Core Insights - The article introduces ArcFlow, a novel image generation acceleration framework developed by Fudan University and Microsoft Research Asia, which addresses the long inference time and high computational costs associated with diffusion models by employing a non-linear flow mechanism instead of traditional linear simplification strategies [2][9]. Group 1: ArcFlow Innovations - ArcFlow achieves significant improvements, requiring only 2 steps (2 NFE) while maintaining high image quality comparable to the teacher model, resulting in approximately 40 times faster inference and 4 times faster training convergence [3][14]. - The method requires fine-tuning of less than 5% of the parameters, making it resource-efficient and quick to converge [3][15]. Group 2: Challenges in Existing Methods - Existing distillation methods assume a linear shortcut between noise and the final image, leading to geometric mismatch and poor image quality due to the complex, curved trajectories of teacher models [5][6]. - Traditional methods often require 40 to 100 steps for denoising, making real-time applications challenging and resulting in quality degradation when attempting to reduce steps [5][6]. Group 3: ArcFlow's Mechanisms - ArcFlow introduces momentum parameterization to capture the continuity of speed, eliminating sampling redundancy by modeling the speed field as a mixture of continuous momentum processes [11]. - The framework derives a closed-form analytical solution based on momentum equations, allowing for precise trajectory integration and high-accuracy flow matching [12]. - ArcFlow's trajectory distillation strategy preserves the non-linear characteristics of the teacher model, aligning instantaneous speeds without disrupting the pre-trained weight distribution, thus enhancing training efficiency [13]. Group 4: Experimental Results - ArcFlow has been validated on large-scale models like Qwen-Image-20B and FLUX.1-dev, demonstrating superior image quality and semantic consistency in benchmark tests compared to existing state-of-the-art methods [15][19]. - The results indicate that ArcFlow generates clearer images with rich details and diversity, avoiding issues like background blurriness and structural distortion seen in linear distillation methods [19]. Group 5: Conclusion - ArcFlow represents a significant advancement in knowledge distillation for image generation, effectively leveraging the prior knowledge of pre-trained teacher models while ensuring faster convergence and higher quality outputs [22].