Space-Selected Image Generation: A Target-Conditioned Framework for Image-Specific Latent Space Selection, Construction, and Evolution

Most modern image-generation systems operate within a learned representation space, a latent space, or a sequence of intermediate feature spaces. Although conditioning mechanisms allow these systems to generate different images from different prompts, the underlying generative architecture and its representation space are commonly shared across multiple generation tasks. This paper introduces Space-Selected Image Generation (SSIG), a conceptual framework that investigates whether different target images should be associated with different, target-adapted generative vector spaces rather than relying exclusively on a fixed shared latent space. Inspired by the representational role of vector spaces in natural language processing, SSIG treats an image not only as an output to be generated but also as a condition that can guide the selection, construction, and subsequent evolution of its generative space. The framework distinguishes between a shared base space, a target-conditioned selected space, and an optional dynamically evolving space. A mathematical formulation is developed to describe target representation, space selection, adaptive dimensionality, image generation, and feedback-driven space refinement. The paper further proposes an implementation architecture, a training objective, an iterative generation algorithm, research hypotheses, and a controlled experimental protocol. Comparisons with fixed latent spaces, conventional conditional generation, and dynamically adapted representation spaces are discussed. The central hypothesis is that target-specific generative spaces may improve the allocation of representational capacity and computational resources when image structure, complexity, and generation requirements vary substantially. However, these benefits remain empirical questions. SSIG is therefore presented as a testable research framework rather than a demonstrated improvement over existing image-generation methods.