λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion
  Models by Leveraging CLIP Latent Space
Authors: name: Maitreya Patel, name: Sangmin Jung, name: Chitta Baral, name: Yezhou Yang

Despite the recent advances in personalized text-to-image (P-T2I) generative
models, subject-driven T2I remains challenging. The primary bottlenecks include
1) Intensive training resource requirements, 2) Hyper-parameter sensitivity
leading to inconsistent outputs, and 3) Balancing the intricacies of novel
visual concept and composition alignment. We start by re-iterating the core
philosophy of T2I diffusion models to address the above limitations.
Predominantly, contemporary subject-driven T2I approaches hinge on Latent
Diffusion Models (LDMs), which facilitate T2I mapping through cross-attention
layers. While LDMs offer distinct advantages, P-T2I methods' reliance on the
latent space of these diffusion models significantly escalates resource
demands, leading to inconsistent results and necessitating numerous iterations
for a single desired image. Recently, ECLIPSE has demonstrated a more
resource-efficient pathway for training UnCLIP-based T2I models, circumventing
the need for diffusion text-to-image priors. Building on this, we introduce
lambda-ECLIPSE. Our method illustrates that effective P-T2I does not
necessarily depend on the latent space of diffusion models. lambda-ECLIPSE
achieves single, multi-subject, and edge-guided T2I personalization with just
34M parameters and is trained on a mere 74 GPU hours using 1.6M image-text
interleaved data. Through extensive experiments, we also establish that
lambda-ECLIPSE surpasses existing baselines in composition alignment while
preserving concept alignment performance, even with significantly lower
resource utilization.