Adversarial Training for Pixel Diffusion

arXiv:2609.38170 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Diffusion Model — 19× in this paper · explained in 8 episodes
• Diffusion Models — 14× in this paper · explained in 6 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Distillation — 8× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Adversarial Training — 3× in this paper · explained in 1 episode
• Domain Shift — 3× in this paper · explained in 4 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• In International Conference on Machine Learning — 3× in this paper
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 2× in this paper
• Analyzer — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Spectral Signature — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• PixelFlow: Pixel-Space Generative Models with Flow
• On the Importance of Noise Scheduling for Diffusion Models
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• PixelGen: Improving Pixel Diffusion with Perceptual Supervision
• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3

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