Embedding Prediction Helps Image Generation

arXiv:2610.02203 · cs.CV, cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Embeddings — 59× in this paper · explained in 2 episodes
• Transformer — 33× in this paper · explained in 2 episodes
• Diffusion Model — 27× in this paper · explained in 8 episodes
• Diffusion Models — 26× in this paper · explained in 6 episodes
• Multi-Embedding Prediction — 17× in this paper
• Alignment — 10× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Latent Space — 8× in this paper · explained in 7 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Latent Diffusion Model — 6× in this paper · explained in 2 episodes
• Latent Diffusion Models — 6× in this paper · explained in 2 episodes
• Flow Matching — 5× in this paper · explained in 7 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Next-Embedding Predictive Autoregression — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
• CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization
• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
• Emerging Properties in Unified Multimodal Pretraining
• Recursive Flow Matching
• No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
• Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
• Boosting Latent Diffusion Models via Disentangled Representation Alignment
• Transfer between Modalities with MetaQueries
• RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space
• Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Improved Techniques for Training GANs
• Improved Baselines with Representation Autoencoders
• Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
• U-REPA: Aligning Diffusion U-Nets to ViTs
• Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
• Representation Learning with Contrastive Predictive Coding

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