title: Boosting Generative Image Modeling via Joint Image-Feature Synthesis
summary: Latent diffusion models (LDMs) dominate high-quality image generation, yet
  integrating representation learning with generative modeling remains a
  challenge. We introduce a novel generative image modeling framework that
  seamlessly bridges this gap by leveraging a diffusion model to jointly model
  low-level image latents (from a variational autoencoder) and high-level
  semantic features (from a pretrained self-supervised encoder like DINO). Our
  latent-semantic diffusion approach learns to generate coherent image-feature
  pairs from pure noise, significantly enhancing both generative quality and
  training efficiency, all while requiring only minimal modifications to standard
  Diffusion Transformer architectures. By eliminating the need for complex
  distillation objectives, our unified design simplifies training and unlocks a
  powerful new inference strategy: Representation Guidance, which leverages
  learned semantics to steer and refine image generation. Evaluated in both
  conditional and unconditional settings, our method delivers substantial
  improvements in image quality and training convergence speed, establishing a
  new direction for representation-aware generative modeling.
paper_authors._id: 680b494c6bd146aa35ab2e1c
paper_authors.name: Theodoros Kouzelis
paper_authors._id: 680b494c6bd146aa35ab2e1d
paper_authors.name: Efstathios Karypidis
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T12:27:13.393Z
paper_authors._id: 677272184d148b904333e874
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/5dUau7gxLk4Wm1TiiJJri.jpeg
paper_authors.fullname: Efstathios Karypidis
paper_authors.type: user
paper_authors.user: Sta8is
paper_authors._id: 680b494c6bd146aa35ab2e1e
paper_authors.name: Ioannis Kakogeorgiou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T12:27:11.569Z
paper_authors._id: 661ba524bd9243bf7e598355
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661ba524bd9243bf7e598355/i77yD4XgJn2vUbn_mIsT8.jpeg
paper_authors.fullname: Ioannis Kakogeorgiou
paper_authors.type: user
paper_authors.user: gkakogeorgiou
paper_authors._id: 680b494c6bd146aa35ab2e1f
paper_authors.name: Spyros Gidaris
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-28T13:50:08.450Z
paper_authors._id: 678cd947d13c19c917644792
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/010zyg-JoIjaqVn9lSDRT.png
paper_authors.fullname: Spyros Gidaris
paper_authors.type: user
paper_authors.user: sgidaris
paper_authors._id: 680b494c6bd146aa35ab2e20
paper_authors.name: Nikos Komodakis
paper_title: Boosting Generative Image Modeling via Joint Image-Feature Synthesis
paper_summary: Latent diffusion models (LDMs) dominate high-quality image generation, yet
  integrating representation learning with generative modeling remains a
  challenge. We introduce a novel generative image modeling framework that
  seamlessly bridges this gap by leveraging a diffusion model to jointly model
  low-level image latents (from a variational autoencoder) and high-level
  semantic features (from a pretrained self-supervised encoder like DINO). Our
  latent-semantic diffusion approach learns to generate coherent image-feature
  pairs from pure noise, significantly enhancing both generative quality and
  training efficiency, all while requiring only minimal modifications to standard
  Diffusion Transformer architectures. By eliminating the need for complex
  distillation objectives, our unified design simplifies training and unlocks a
  powerful new inference strategy: Representation Guidance, which leverages
  learned semantics to steer and refine image generation. Evaluated in both
  conditional and unconditional settings, our method delivers substantial
  improvements in image quality and training convergence speed, establishing a
  new direction for representation-aware generative modeling.
paper_ai_summary: A novel generative image modeling framework uses latent-semantic diffusion to learn coherent image-feature pairs, enhancing generative quality and training efficiency with minimal modifications to standard architectures.
paper_ai_keywords: latent diffusion models variational autoencoder pretrained self-supervised encoder DINO latent-semantic diffusion generative quality training efficiency Diffusion Transformer Representation Guidance image quality training convergence speed