pollinations-ai

pollinations-ai

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 #creativeWeekly Installs10.2KRepositorysupercent-io/sk…templateGitHub Stars53First SeenFeb 13, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex10.2Kgemini-cli10.2Kopencode10.2Kgithub-copilot10.2Kcursor10.2Kamp10.2K