flux-pro-1.0-canny-finetuned

Technical Specifications of flux-pro-1-0-canny-finetuned

What is flux-pro-1-0-canny-finetuned?

flux-pro-1-0-canny-finetuned is CometAPI’s platform identifier for a FLUX.1 [pro]-based image generation model that combines Canny edge control with custom fine-tuning. In practice, this means the model can follow the structural outline extracted from an input image while also applying a trained fine-tune so outputs better match a specific subject, visual identity, or stylistic domain.

The “canny” part refers to edge-based conditioning: instead of generating an image from text alone, the model uses edge information to preserve composition, layout, and object boundaries from a reference image. This is useful when you want stronger control over pose, silhouette, scene geometry, or graphic structure than prompt-only generation usually provides.

The “finetuned” part indicates that inference is tied to a custom-trained fine-tune, typically referenced by a finetune_id. That lets teams steer the base FLUX.1 [pro] model toward a particular brand look, product style, character identity, or recurring art direction while still benefiting from control-image guidance.

As of Black Forest Labs’ October 31, 2025 deprecation notice, flux-pro-1.0-canny-finetuned is listed among discontinued finetuning-related endpoints. If you are integrating it through CometAPI, availability depends on CometAPI’s routing and support layer rather than the original upstream endpoint alone.

Main features of flux-pro-1-0-canny-finetuned

• Canny edge guidance: Uses edge maps or edge-derived structure from an input image to keep composition and contours more consistent than prompt-only generation.
• Custom fine-tune support: Designed to work with a trained finetune_id, enabling outputs aligned to a custom visual domain, brand style, or subject-specific adaptation.
• Structure-preserving image transformation: Well suited for workflows where you want to change texture, styling, mood, or rendering quality without losing the original layout.
• High-end FLUX.1 [pro] quality base: Inherits the premium FLUX.1 [pro] generation stack, which is positioned as a high-quality image model family in the Black Forest Labs ecosystem.
• Creative control for production pipelines: Helpful for concept art, marketing asset variation, product visualization, and repeatable visual generation where both style consistency and spatial control matter. This is an inference based on the model’s control-image and fine-tune design.
• Fine-tune strength-style balancing: Related FLUX.1 [pro] fine-tuned endpoints expose control over how strongly the fine-tune affects the final image, which is useful when balancing adherence to the base structure versus custom style influence.
• Deprecation-aware integration considerations: Since the upstream finetuning API and flux-pro-1.0-canny-finetuned were deprecated by Black Forest Labs, teams should validate current support, latency, and fallback behavior before depending on it in production.

How to access and integrate flux-pro-1-0-canny-finetuned

Step 1: Sign Up for API Key

Sign up for CometAPI and create an API key from your dashboard. Store the key securely in an environment variable on your server. Avoid exposing production API keys in client-side apps or public repositories.

Step 2: Send Requests to flux-pro-1-0-canny-finetuned API

Use CometAPI’s OpenAI-compatible API format and set the model field to flux-pro-1-0-canny-finetuned.

curl https://api.cometapi.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "flux-pro-1-0-canny-finetuned",
    "input": "Generate a refined image based on the provided canny-guided composition and prompt."
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.COMETAPI_API_KEY,
  baseURL: "https://api.cometapi.com/v1"
});

const response = await client.responses.create({
  model: "flux-pro-1-0-canny-finetuned",
  input: "Generate a refined image based on the provided canny-guided composition and prompt."
});

console.log(response);

Step 3: Retrieve and Verify Results

Read the API response and extract the generated output, asset URLs, or structured result fields your application needs. In production, validate response status, handle retries, log request IDs, and verify that the returned result matches your expected format and quality requirements before exposing it to end users.