Layered Generation: A New Approach to Precise AI Image Synthesis

From Single Instance to Complex Scene: A New Approach to AI Image Generation

Text-to-image diffusion models have revolutionized the possibilities of AI-powered image generation. High-quality images can be created using text input. However, precisely controlling the generated content often requires laborious interventions and adjustments to the text input (prompt engineering). Simply describing the image content through text reaches its limits, especially with complex scenes involving multiple objects and specific attributes. Accurately capturing subtle linguistic nuances, spatial relationships, or the exact number of objects in text form poses a challenge. Furthermore, small changes in the text can lead to unexpected and significant deviations in the generated image.

Approaches to improve control, such as specifying image layouts with object positions in the form of bounding boxes, offer more control over the image structure but still generate all components as a single image. This limits control over object attributes and requires image regeneration for layout adjustments, whereby the original image content is not necessarily preserved. Image editing techniques can help here, but are often computationally intensive and require additional training data. In particular, moving or scaling objects within a scene presents a challenge, as both objects and the background must be regenerated while maintaining the appearance of the original image.

A promising approach to solving these problems is layer-based generation. Here, images are represented as layered structures, with different image components generated on separate layers. This offers two advantages: First, individual objects can be generated more precisely as separate images with individual text inputs. Second, the separation of image components simplifies image manipulation, as only the relevant layers need to be changed. However, existing layer-based methods suffer from the drawback that the layers are not fully generated, but are combined into a single image in later stages of the diffusion process. The joint generation of all components can impair control over object attributes, their relative positioning in 3D space, and the seamless composition of all image components.

A Multi-Stage Approach for Precise Image Design

A novel approach pursues a multi-stage generation process designed for precise control, flexibility, and interactivity. Complex scenes are built by first generating individual objects as RGBA images (with transparency information) and then iteratively integrating them into a multi-layered image according to a specific layout. Generating individual instances allows precise control over their appearance and attributes (e.g., colors, patterns, and pose), while layered composition facilitates control of position, size, and order. This two-stage approach enables intrinsic layout and attribute manipulations while preserving image content.

To achieve this, a diffusion model is first trained that can generate RGBA images. The direct generation of isolated objects, as opposed to generation and extraction using a segmentation model, allows for more precise transparency masks and finer control over object attributes. The RGBA generator is trained by fine-tuning a Latent Diffusion Model (LDM) using RGBA instance data. To integrate transparency information into the generation process, a special training procedure for VAE and diffusion model is developed. In contrast to other methods that implicitly encode transparency information, transparency is explicitly integrated into the generation and training process here. The VAE is trained to decouple RGB and alpha channels, ensuring color and detail fidelity during RGB reconstruction. The LDM is then fine-tuned with a novel training paradigm that leverages the decoupled latent space and enables a conditional, sampling-controlled inference procedure where alpha and RGB latents are denoised sequentially and conditionally on each other.

Finally, the RGBA generator is used to create composite images with precise control over object attributes and scene layout. Multi-instance scenes are built through a multi-layered noise-blending process where each instance is assigned to a specific image layer. Each instance is individually integrated into the scene, creating progressively more complex image layers to ensure scene coherence and accurate relative positioning. This contrasts with previous multi-layered approaches that combine all instances simultaneously. By manipulating latent representations in early stages of the denoising process, a high degree of precision and control is achieved while generating smooth and realistic scenes.

Conclusion: A Step Towards Interactive Image Generation

This multi-stage approach to image generation offers a high degree of control and flexibility. The generation of RGBA instances allows precise control of object attributes, while the multi-layered composition simplifies the arrangement and interaction of objects in the scene. This approach paves the way for interactive image generation systems that allow users to create and manipulate complex scenes with a high degree of precision and control.

Bibliography: https://arxiv.org/abs/2411.10913 https://arxiv.org/html/2411.10913v1 https://openreview.net/forum?id=MwFeh4RqvA&referrer=%5Bthe%20profile%20of%20Shifeng%20Zhang%5D(%2Fprofile%3Fid%3D~Shifeng_Zhang5) https://mulanrgba.github.io/ https://www.arxiv-sanity-lite.com/?rank=pid&pid=2411.10913 https://paperreading.club/page?id=266791 https://trendingpapers.com/similar?id=2411.10913 https://www.linkedin.com/posts/alessandro-fontanella-4233b315a_the-paper-from-my-internship-at-huawei-has-activity-7245007035782549504-lRvI https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Tan_Text2Scene_Generating_Compositional_CVPR_2019_supplemental.pdf https://www.iflowai.com/static/chat/Generating%20Compositional%20Scenes%20via%20Text-to-image%20RGBA%20Instance%20Generation