flux2

Official inference repo for FLUX.2 models

AI & Machine Learning

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REPOSITORY AT A GLANCE

What to know before exploring

What it is

Official inference repo for FLUX.2 models

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week2 dated mentions

Observed mentions: 2025-11-25 18:54:29 UTC to 2026-01-18 19:27:12 UTC. Weeks start Monday, UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
Nov 24	2025-11-24 00:00:00 UTC	2025-12-01 00:00:00 UTC	1	1
Dec 1	2025-12-01 00:00:00 UTC	2025-12-08 00:00:00 UTC	0	0
Dec 8	2025-12-08 00:00:00 UTC	2025-12-15 00:00:00 UTC	0	0
Dec 15	2025-12-15 00:00:00 UTC	2025-12-22 00:00:00 UTC	0	0
Dec 22	2025-12-22 00:00:00 UTC	2025-12-29 00:00:00 UTC	0	0
Dec 29	2025-12-29 00:00:00 UTC	2026-01-05 00:00:00 UTC	0	0
Jan 5	2026-01-05 00:00:00 UTC	2026-01-12 00:00:00 UTC	0	0
Jan 12	2026-01-12 00:00:00 UTC	2026-01-19 00:00:00 UTC	1	1

What Hacker News says

2 sources

throwaway2027·Jan 18, 2026

If I asked Claude to do the same can I also just put MIT license on it with my name? https://github.com/black-forest-labs/flux2 uses Apache License apparently. I know it doesn't matter that much and as long as it's permissive and openly available people don't care it's just pedantics but still.

In Flux 2 Klein pure C inference

minimaxir·Nov 25, 2025

I just finished my Flux 2 testing (focusing on the Pro variant here: https://replicate.com/black-forest-labs/flux-2-pro). Overall, it's a tough sell to use Flux 2 over Nano Banana for the same use cases, but even if Nano Banana didn't exist it's only an iterative improvement over Flux 1.1 Pro. Some notes: - Running my nuanced Nano Banana prompts though Flux 2, Flux 2 definitely has better prompt adherence than Flux 1.1, but in all cases the image quality was worse/more obviously AI generated. - The prompting guide for Flux 2 (https://docs.bfl.ai/guides/prompting_guide_flux2) encourages JSON prompting by default, which is new for an image generation model that has the text encoder to support it. It also encourages hex color prompting, which I've verified works. - Prompt upsampling is an option, but it's one that's pushed in the documentati

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on (https://github.com/black-forest-labs/flux2/blob/main/docs/fl...). This does allow the model to deductively reason, e.g. if asked to generate an image of a Fibonacci implementation in Python it will fail hilariously if prompt sampling is disabled, but get somewhere if it's enabled: https://x.com/minimaxir/status/1993361220595044793 - The Flux 2 API will flag anything tangently related to IP as sensentive even at its lowest sensitivity level, which is different from Flux 1.1 API. If you enable prompt upsampling, it won't get flagged, but the results are...unexpected. https://x.com/minimaxir/status/1993365968605864010 - Costwise and generation-speed-wise, Flux 2 Pro is on par with Nano Banana, and adding an image as an input pushes the cost of Flux 2 Pro higher than Nano Banana. The cost discrepancy increases if you try to utilize the advertised multi-image reference feature. - Testing Flux 1.1 vs. Flux 2 generations does not result in objective winners, particularly around more abstract generations.

In FLUX.2: Frontier Visual Intelligence