How much does FLUX.1 Canny cost via Railwail?

Per-call: €0.05. No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of FLUX.1 Canny?

FLUX.1 Canny supports a unknown context window — enough for typical AI workloads.

How fast is FLUX.1 Canny?

Latency depends on prompt length and load — typically 200ms to 2s for short prompts. We measure p50/p95 in real-time on /rankings.

Is FLUX.1 Canny better than Flux 1.1 Pro Ultra?

It depends on your use case. FLUX.1 Canny (Replicate) and Flux 1.1 Pro Ultra (Black Forest Labs) are both strong choices in image generation. Compare them side-by-side at /compare/flux-1-canny-vs-flux-pro-ultra.

Does FLUX.1 Canny support image input (vision)?

Yes — FLUX.1 Canny accepts image inputs in addition to text. Send images via the standard OpenAI-compatible `messages` array with `image_url` content blocks. Supported formats: image, text.

FLUX.1 Canny

Name: FLUX.1 Canny
Brand: Replicate
SKU: flux-1-canny
Price: 0.05 EUR
Availability: InStock

Replicate

Image Generation

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

Generate with FLUX.1 Canny

Describe what you want and pick a size — the image renders inline.

Size

Result appears here

TL;DR·Last updated May 16, 2026

FLUX.1 Canny is image generation AI model from Replicate, priced at €0.000 per 1M input tokens with a unknown context window.

Try FLUX.1 Canny

Prompt

Aspect Ratio

Quality

Pricing

Price per Generation

Per generation€0.05

API Integration

Use our OpenAI-compatible API to integrate FLUX.1 Canny into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const images = await rw.run("flux-1-canny", "A beautiful sunset over Tokyo");
console.log(images[0].url);

// Or use the image() method for full control
const res = await rw.image("flux-1-canny", "A cat in space", {
  size: "1024x1024",
  n: 1,
});
console.log(res.data[0].url);

Specifications

Price

€0.05

Developer

Replicate

Deep dive — Black Forest Labs's FLUX.1 Canny

About Black Forest Labs

Founded 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.

Visit Black Forest Labs →

Architecture

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.

Parameters: ~12B (FLUX.1 dev backbone) plus ControlNet-style conditioning
Context: 512 tokens

What it can do

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.

Training & License

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.

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

Known limitations