# Flow Map Distillation Without Data

Source: [Flow Map Distillation Without Data](<https://www.sota2.com/research/paper/69253887d6b0dd334292f957>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-28T06:00:01.729Z

Authors: Shangyuan Tong, Nanye Ma, Saining Xie, Tommi Jaakkola

Published: 2025-11-24T18:58:55.000Z

## Abstract

State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure that conventionally requires sampling from an external dataset. We argue that this data-dependency introduces a fundamental risk of Teacher-Data Mismatch, as a static dataset may provide an incomplete or even misaligned representation of the teacher's full generative capabilities. This leads us to question whether this reliance on data is truly necessary for successful flow map distillation. In this work, we explore a data-free alternative that samples only from the prior distribution, a distribution the teacher is guaranteed to follow by construction, thereby circumventing the mismatch risk entirely. To demonstrate the practical viability of this philosophy, we introduce a principled framework that learns to predict the teacher's sampling path while actively correcting for its own compounding errors to ensure high fidelity. Our approach surpasses all data-based counterparts and establishes a new state-of-the-art by a significant margin. Specifically, distilling from SiT-XL/2+REPA, our method reaches an impressive FID of 1.45 on ImageNet 256x256, and 1.49 on ImageNet 512x512, both with only 1 sampling step. We hope our work establishes a more robust paradigm for accelerating generative models and motivates the broader adoption of flow map distillation without data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Class-conditional image generation on ImageNet 256x256 (val)](<https://www.sota2.com/research/sota/class-conditional-image-generation-on-imagenet-256x256-val.md>): Class-conditional Image Generation; ImageNet 256x256 (val); Inception Score (IS): not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.19428>)

[PDF](<https://arxiv.org/pdf/2511.19428.pdf>)