A Unified and Controllable Framework for Layered Image Generation with Visual Effects

Recent image generation models produce impressive composites, but often fail to preserve the identity of user-provided content when editing specific elements: the surrounding scene may shift, and even the edited object’s appearance can drift from the original. Layered representations offer a natural remedy—they allow users to independently manipulate individual elements—but existing layered methods typically produce transparent foregrounds without realistic visual effects such as shadows and reflections, forcing the use of a second harmonization model after every edit, which in turn reintroduces drift. To overcome these limitations, we present LASAGNA, which generates a photorealistic background and an RGBA foreground with compelling visual effects in a single forward pass. By treating object-associated visual effects as part of the foreground layer, LASAGNA supports the dominant class of consumer edits (e.g., translation, scaling, recoloring, duplication) via alpha compositing alone, without invoking any model post-edit, thereby eliminating the identity drift inherent to cascade editing pipelines. This single-pass design contrasts with prior layered methods that rely on separate expert models for each task; LASAGNA handles diverse conditioning inputs—text prompts, foreground, background, and location masks—within a unified architecture. We further release two community resources: LASAGNA-48K, the first public dataset of 48K layered image triplets with physically grounded visual effects (curated via a VLM-based filter trained on 30K human-labeled examples), and LASAGNABench, the first standardized benchmark for layer-centric generation and editing, comprising 242 expert-annotated samples across six diverse sources. Experiments show that LASAGNA outperforms both general-purpose editors (FLUX, Qwen-Image-Edit, gpt-image-1) and prior layered methods (LayerDiffuse) across three generation modes, and supports a wide range of post-edits without any neural re-inference.