SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation

• URL: http://arxiv.org/abs/2605.18267v2
• Date: Sat, 23 May 2026 08:45:59 GMT
• Title: SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation
• Abstract summary: Normalizing flows (NFs) provide exact likelihoods and deterministic invertible sampling, but have lagged behind diffusion models for large-scale image generation.<n>We propose SRC-Flow, which introduces a Semantic Representation (SRC) to compact high-dimensional RAE features into a low-dimensional semantic space.<n>SRC-Flow achieves state-of-the-art generation quality among flow methods, with gFID scores of 1.65 and 2.07 under computation-free guidance.
• Score: 73.51436199324066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Normalizing flows (NFs) provide exact likelihoods and deterministic invertible sampling, but have historically lagged behind diffusion models for large-scale image generation. We identify a key obstacle: NFs are required to learn a single invertible transport over the full ambient space, making them highly sensitive to high-dimensional representations. This leads to a semantic-capacity mismatch in modern visual representation spaces, where semantic information is compact but encoded in overcomplete features. We propose SRC-Flow, which introduces a Semantic Representation Compressor (SRC) to compact high-dimensional RAE features into a low-dimensional semantic space before flow modeling and preserve reconstruction through the frozen RAE decoder. This compact space reduces the modeling burden of NFs and enables effective likelihood-based generation in semantic representation space. We further adopt constant noise regularization tailored to the fixed unconditional bijection learned by flows. On ImageNet $256 \times 256$ and $512 \times 512$, SRC-Flow achieves state-of-the-art generation quality among normalizing flow methods, with gFID scores of 1.65 and 2.07 under classifier-free guidance, while retaining exact likelihood computation in the compact semantic representation space and deterministic invertible sampling at the flow level. Codes and models will be available at https://github.com/longtaojiang/SRC-Flow.

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