FLUX.1 Canny

BilledgenereringTilgængelig
af Black Forest LabsModell-ID: flux-1-canny

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

Pris
0,060 US$/billede
Input → output
Tekst → Billede
Udvikler
Black Forest Labs
Opdateret
23. september 2026
01

Playground

Prøv FLUX.1 Canny

Ingen inputmaske

0,060 US$/billede

Ingen inputmaske til denne model endnu

Dens inputs er endnu ikke dokumenteret. For at ingen kørsel mislykkes på grund af forkert input tilbyder vi ikke en formular her. Vælg i stedet en sammenlignelig model.

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.
03

Om FLUX.1 Canny

Kort sagtFra 23. september 2026

FLUX.1 Canny er en model af Black Forest Labs i kategorien Billedgenerering. På Railwail koster FLUX.1 Canny 0,060 US$ pr. billede.

Baggrund

Om Black Forest Labs

Grundlagt 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.

Besøg 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.

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

Funktioner

  • 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.

Træning og licens

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.

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

Sikkerhedstests: 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.

Kendte begrænsninger

  • 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
04

Priser

Priser i US-dollar. Forbrug debiteres fra forudbetalte credits.
Pr. billede0,060 US$ pr. billede
  • 1 kredit = 0,01 US$

Omkostningsberegner

Prisberegner

I alt

6,00 US$

600 credits

Pr. kørsel

0,06 US$ · 6 credits

Fast pris pr. kørsel, kendt før kørslen starter.

05

API

Kald FLUX.1 Canny med din Railwail API-nøgle. Brug dette model-ID i anmodningen:

Intet bekræftet API-eksempel

Inputene til denne model er ikke dokumenteret endnu.

06

Specifikationer

Model-ID
flux-1-canny
Input
Tekst
Output
Billede
Fakturering
Fastpris, kendt før kørslen
Modelstørrelse
~12B (FLUX.1 dev backbone) plus ControlNet-style conditioning
Licens
FLUX.1 [dev] non-commercial license for the dev weights; FLUX.1 [pro] available only via API. Commercial use requires the pro/enterprise tier.
Katalogelement opdateret
23. september 2026

Tags

  • flux
  • black-forest-labs
  • image-edit
  • controlnet
  • canny
  • structural
07

Anvendelsestilfælde

Hvad det bruges 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
08

Ofte stillede spørgsmål

Hvad er FLUX.1 Canny?

FLUX.1 Canny er en model fra Black Forest Labs i kategorien Billedgenerering.

Hvad koster FLUX.1 Canny på Railwail?

På Railwail koster FLUX.1 Canny 0,060 US$ pr. billede. Prisen kendes før kørslen starter. Forbrug betales fra forudbetalte credits; 1 credit svarer til 0,01 US$.

Hvor hurtig er FLUX.1 Canny?

Der er endnu ikke nok målte kørsler af FLUX.1 Canny på Railwail til at angive en udførelsestid. Det afhænger af inputtet, indstillingerne og belastningen hos provideren.

Er FLUX.1 Canny bedre end FLUX 1.1 Pro?

Det afhænger af opgaven. FLUX.1 Canny (Black Forest Labs) og FLUX 1.1 Pro (Black Forest Labs) er begge modeller i kategorien Billedgenerering. Sammenligningssiden viser deres priser og specifikationer side om side.

Sammenlign FLUX.1 Canny og FLUX 1.1 Pro
09

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