FLUX.1 Canny

BildegenereringTilgjengelig
av Black Forest LabsModell-ID: flux-1-canny

FLUX structural control via Canny edge maps. Preserve composition while restyling.

Pris
0,060 USD/bilde
Input → output
Tekst → Bilde
Utvikler
Black Forest Labs
Oppdatert
23. september 2026
01

Playground

Prøv FLUX.1 Canny

Ingen innmaskering

0,060 USD/bilde

Ingen innmaskering for denne modellen ennå

Inndataene er ikke dokumentert ennå. For at ingen kjøring skal mislykkes på grunn av feil inndata, tilbyr vi ikke et skjema her. Velg en sammenlignbar modell i stedet.

02

Examples

Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
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Om FLUX.1 Canny

Kort sagtPer 23. september 2026

FLUX.1 Canny er en modell fra Black Forest Labs i kategorien Bildegenerering. På Railwail koster FLUX.1 Canny 0,060 USD per bilde.

Bakgrunn

Om Black Forest Labs

Grunnlagt 2024 · Freiburg, Germany

Black Forest Labs was founded in August 2024 in Freiburg, Germany by Robin Rombach, Patrick Esser, Andreas Blattmann and Dominik Lorenz, the core team that previously developed Latent Diffusion Models (LDM) and Stable Diffusion at LMU Munich and Stability AI. The team's earlier work includes the seminal 'High-Resolution Image Synthesis with Latent Diffusion Models' (CVPR 2022) and the Stable Diffusion 1.x/2.x and SDXL releases. The company raised a $31M seed round led by Andreessen Horowitz in 2024 and launched the FLUX.1 model family (pro, dev, schnell) along with specialist Tools models (Fill, Canny, Depth, Redux). Their stated goal is to be the leading open foundation-model lab for generative media in Europe.

Besøk Black Forest Labs

Arkitektur

Rectified-flow Transformer (DiT) with Canny-edge structural conditioning

FLUX.1 Canny is part of the FLUX.1 Tools family released by Black Forest Labs in late 2024. It extends the base FLUX.1 [dev] / [pro] rectified-flow transformer (a Diffusion Transformer trained with the flow-matching objective rather than classical DDPM) with a structural conditioning branch that ingests a Canny edge map of a reference image. The conditioning is implemented as a ControlNet-like adapter that injects edge features into the DiT blocks via cross-attention residuals, so that the output respects the line structure of the input while the prompt controls semantics, style and lighting. The backbone uses T5-XXL plus CLIP-L as text encoders and operates in a learned latent space at 16x downsampling. Sampling typically uses 28-50 flow-matching steps with the Euler or DPM-Solver sampler. The model was distilled and fine-tuned on edge-conditioned pairs derived from a large licensed image corpus.

Parametere
~12B (FLUX.1 dev backbone) plus ControlNet-style conditioning
Kontekst
512 tokens

Evner

  • Edge-conditioned image generation that preserves line structure of a reference
  • Works at up to 2 MP output resolution
  • Strong photorealism inherited from FLUX.1 [dev]/[pro]
  • Excellent prompt adherence via T5-XXL text encoder
  • Useful for redesigns, restyles and product re-skins on a fixed silhouette
  • Compatible with the rest of the FLUX Tools family (Fill, Depth, Redux)
  • Best for: e-commerce restyling, architectural restyling, fashion mockups, design iteration.

Trening og lisens

Fine-tuned from FLUX.1 base weights on pairs of images with extracted Canny edge maps. Exact dataset is not disclosed; Black Forest Labs states licensed and curated data.

Lisens: FLUX.1 [dev] non-commercial license for the dev weights; FLUX.1 [pro] available only via API. Commercial use requires the pro/enterprise tier.

Sikkerhetstesting: Standard Black Forest Labs safety filters; the model inherits guardrails of the base FLUX.1 family including filters for CSAM, non-consensual intimate imagery and IP-protected content.

Kjente begrensninger

  • Requires a precomputed Canny edge map as input
  • Quality of output is strongly dependent on the source edges
  • Dev weights are non-commercial only
  • Slower than schnell-tier base model
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Priser

Priser i amerikanske dollar. Bruk belastes fra forhåndsbetalt kreditt.
Per bilde0,060 USD per bilde
  • 1 kreditt = 0,01 USD

Kostnadsberegner

Prisberegner

Totalt

6,00 USD

600 credits

Per kjøring

0,06 USD · 6 credits

Fast pris per kjøring, kjent før kjøringen starter.

05

API

Ring FLUX.1 Canny med din Railwail API-nøkkel. Bruk denne modell-IDen i forespørselen:

Ingen verifisert API-eksempel

Inngangene til denne modellen er ikke dokumentert ennå.

06

Spesifikasjoner

Modell-ID
flux-1-canny
Inndata
Tekst
Utdata
Bilde
Fakturering
Fastpris, kjent før kjøring
Modellstørrelse
~12B (FLUX.1 dev backbone) plus ControlNet-style conditioning
Lisens
FLUX.1 [dev] non-commercial license for the dev weights; FLUX.1 [pro] available only via API. Commercial use requires the pro/enterprise tier.
Katalogoppføring oppdatert
23. september 2026

Merkelapper

  • flux
  • black-forest-labs
  • image-edit
  • controlnet
  • canny
  • structural
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Brukstilfeller

Hva det brukes til

  • E-commerce product restyling on fixed silhouettes
  • Architectural facade restyling
  • Fashion try-on and material swap
  • Logo-to-render exploration
  • Coloring-book-style controlled generation
  • Storyboard line-to-render conversion
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Ofte stilte spørsmål

Hva er FLUX.1 Canny?

FLUX.1 Canny er en modell fra Black Forest Labs i kategorien Bildegenerering.

Hvor mye koster FLUX.1 Canny på Railwail?

På Railwail koster FLUX.1 Canny 0,060 USD per bilde. Prisen er kjent før kjøringen starter. Bruk betales fra forhåndskjøpte kreditter; 1 kreditt tilsvarer 0,01 USD.

Hvor rask er FLUX.1 Canny?

Det finnes ennå ikke nok målte kjøringer av FLUX.1 Canny på Railwail til å angi en kjøretid. Det avhenger av inndataene, innstillingene og belastningen hos leverandøren.

Er FLUX.1 Canny bedre enn FLUX 1.1 Pro?

Det avhenger av oppgaven. FLUX.1 Canny (Black Forest Labs) og FLUX 1.1 Pro (Black Forest Labs) er begge modeller i kategorien Bildegenerering. Sammenligningssiden viser prisene og spesifikasjonene deres side ved side.

Sammenlign FLUX.1 Canny og FLUX 1.1 Pro
09

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