Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation

arXiv:2609.29116 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/black-forest-labs/flux

Terminology

• Distillation — 32× in this paper · explained in 2 episodes
• Diffusion Model — 18× in this paper · explained in 8 episodes
• Distribution Matching — 16× in this paper · explained in 2 episodes
• Diffusion Models — 14× in this paper · explained in 6 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Distribution Matching Distillation — 8× in this paper
• Ablation — 6× in this paper · explained in 1 episode
• Spectral Amplitude Purification — 5× in this paper
• CLIP Score — 3× in this paper
• Consistency Distillation — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Spectral Amplitude Purification for Distribution Matching Distillation — 3× in this paper
• AUC — 2× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Score Function — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Two Time-scale Update Rule — 1× in this paper

Sources

• A Fourier Space Perspective on Diffusion Models
• SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
• Classifier-Free Diffusion Guidance
• Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
• SDXL-Lightning: Progressive Adversarial Diffusion Distillation
• Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• Seedance 2.0: Advancing Video Generation for World Complexity
• Qwen-Image Technical Report
• Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
• Open-Sora: Democratizing Efficient Video Production for All

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