Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation

arXiv:2606.03746 · cs.CV, cs.AI, cs.GR, cs.LG · Submitted 2026-06-02 · Read on arXiv

cs.CV, cs.AI, cs.GR, cs.LG

Submitted: 2026-06-02

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Distillation — 110× in this paper · explained in 2 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Distribution Matching — 9× in this paper · explained in 2 episodes
• Flow Matching — 9× in this paper · explained in 7 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Core — 3× in this paper · explained in 2 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• On-Policy Distillation — 3× in this paper · explained in 3 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Prompt Adherence — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Optimizing Few-Step Generation with Adaptive Matching Distillation
• Flow-OPD: On-Policy Distillation for Flow Matching Models
• Mean Flows for One-step Generative Modeling
• Distribution Matching Distillation Meets Reinforcement Learning
• DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models
• Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
• ERNIE-Image Technical Report
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward
• Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
• Progressive Distillation for Fast Sampling of Diffusion Models
• JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
• TIIF-Bench: How Does Your T2I Model Follow Your Instructions?
• Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
• MiMo-V2-Flash Technical Report
• Qwen3 Technical Report
• Qwen-Image-2.0 Technical Report

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