Flux (text-to-image model)
suite of text-to-image models

Flux is a family of text-to-image and image-to-image models developed by Black Forest Labs (BFL), based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts. In addition, some Flux models support editing images.
Black Forest Labs
Black Forest Labs (BFL) was founded in 2024 by Robin Rombach, Andreas Blattmann, and Patrick Esser, former employees of Stability AI, and seven others. Rombach, Blattmann and Esser had previously researched the artificial intelligence image generation at LMU Munich as research assistants under Björn Ommer. They had published their research results on image generation in April 2022, which resulted in creation of Stable Diffusion. Investors in BFL included venture capital firm Andreessen Horowitz, Brendan Iribe, Michael Ovitz, Garry Tan, and Vladlen Koltun. The company received an initial investment of US$31 million.
At the end of 2025, investor interest had risen dramatically including AMP, Salesforce Ventures, Samsung, Adobe, telekom, Canva offering USD 450 million. and BLF became a Delaware corporation.
“Flux (text-to-image model)” enters the record as suite of text-to-image models. Crown Archives preserves that source wording while asking what Flux, text-to-image and model can confirm, complicate or overturn.
Why this record matters
“Flux (text-to-image model)” is worth following because a concise public description often conceals a longer documentary argument. Here, Flux, text-to-image and model provides the most credible route into that argument.
The citation trail is more important than the brevity of the summary: it shows where individual claims can be examined in context. The source revision retrieved here is dated Sep 8, 2026. The linked authority identifier is Q128801793. None of the 2 selected statements returned an explicit reference. The first chronological checks are 2024, 2022 and 2025.
A concise general-reference account can conceal disagreements about scope, terminology or the weight assigned to individual sources. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
How to read it
Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.
- Subject orientation
- Search vocabulary
- Locating named sources
The closest primary source, responsible institution and strongest cited specialist reference.
Three-step research path
- Establish the record: confirm the title “Flux (text-to-image model)”, its source revision and the description used here.
- Expand the search: follow Flux (text-to-image model) primary sources, Flux (text-to-image model) archive and Flux research across catalogues and specialist indexes.
- Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.
Questions for further research
- Which source most directly establishes the central claim about “Flux (text-to-image model)”?
- Which institution is responsible for the underlying evidence?
- What terminology or title could unlock a more precise catalogue search?
Search terms from this dossier
This entry incorporates text from “Flux (text-to-image model)” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.