Pollinations.ai Image Generation

Free, open-source AI image generation through simple URL parameters. No API key or signup required.

When to use this skill

Quick prototyping

Generate placeholder images instantly

Marketing assets

Create hero images, banners, social media content

Creative exploration

Test multiple styles and compositions rapidly

No-budget projects

Free alternative to paid image generation services

Automated workflows

Script-friendly URL-based API

Instructions

Step 1: Understand the API Structure

Pollinations.ai uses a simple URL-based API:

https://image.pollinations.ai/prompt/{YOUR_PROMPT}?{PARAMETERS}

No authentication required

- just construct the URL and fetch the image.

Available Parameters

:

width

/

height

Resolution (default: 1024x1024)

model

AI model (

flux

,

turbo

,

stable-diffusion

)

seed

Number for reproducible results

nologo

:

true

to remove watermark (if supported)

enhance

:

true

for automatic prompt enhancement

Step 2: Craft Your Prompt

Use descriptive prompts with specific details:

Good prompt structure

:

[Subject], [Style], [Lighting], [Mood], [Composition], [Quality modifiers]

Example

:

A father welcoming a beautiful holiday, warm golden hour lighting,

cozy interior background with festive decorations, 8k resolution,

highly detailed, cinematic depth of field

Prompt styles

:

Photorealistic

"photorealistic shot, 8k resolution, highly detailed, cinematic"

Illustrative

"digital illustration, soft pastel colors, disney style animation"
Minimalist: "minimalist vector art, flat design, simple geometric shapes" Step 3: Generate via URL (Browser Method) Simply open the URL in a browser or use curl :

Basic generation

curl "https://image.pollinations.ai/prompt/A_serene_mountain_landscape" -o mountain.jpg

With parameters

curl "https://image.pollinations.ai/prompt/A_serene_mountain_landscape?width=1920&height=1080&model=flux&seed=42" -o mountain-hd.jpg Step 4: Generate and Save (Python Method) For automation and file management: import requests from urllib . parse import quote def generate_image ( prompt , output_file , width = 1920 , height = 1080 , model = "flux" , seed = None ) : """ Generate image using Pollinations.ai and save to file Args: prompt: Description of the image to generate output_file: Path to save the image width: Image width in pixels height: Image height in pixels model: AI model ('flux', 'turbo', 'stable-diffusion') seed: Optional seed for reproducibility """

Encode prompt for URL

encoded_prompt

quote ( prompt ) url = f"https://image.pollinations.ai/prompt/ { encoded_prompt } "

Build parameters

params

{ "width" : width , "height" : height , "model" : model , "nologo" : "true" } if seed : params [ "seed" ] = seed

Generate and save

print ( f"Generating: { prompt [ : 50] } ..." ) response = requests . get ( url , params = params ) if response . status_code == 200 : with open ( output_file , "wb" ) as f : f . write ( response . content ) print ( f"✓ Saved to { output_file } " ) return True else : print ( f"✗ Error: { response . status_code } " ) return False

Example usage

generate_image

(

prompt

=

"A father welcoming a beautiful holiday, warm lighting, festive decorations"

,

output_file

=

"holiday_father.jpg"

,

width

=

1920

,

height

=

1080

,

model

=

"flux"

,

seed

=

12345

)

Step 5: Batch Generation

Generate multiple variations:

prompts

=

[

"photorealistic shot of a father at front door, warm lighting, festive decorations"

,

"digital illustration of a father in snow, magical winter wonderland, disney style"

,

"minimalist silhouette of father and child, holiday fireworks, flat design"

]

for

i

,

prompt

in

enumerate

(

prompts

)

:

generate_image

(

prompt

=

prompt

,

output_file

=

f"variant_

{

i

+

1

}

.jpg"

,

width

=

1920

,

height

=

1080

,

model

=

"flux"

)

Step 6: Document Your Generations

Save metadata for reproducibility:

import

json

from

datetime

import

datetime

metadata

=

{

"prompt"

:

prompt

,

"model"

:

"flux"

,

"width"

:

1920

,

"height"

:

1080

,

"seed"

:

12345

,

"output_file"

:

"holiday_father.jpg"

,

"timestamp"

:

datetime

.

now

(

)

.

isoformat

(

)

}

with

open

(

"generation_metadata.json"

,

"w"

)

as

f

:

json

.

dump

(

metadata

,

f

,

indent

=

2

)

Examples

Example 1: Hero Image for Website

generate_image

(

prompt

=

"serene mountain landscape at sunset, wide 16:9, minimal style, soft gradients in blue tones, clean lines, modern aesthetic"

,

output_file

=

"hero-image.jpg"

,

width

=

1920

,

height

=

1080

,

model

=

"flux"

)

Expected output

16:9 landscape image, minimal style, blue color palette

Example 2: Product Thumbnail

generate_image

(

prompt

=

"futuristic dashboard UI, 1:1 square, clean interface, soft lighting, professional feel, dark theme, subtle glow effects"

,

output_file

=

"product-thumb.jpg"

,

width

=

1024

,

height

=

1024

,

model

=

"flux"

)

Expected output

Square thumbnail, dark theme, app store ready
Example 3: Social Media Banner
generate_image
(
prompt
=
"LinkedIn banner for SaaS startup, modern gradient background, abstract geometric shapes, colors from purple to blue, space for text on left side"
,
output_file
=
"linkedin-banner.jpg"
,
width
=
1584
,
height
=
396
,
model
=
"flux"
)
Expected output: LinkedIn-optimized dimensions (1584x396), text-safe zone Example 4: Batch Variations with Seeds

Generate 4 variations of the same prompt with different seeds

base_prompt

"A father welcoming a beautiful holiday, cinematic lighting"

for

seed

in

[

100

,

200

,

300

,

400

]

:

generate_image

(

prompt

=

base_prompt

,

output_file

=

f"variation_seed_

{

seed

}

.jpg"

,

width

=

1920

,

height

=

1080

,

model

=

"flux"

,

seed

=

seed

)

Expected output

4 similar images with subtle variations

Best practices

Use specific prompts

Include style, lighting, mood, and quality modifiers

Specify dimensions early

Prevents unintended cropping

Use seeds for consistency

Same seed + prompt = same image

Model selection

:

flux

Highest quality, slower

turbo

Fast iterations

stable-diffusion

Balanced

Save metadata

Track prompts, seeds, and parameters for reproducibility

Batch similar requests

Generate style sets with consistent parameters

URL encode prompts

Use

urllib.parse.quote()

for special characters

Common pitfalls

Vague prompts

Add specific details about style, lighting, and composition

Ignoring aspect ratios

Check target platform requirements (Instagram 1:1, LinkedIn 1584x396, etc.)

Overly complex scenes

Simplify for clarity and better results

Not saving metadata

Difficult to reproduce or iterate on successful images

Forgetting URL encoding

Special characters break URLs

Troubleshooting

Issue: Inconsistent outputs

Cause

No seed specified
Solution: Use a fixed seed for reproducible results generate_image ( prompt = "..." , seed = 12345 , . . . )

Same output every time

Issue: Wrong aspect ratio

Cause

Incorrect width/height parameters
Solution: Use platform-specific dimensions

Instagram: 1:1

generate_image ( prompt = "..." , width = 1080 , height = 1080 )

generate_image ( prompt = "..." , width = 1584 , height = 396 )

YouTube thumbnail: 16:9

generate_image

(

prompt

=

"..."

,

width

=

1280

,

height

=

720

)

Issue: Image doesn't match brand colors

Cause

No color specification in prompt

Solution

Include HEX codes or color names

prompt

=

"landscape with brand colors deep blue #2563EB and purple #8B5CF6"

Issue: Request fails (HTTP error)

Cause

Network issue or service downtime
Solution: Add retry logic import time def generate_with_retry ( prompt , output_file , max_retries = 3 ) : for attempt in range ( max_retries ) : if generate_image ( prompt , output_file ) : return True print ( f"Retry { attempt + 1 } / { max_retries } ..." ) time . sleep ( 2 ) return False Output format

Image Generation Report

Request

Prompt

[full prompt text]

Model

flux

Dimensions

1920x1080

**
Seed
**: 12345

Output Files 1. hero-image-v1.jpg - Primary variant 2. hero-image-v2.jpg - Alternative style 3. hero-image-v3.jpg - Different lighting

Metadata

Generated: 2026-02-13T14:30:00Z

Iterations: 3

Selected: hero-image-v1.jpg

Usage Notes

Best for: Website hero section

Format: JPEG, 1920x1080

Reproducible: Yes (seed: 12345) Multi-Agent Workflow Validation & Quality Check Round 1 (Orchestrator - Claude) : Validate prompt completeness Check dimension requirements Verify seed consistency Round 2 (Executor - Codex) : Execute generation script Save files with proper naming Generate metadata JSON Round 3 (Analyst - Gemini) : Review style consistency Check brand alignment Suggest prompt improvements Agent Roles Agent Role Tools Claude Prompt engineering, quality validation Write, Read Codex Script execution, batch processing Bash, Write Gemini Style analysis, brand consistency check Read, ask-gemini Example Multi-Agent Workflow

1. Claude: Generate prompts and script

2. Codex: Execute generation

bash -c "python generate_images.py"

3. Gemini: Review outputs

ask-gemini

"@outputs/ Analyze brand consistency of generated images"

Metadata

Version

Current Version

1.0.0

Last Updated

2026-02-13
Compatible Platforms: Claude, ChatGPT, Gemini, Codex

pollinations-ai

安装

Basic generation

With parameters

Encode prompt for URL

encoded_prompt

Build parameters

params

Generate and save

Example usage

Generate 4 variations of the same prompt with different seeds

base_prompt

Same output every time

Instagram: 1:1

LinkedIn banner: ~4:1

YouTube thumbnail: 16:9

Request

[full prompt text]

flux

1920x1080

Metadata

Generated: 2026-02-13T14:30:00Z

Iterations: 3

Usage Notes

Best for: Website hero section

Format: JPEG, 1920x1080

1. Claude: Generate prompts and script

2. Codex: Execute generation

3. Gemini: Review outputs