
into detailed prompts, guiding pre-trained text-to-image models for subsequent
image generation. We manipulate the model's spatial features and self-attention
mechanisms to maintain animation integrity and enable seamless morphing
directly from text prompts, eliminating the need for additional fine-tuning or
annotations. Our meticulous control over spatial features and self-attention
ensures structural consistency in the images. This paper presents a novel
framework integrating LLMs with text-to-image models to create high-quality
animations from a single text input. We also propose a Text-conditioned
Image-to-Animation Benchmark to validate the effectiveness and efficacy of
LASER. Extensive experiments demonstrate that LASER produces impressive,
consistent, and efficient results in animation generation, positioning it as a
powerful tool for advanced digital content creation.
