Siddharth Ancha @siddancha · 19h
This is very cool! But also not that surprising. Flow matching models are "uniquely identifiable" i.e. any sufficiently well trained flow model on the same data distribution will learn the same latent encoding (mapping from z → x), regardless of architecture.
In @jaschasd's words: youtube.com/watch?v=XCUlnH... . This should be true for diffusion models too if you properly seed the randomness used to generate intermediate samples.
It's still remarkable how strongly identifiable flow matching models are, especially the male/female and CelebHQ/FFHQ experiments. Says a lot about the datasets!
> QUOTED:
> Kwang Moo Yi @kwangmoo_yi · Apr 20
> Briq et al., "The Amazing Stability of Flow Matching"
>
> The attached image explains it all (with minor caption error though) -- training flow matching ...
>
> [Figure 1: Stability of the generated images grid, 4 panels: (a) Two disjoint random subsets — model trained on two disjoint random subsets of data produces visually very similar images; (b) DiT-XL/4 → DiT-S/2 → U-Net — model capacity/architecture change retains high similarity; (c) Both genders/Female/Male — data split by zero-shot gender classification, retained partition preserves semantic interpretation while complementary class swaps it; (d) CelebHQ → FFHQ — changing training dataset while keeping the same VAE retains similarity too. Caption: "Figure 1: Stability of the generated images. (a) We train the model on two disjoint random subsets of the data, and obtain visually very similar images. (b) The data is split into two sets based on zero-shot classification as male/female. Images we visually interpret as belonging to the retained partition are semantically preserved, while images of the complementary class swap the semantic interpretation. (c) Model capacity change from DiT-XL to DiT-S retains high similarity, while switching to a U-Net architecture retains similarity to a lesser degree. (d) Changing the training dataset from CelebHQ to FFHQ, while still using CelebHQ VAE, retains similarity too."]
Note from Claude Sonnet 5
A thread discussing "The Amazing Stability of Flow Matching" (Briq et al.) — the finding that flow-matching/diffusion generative models trained on different data subsets, architectures, or even datasets converge to nearly identical latent-to-output mappings, taken as evidence for architecture-independent "uniquely identifiable" representations. Directly relevant to the platonic-representation-hypothesis thread flagged in project memory ("Platonic hypothesis and model representation spaces" chat, cluster 11) as potentially bearing on alignment-via-character arguments — convergent representations across architectures/training runs is empirical support for that hypothesis.