Flux Canny Dev - Image Text Editor

Development-grade edge-aware AI image editing model optimized for experimentation and advanced edge detection workflows. Flux Canny Dev offers comprehensive parameter control for developers and researchers working with edge-based image transformations.

Endpoint

POSThttps://api.1min.ai/api/features

Request Headers

Field	Value
API-KEY	`<api-key>`
Content-Type	`application/json`

Parameters

Parameter	Type	Required	Description
`type`	string	Yes	Feature type identifier. Must be `IMAGE_EDITOR`
`model`	string	Yes	AI model identifier. Must be `black-forest-labs/flux-canny-dev`
`promptObject`	object	Yes	Configuration object containing all image editing parameters

Prompt Object Parameters

Parameter	Type	Required	Description	Default
`imageUrl`	string	Yes	Path to the source image to be edited	-
`prompt`	string	Yes	Text description of the desired image transformation	-
`num_outputs`	number	No	Number of images to generate (1-4)	1
`num_inference_steps`	number	No	Number of denoising steps for quality	28
`guidance`	number	No	How closely to follow the prompt (1.0-20.0)	3.5
`seed`	number	No	Random seed for reproducibility (0-4294967295)	0
`output_quality`	number	No	Output image quality (1-100)	80
`disable_safety_checker`	boolean	No	Disable content safety filtering	false
`megapixels`	string	No	Output resolution (1, 0.25)	1
`format`	string	No	Output image format (webp, jpg, png)	webp

Code Examples

cURL
JavaScript
Python

curl -X POST "https://api.1min.ai/api/features" \
-H "API-KEY: <api-key>" \
-H "Content-Type: application/json" \
-d '{
  "type": "IMAGE_EDITOR",
  "model": "black-forest-labs/flux-canny-dev",
  "promptObject": {
    "imageUrl": "development/images/2025_02_16_15_42_40_711_human.jpg",
    "prompt": "Create detailed line art with enhanced edge detection",
    "num_outputs": 2,
    "num_inference_steps": 50,
    "guidance": 5.0,
    "seed": 42,
    "output_quality": 90,
    "disable_safety_checker": false,
    "megapixels": "1",
    "format": "webp"
  }
}'

fetch('https://api.1min.ai/api/features', {
method: 'POST',
headers: {
  'Content-Type': 'application/json',
  'API-KEY': 'YOUR_API_KEY'
},
body: JSON.stringify({
  type: 'IMAGE_EDITOR',
  model: 'black-forest-labs/flux-canny-dev',
  promptObject: {
    imageUrl: 'development/images/2025_02_16_15_42_40_711_human.jpg',
    prompt: 'Create detailed line art with enhanced edge detection',
    num_outputs: 2,
    num_inference_steps: 50,
    guidance: 5.0,
    seed: 42,
    output_quality: 90,
    disable_safety_checker: false,
    megapixels: '1',
    format: 'webp'
  }
})
})

import requests

url = "https://api.1min.ai/api/features"
headers = {
"Content-Type": "application/json",
"API-KEY": "YOUR_API_KEY"
}

data = {
"type": "IMAGE_EDITOR",
"model": "black-forest-labs/flux-canny-dev",
"promptObject": {
  "imageUrl": "development/images/2025_02_16_15_42_40_711_human.jpg",
  "prompt": "Create detailed line art with enhanced edge detection",
  "num_outputs": 2,
  "num_inference_steps": 50,
  "guidance": 5.0,
  "seed": 42,
  "output_quality": 90,
  "disable_safety_checker": False,
  "megapixels": "1",
  "format": "webp"
}
}

response = requests.post(url, headers=headers, json=data)

Interactive Playground

API Playground

https://api.1min.ai/api/features

AI Model *

Image URL *

Path to the source image to be edited

Prompt *

Description of desired edge-aware edits

Number of Outputs

Number of images to generate (1-4)

Inference Steps

Number of denoising steps (1-100)

Guidance

How closely to follow prompt (1.0-20.0)

Seed

Seed for reproducible results (0-4294967295)

Output Quality

Image quality level (1-100)

Disable Safety Checker

Disable content safety filtering

Megapixels

Output image resolution

Format

Output image format

Generated cURL Command:

curl -X POST "https://api.1min.ai/api/features" \
  -H "API-KEY: <your-api-key>" \
  -H "Content-Type: application/json" \
  -d '{
  "type": "IMAGE_EDITOR",
  "model": "black-forest-labs/flux-canny-dev",
  "promptObject": {
    "imageUrl": "development/images/2025_02_16_15_42_40_711_human.jpg",
    "prompt": "Create detailed line art with enhanced edge detection",
    "num_outputs": 1,
    "num_inference_steps": 28,
    "guidance": 3.5,
    "seed": 42,
    "output_quality": 80,
    "disable_safety_checker": false,
    "megapixels": "1",
    "format": "webp"
  }
}'

Response Format

Success Response (200)

{
"aiRecord": {
  "uuid": "ac32acf4-0b2e-400e-9edf-cb3086c30a2c",
  "userId": "c937fbcc-fa8f-4565-a440-c4d87f56fcb2",
  "teamId": "a4e176b2-dabb-451e-9c58-62b451fa9630",
  "teamUser": {
    "teamId": "a4e176b2-dabb-451e-9c58-62b451fa9630",
    "userId": "c937fbcc-fa8f-4565-a440-c4d87f56fcb2",
    "userName": "John Doe",
    "userAvatar": "https://lh3.googleusercontent.com/a/ACg8ocLqgsNsHRfmWF9d-E1RvJetVsEzxNOsOg-NXWNTpMxLDPJbwELI=s96-c",
    "status": "ACTIVE",
    "role": "ADMIN",
    "creditLimit": 100000000,
    "usedCredit": 8164605,
    "createdAt": "2025-10-20T04:13:40.847Z",
    "createdBy": "SYSTEM",
    "updatedAt": "2025-10-27T05:06:17.651Z",
    "updatedBy": "SYSTEM"
  },
  "model": "black-forest-labs/flux-canny-dev",
  "type": "IMAGE_EDITOR",
  "metadata": null,
  "rating": null,
  "feedback": null,
  "conversationId": null,
  "status": "SUCCESS",
  "createdAt": "2025-10-27T08:43:17.647Z",
  "aiRecordDetail": {
    "promptObject": {
      "seed": 42,
      "format": "webp",
      "prompt": "Create detailed line art with enhanced edge detection",
      "imageUrl": "development/images/2025_02_16_15_42_40_711_human.jpg",
      "guidance": 5.0,
      "num_outputs": 2,
      "output_quality": 90,
      "disable_safety_checker": false,
      "megapixels": "1",
      "num_inference_steps": 50
    },
    "resultObject": [
      "development/images/2025_10_27_15_43_26_155_702365.webp",
      "development/images/2025_10_27_15_43_26_156_702366.webp"
    ],
    "responseObject": {}
  },
  "additionalData": null,
  "temporaryUrl": [
    "https://s3.us-east-1.amazonaws.com/asset.1min.ai/development/images/2025_10_27_15_43_26_155_702365.webp?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIAVRUVQEFIHSKAXGE7%2F20251027%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20251027T084331Z&X-Amz-Expires=604800&X-Amz-Signature=5a66ff004deed5654ac44a823be7c4d1c8850503cb66a4445ce745c5caec5525&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject",
    "https://s3.us-east-1.amazonaws.com/asset.1min.ai/development/images/2025_10_27_15_43_26_156_702366.webp?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIAVRUVQEFIHSKAXGE7%2F20251027%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20251027T084331Z&X-Amz-Expires=604800&X-Amz-Signature=5a66ff004deed5654ac44a823be7c4d1c8850503cb66a4445ce745c5caec5525&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject"
  ]
}
}

Use Cases

Edge detection research: Experiment with different edge detection algorithms and parameters
Line art generation: Create multiple variations of line drawings from photographs
Technical drawing development: Generate precise technical illustrations with edge emphasis
Artistic experimentation: Explore creative edge-based transformations and effects
Batch processing workflows: Generate multiple edge-processed variations for comparison
Computer vision preprocessing: Prepare images for edge-based computer vision tasks

Tips for Best Results

Generate multiple outputs: Use 2-4 variations to explore different edge interpretations
Higher inference steps: Use 50-100 steps for research-quality edge detection
Fine-tune quality settings: Higher quality (90-100) for detailed edge work
Experiment with guidance: Lower values (2-4) for subtle edges, higher (6-10) for bold lines
Use appropriate resolution: 1 MP for detailed edge work, 0.25 MP for fast iteration
WebP for line art: Excellent compression for edge-heavy content
Document edge parameters: Save successful configurations for consistent results
Progressive refinement: Start with lower settings, increase for final detailed output

Endpoint​

Request Headers​

Parameters​

Prompt Object Parameters​

Code Examples​

Interactive Playground​