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)

LinkedIn banner: ~4:1

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

Related Skills

• image-generation - MCP-based image generation

• design-system - Design system implementation

• presentation-builder - Presentation creation

API Documentation

• Official Site: https://pollinations.ai

• API Endpoint: https://image.pollinations.ai/prompt/{prompt}

• Models: flux, turbo, stable-diffusion

Tags

#pollinations #image-generation #free #api #url-based #no-signup #creative