PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Distillation — 86× in this paper · explained in 2 episodes
• Fidelity — 49× in this paper · explained in 2 episodes
• Distribution Matching — 31× in this paper · explained in 2 episodes
• Diffusion Model — 24× in this paper · explained in 8 episodes
• Transformer — 23× in this paper · explained in 2 episodes
• Consistency — 21× in this paper · explained in 3 episodes
• Adaptation — 20× in this paper · explained in 1 episode
• Ablation — 12× in this paper · explained in 1 episode
• Knowledge Distillation — 9× in this paper · explained in 11 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Directional Reliability Weighting — 6× in this paper
• Latent Space — 6× in this paper · explained in 7 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Distribution Matching Distillation — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Heterogeneous Distribution Adaptation — 4× in this paper
• LoRA Adaptation — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Gaussian Splatting — 2× in this paper · explained in 4 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
• OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression
• Distilling the Knowledge in a Neural Network
• Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers
• Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution
• Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
• Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
• Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling
• GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting
• HunyuanVideo 1.5 Technical Report
• Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
• Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
• GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution

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