Image Generation — Генерация и редактирование изображений с помощью ИИ

--- name: image-generation description: | AI-powered image generation and editing using Google Gemini, Google Imagen, and OpenAI models. Generate images from text descriptions, edit existing images, create logos/stickers, apply style transfers, and produce product mockups. Use this skill when the user requests: - Image generation from text descriptions - Image editing or modifications - Logos, stickers, or graphic design assets - Product mockups or visualizations - Style transfers or artistic effects - Iterative image refinement Available models: - Google Gemini: gemini-2.5-flash-image (Nano Banana), gemini-3-pro-image-preview (Nano Banana Pro) - Google Imagen: imagen-4.0-generate-001, imagen-4.0-ultra-generate-001, imagen-4.0-fast-generate-001 - OpenAI: gpt-image-1.5 (recommended), gpt-image-1, dall-e-3, dall-e-2 Inspired by: https://github.com/EveryInc/every-marketplace/tree/main/plugins/compounding-engineering/skills/gemini-imagegen allowed-tools: ["Bash", "Read", "Write", "AskUserQuestion", "WebFetch"] --- # Image Generation > **Important (December 2025)**: The `google-generativeai` package has been deprecated. > This skill now uses the `google-genai` SDK. If upgrading from older code, see the > [migration guide](https://ai.google.dev/gemini-api/docs/migrate). ## Purpose This skill enables AI-powered image generation and editing through Google's Gemini image models and OpenAI's DALL-E models. Create photorealistic images, illustrations, logos, stickers, and product mockups from natural language descriptions. Edit existing images with text instructions, apply style transfers, and refine outputs through iterative conversation. **Attribution:** This skill is inspired by the `gemini-imagegen` skill from [Every Marketplace](https://github.com/EveryInc/every-marketplace/tree/main/plugins/compounding-engineering/skills/gemini-imagegen) by Every Inc. ## When to Use This skill should be invoked when the user asks to: - Generate images from text descriptions ("create an image of...", "generate a picture...") - Create logos, icons, or stickers ("design a logo for...", "make a sticker...") - Edit or modify existing images ("change the background to...", "add... to this image") - Apply artistic styles or effects ("make it look like...", "stylize as...") - Create product mockups or visualizations ("product photo of...", "mockup showing...") - Refine or iterate on images ("make it more...", "adjust the...", "try again with...") - Generate variations with different styles or compositions ## Available Models ### Google Gemini Models (Nano Banana) 1. **gemini-2.5-flash-image** ("Nano Banana") - Resolution: 1K (1024px), supports 2K - Aspect ratios: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 - Best for: Speed, high-volume operations, rapid iteration, image editing - Use when: Quick prototypes, multiple variations, time-sensitive requests - Cost: ~$0.039 per image (~$30/million output tokens) 2. **gemini-3-pro-image-preview** ("Nano Banana Pro") - Resolution: 1K default, supports 2K and 4K - Aspect ratios: Same as Flash - Best for: Professional assets, complex instructions, highest quality - Use when: Final deliverables, detailed compositions, text-heavy designs - Special features: - Google Search grounding for real-time data visualization - "Thinking" mode with interim composition refinement - Up to 14 reference images (6 objects, 5 humans for character consistency) - Advanced text rendering ### Google Imagen 4 Family (New) 3. **imagen-4.0-fast-generate-001** ("Imagen 4 Fast") - Resolution: Standard - Best for: Rapid generation, high-volume tasks - Use when: Speed is priority, budget-conscious - Cost: $0.02 per image - Note: Text-only input (no image editing) 4. **imagen-4.0-generate-001** ("Imagen 4") - Resolution: Up to 2K - Best for: High-quality photorealistic images, excellent text rendering - Use when: Professional quality needed, text in images - Features: Significant improvements in text rendering over previous Imagen models 5. **imagen-4.0-ultra-generate-001** ("Imagen 4 Ultra") - Resolution: Up to 2K - Best for: Highest quality, detailed visuals - Use when: Maximum quality is essential (one image at a time) - Limitation: Only generates one image per request ### OpenAI GPT Image Models 6. **gpt-image-1.5** (Recommended - December 2025) - Resolution: 1024x1024, 1536x1024, 1024x1536, or auto - Best for: Production-quality visuals, precise editing, character consistency - Use when: Professional design, iterative workflows, text-heavy images - Features: - 4x faster than gpt-image-1, 20% lower cost - Built-in reasoning and world knowledge - Precise logo & face preservation during edits - Excellent text rendering (crisp lettering, dense text) - Complex structured visuals (infographics, diagrams, multi-panel) - Streaming support - Output formats: png, jpeg, webp (with compression control) - Transparency: transparent, opaque, or auto background 7. **gpt-image-1** (April 2025) - Resolution: Up to 4096x4096 - Best for: High-resolution images, creative workflows - Use when: Maximum resolution needed - Cost: ~$0.02 (low), ~$0.07 (medium), ~$0.19 (high) per image - Output formats: png, jpeg, webp - Note: Single image per request, no inpainting ### Legacy OpenAI DALL-E Models 8. **dall-e-3** - Resolution: 1024x1024, 1024x1792, 1792x1024 - Best for: Creative interpretations, artistic renders - Use when: Natural artistic style preferred - Note: Automatic prompt expansion 9. **dall-e-2** - Resolution: 1024x1024, 512x512, 256x256 - Best for: Faster generation, lowest cost, variations - Use when: Budget-conscious, simpler images - Unique feature: Can generate variations of existing images ## Model Selection Logic Ask the user or use this decision tree: ``` Need image editing or iterative refinement? ├─ Yes → gpt-image-1.5 (best editing) or gemini-2.5-flash-image (multi-turn chat) └─ No → Text-to-image only ├─ Need highest quality? │ ├─ Text rendering critical → gpt-image-1.5 or imagen-4.0-generate-001 │ ├─ Maximum resolution (4K) → gemini-3-pro-image-preview │ ├─ Ultra quality (single image) → imagen-4.0-ultra-generate-001 │ └─ Character consistency → gpt-image-1.5 or gemini-3-pro-image-preview ├─ Need speed/volume? │ ├─ Cheapest → imagen-4.0-fast-generate-001 ($0.02) │ └─ Fast + editing → gemini-2.5-flash-image └─ Balanced default → gpt-image-1.5 (recommended) ``` **Quick Reference:** - **Best overall**: `gpt-image-1.5` - fast, affordable, great editing & text - **Best for text rendering**: `gpt-image-1.5` or `imagen-4.0-generate-001` - **Best for 4K resolution**: `gemini-3-pro-image-preview` - **Cheapest per image**: `imagen-4.0-fast-generate-001` ($0.02) - **Best for reference images**: `gemini-3-pro-image-preview` (up to 14 refs) - **Best for iterative editing**: `gpt-image-1.5` (face/logo preservation) If the user has specific model preference, use that. ## Capabilities 1. **Text-to-Image Generation**: Create images from detailed text descriptions 2. **Image Editing**: Modify existing images with text instructions 3. **Style Transfer**: Apply artistic styles, filters, and effects 4. **Logo & Sticker Design**: Generate branded assets with specific styles 5. **Product Mockups**: Create professional product photography and presentations 6. **Multi-turn Refinement**: Iteratively improve images through conversation 7. **Aspect Ratio Control**: Generate images in various formats (square, portrait, landscape, wide) 8. **Reference-based Generation**: Use existing images as compositional references (Gemini Pro) ## Instructions ### Step 1: Understand the Request Analyze the user's request to determine: - **Type**: Text-to-image, image editing, style transfer, logo/sticker, mockup - **Subject**: What should be in the image - **Style**: Photorealistic, illustration, artistic, minimalist, etc. - **Details**: Colors, lighting, composition, mood, specific elements - **Format**: Aspect ratio, resolution requirements - **Urgency**: Speed vs. quality trade-off ### Step 2: Select Model Based on requirements: - **High quality + complexity** → `gemini-3-pro-image-preview` - **Speed + iterations** → `gemini-2.5-flash-image` - **DALL-E preference** → `dall-e-3` or `dall-e-2` If unclear, use `AskUserQuestion` tool to clarify model preference. ### Step 3: Craft Effective Prompt Build a detailed prompt following these patterns: **For Photorealistic Images:** ``` [Subject], [camera details], [lighting], [mood/atmosphere], [composition] Example: "Close-up portrait of a woman, 85mm lens, soft golden hour lighting, serene mood, shallow depth of field, professional photography" ``` **For Illustrations/Art:** ``` [Subject], [art style], [color palette], [details], [mood] Example: "Kawaii cat sticker, bold black outlines, cel-shading, pastel colors, cute expression, chibi style" ``` **For Logos:** ``` [concept], [style], [elements], [colors], [context] Example: "Tech startup logo, minimalist geometric design, abstract network nodes, blue and silver gradient, professional, vector style" ``` **For Product Photography:** ``` [product], [setting], [lighting], [presentation], [context] Example: "Wireless earbuds, white background, studio lighting, 3/4 angle view, clean minimal composition, e-commerce product shot" ``` **Key principles:** - Be specific and detailed - Include lighting, composition, and mood - Specify style clearly (photorealistic, illustration, etc.) - Mention camera/lens for photorealistic (85mm, wide angle, macro) - For text in images, use Pro model and specify exact text ### Step 4: Implement API Call #### For Gemini Models: > **Note**: The `google.generativeai` package has been deprecated. Use `google.genai` instead. > See migration guide: https://ai.google.dev/gemini-api/docs/migrate ```python from google import genai from google.genai import types from pathlib import Path # Initialize client (uses GEMINI_API_KEY or GOOGLE_API_KEY env var automatically) client = genai.Client() # Basic text-to-image response = client.models.generate_content( model="gemini-2.5-flash-image", # or gemini-3-pro-image-preview contents=prompt_text, config=types.GenerateContentConfig( response_modalities=["TEXT", "IMAGE"], # Optional configurations: # image_config=types.ImageConfig( # aspect_ratio="1:1", # 1:1, 3:4, 4:3, 9:16, 16:9, 21:9 # image_size="1K", # 1K, 2K, 4K (Pro only) # ) ) ) # Extract and save image for part in response.parts: if part.text is not None: print(part.text) elif part.inline_data is not None: image = part.as_image() image.save("output.png") # For image editing (pass existing image): from PIL import Image image = Image.open("input.png") response = client.models.generate_content( model="gemini-2.5-flash-image", contents=[image, "Make the background a sunset scene"], config=types.GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]) ) # For multi-turn refinement (use chat): chat = client.chats.create( model="gemini-2.5-flash-image", config=types.GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]) ) response1 = chat.send_message("A futuristic city skyline") response2 = chat.send_message("Add more neon lights and flying cars") ``` #### For Google Imagen 4 Models: ```python from google import genai from google.genai import types # Initialize client (uses GEMINI_API_KEY or GOOGLE_API_KEY env var automatically) client = genai.Client() # Imagen 4 text-to-image (no editing support) # Also available: imagen-4.0-fast-generate-001, imagen-4.0-ultra-generate-001 response = client.models.generate_images( model="imagen-4.0-generate-001", prompt=prompt_text, config=types.GenerateImagesConfig( number_of_images=4, # 1-4 for standard, 1 for Ultra aspect_ratio="1:1", # 1:1, 3:4, 4:3, 9:16, 16:9 person_generation="allow_adult", # "dont_allow", "allow_adult", "allow_all" ) ) # Save images for i, generated_image in enumerate(response.generated_images): generated_image.image.save(f"output_{i}.png") ``` #### For OpenAI Models (gpt-image-1.5 recommended): ```python from openai import OpenAI from pathlib import Path import base64 client = OpenAI() # reads OPENAI_API_KEY env var automatically # gpt-image-1.5 generation (recommended) response = client.images.generate( model="gpt-image-1.5", prompt=prompt_text, size="1024x1024", # or "1536x1024", "1024x1536", "auto" quality="high", # "low", "medium", "high" n=1, # 1-10 images output_format="png", # "png", "jpeg", "webp" background="auto", # "transparent", "opaque", "auto" moderation="auto", # "auto" or "low" for less restrictive ) # Response returns base64 data image_data = base64.b64decode(response.data[0].b64_json) Path("output.png").write_bytes(image_data) # gpt-image-1 generation (for max 4K resolution) response = client.images.generate( model="gpt-image-1", prompt=prompt_text, size="1024x1024", quality="high", n=1, ) # Image editing with gpt-image-1.5 response = client.images.edit( model="gpt-image-1.5", image=open("input.png", "rb"), prompt="Change the background to a beach sunset", size="1024x1024", ) # Legacy DALL-E 3 generation response = client.images.generate( model="dall-e-3", prompt=prompt_text, size="1024x1024", # or "1024x1792", "1792x1024" quality="standard", # or "hd" n=1, ) image_url = response.data[0].url # Download URL-based response import requests image_data = requests.get(image_url).content Path("output.png").write_bytes(image_data) ``` **Implementation approach:** - Use `Bash` tool to execute Python scripts with API calls - Check for API keys in environment variables - Handle errors gracefully (API limits, invalid prompts, etc.) - Save images with descriptive filenames - Report image location to user ### Step 5: Handle Output 1. **Save the generated image** to an appropriate location 2. **Verify the output** meets the request 3. **Show the user** the saved file path 4. **Offer refinement** if the result isn't quite right 5. **Explain the prompt used** so the user understands the generation ### Step 6: Iterate if Needed If the user wants changes: - For Gemini: Use chat interface to maintain context - For gpt-image-1.5: Use editing API for precise face/logo preservation - For Imagen/DALL-E: Generate new image with updated prompt - Keep previous versions for comparison - Suggest specific adjustments based on the current result ## Requirements **API Keys:** - Google (Gemini/Imagen): Set `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable - OpenAI: Set `OPENAI_API_KEY` environment variable **Python Packages:** ```bash pip install google-genai openai pillow requests ``` > **Note**: The `google-generativeai` package has been deprecated and will no longer receive updates. > Use `google-genai` instead. Migration guide: https://ai.google.dev/gemini-api/docs/migrate **System:** - Python 3.8+ - Internet connection for API access - Write permissions for saving images **Approximate Costs (per image):** | Model | Low Quality | High Quality | |-------|-------------|--------------| | imagen-4.0-fast | $0.02 | $0.02 | | imagen-4.0 | - | ~$0.04 | | imagen-4.0-ultra | - | ~$0.08 | | gemini-2.5-flash-image | ~$0.039 | ~$0.039 | | gpt-image-1.5 | ~$0.016 | ~$0.15 | | gpt-image-1 | ~$0.02 | ~$0.19 | | dall-e-3 | ~$0.04 | ~$0.08 | | dall-e-2 | ~$0.02 | ~$0.02 | ## Best Practices ### Prompt Engineering 1. **Be Specific**: Vague prompts produce inconsistent results - Bad: "a nice landscape" - Good: "mountain valley at sunrise, mist over lake, pine trees, warm golden light, peaceful atmosphere" 2. **Include Technical Details** for photorealism: - Camera: "shot on 85mm lens", "wide angle 24mm", "macro photography" - Lighting: "golden hour", "studio lighting", "rim light", "soft diffused" - Quality: "high resolution", "detailed", "sharp focus", "professional photography" 3. **Specify Style Clearly**: - "photorealistic", "oil painting", "watercolor", "digital art", "3D render" - "minimalist", "detailed", "abstract", "realistic", "stylized" - "anime style", "pixel art", "vector art", "charcoal sketch" 4. **Use Examples and References**: - "in the style of [artist/art movement]" - "similar to [known visual reference]" - For Gemini Pro: Provide actual reference images 5. **Negative Prompts** (what to avoid): - DALL-E doesn't support negative prompts directly - For Gemini, phrase as positive instructions: "clear sky" vs "no clouds" ### Model-Specific Tips **gpt-image-1.5 (Recommended):** - Best for iterative editing workflows - preserves faces/logos during edits - Built-in reasoning understands context (e.g., "Bethel, NY, August 1969" → Woodstock) - Excellent text rendering, especially dense/small text - Great for infographics, diagrams, multi-panel compositions - 4x faster than gpt-image-1, use streaming for real-time feedback - Use `background="transparent"` for assets **gpt-image-1:** - Maximum resolution (4096x4096) when needed - Good for one-shot high-res generation - No editing/inpainting support **Imagen 4 Family:** - Best text rendering among Google models - Use Fast ($0.02) for high-volume prototyping - Use Ultra for highest quality single images - Text-to-image only (no editing) - use Gemini for edits - All images include SynthID watermark **Gemini Flash (2.5) - Nano Banana:** - Best for iterative multi-turn editing via chat - Good for generating multiple variations quickly - Use for draft/concept phase with refinement **Gemini Pro (3) - Nano Banana Pro:** - Use for final deliverables and 4K output - Best for complex compositions with reference images (up to 14) - "Thinking" mode generates interim drafts for composition planning - Leverage Google Search grounding for current events/real places **DALL-E 3 (Legacy):** - Excellent at understanding natural language - Strong at creative interpretations - Automatic prompt expansion (may deviate from exact request) **DALL-E 2 (Legacy):** - More literal interpretation of prompts - Can generate variations of existing images - Budget-friendly for simple tasks ### Quality Guidelines 1. **Start with clear requirements**: Ask clarifying questions before generating 2. **Choose appropriate model**: Match model capabilities to requirements 3. **Iterate thoughtfully**: Make specific changes rather than complete regeneration 4. **Save intermediate versions**: Keep promising iterations 5. **Respect usage policies**: Follow content policies for each platform 6. **Credit the tool**: Disclose AI-generated images when sharing ### Error Handling - **API key missing**: Prompt user to set environment variable - **Invalid prompt**: Suggest refinements, check content policy - **Rate limits**: Inform user and suggest retry timing - **Generation failure**: Try simpler prompt or different model - **Unsatisfactory result**: Offer to regenerate with adjusted prompt ## Examples ### Example 1: Logo Design **User request:** "Create a logo for a coffee shop called 'Morning Brew'" **Expected behavior:** 1. Ask user about style preference (modern, vintage, minimalist, etc.) 2. Ask about color preferences 3. Select model (gpt-image-1.5 for text rendering, or gemini-3-pro-image-preview for 4K) 4. Generate with prompt: "Coffee shop logo for 'Morning Brew', minimalist modern design, coffee cup with steam forming sunrise rays, warm brown and orange colors, clean professional aesthetic, vector style, white background" 5. Use `background="transparent"` for gpt-image-1.5 for easy placement 6. Save image and show path 7. Offer to generate variations with different styles ### Example 2: Product Photography **User request:** "Generate product photos of wireless earbuds" **Expected behavior:** 1. Select model (imagen-4.0-generate-001 for photorealism, or gpt-image-1.5 for editing) 2. Generate with prompt: "Wireless earbuds product photography, white background, professional studio lighting, 3/4 angle view showing charging case and earbuds, clean minimal composition, high resolution, sharp focus, e-commerce quality" 3. Generate additional angles if requested 4. Save all versions ### Example 3: Illustration **User request:** "Create a cute sticker of a robot" **Expected behavior:** 1. Select model (gpt-image-1.5 with `background="transparent"` for stickers) 2. Generate with prompt: "Cute robot sticker, kawaii style, bold black outlines, cel-shading, pastel blue and silver colors, big friendly eyes, rounded shapes, chibi proportions, white border, transparent background suitable for sticker" 3. Save and offer variations ### Example 4: Image Editing **User request:** "Change the background of this photo to a beach sunset" **Expected behavior:** 1. Use `Read` tool to load the existing image 2. Select model (gpt-image-1.5 for best editing with face preservation, or Gemini for chat-based iteration) 3. Generate with image + prompt: "Change the background to a beautiful beach at sunset, golden hour lighting, warm colors, ocean and palm trees visible, maintain the subject in foreground, seamless composition" 4. Save edited image ### Example 5: Iterative Refinement **User request:** "Generate a futuristic city" → "Add more neon lights" → "Make it rain" **Expected behavior:** 1. First generation: "Futuristic city skyline, towering skyscrapers, advanced architecture, night scene, detailed, cinematic lighting" 2. Use gpt-image-1.5 edit API or Gemini chat interface to maintain context 3. Second refinement: "Add vibrant neon lights throughout the city, cyberpunk aesthetic, glowing signs and billboards" 4. Third refinement: "Add rain effect, wet streets reflecting neon lights, atmospheric, moody" 5. Save each version with descriptive names ## Limitations 1. **Content Policies**: All models have content restrictions (no violence, explicit content, copyrighted characters, real people without consent) 2. **Text Rendering**: Much improved in gpt-image-1.5 and Imagen 4, but very long/complex text may still have issues 3. **Photorealism of People**: May not perfectly capture specific facial features; gpt-image-1.5 preserves faces best during edits 4. **Complex Compositions**: Very complex scenes may need multiple iterations 5. **Consistency**: Hard to maintain exact consistency across multiple generations; use gpt-image-1.5 or Gemini Pro with reference images for character consistency 6. **Real-time Events**: Results may not reflect very recent events (use Gemini Pro Search grounding for current topics) 7. **API Costs**: Be mindful of usage; see pricing table above 8. **Rate Limits**: APIs have rate limits; may need to wait between requests 9. **Imagen Limitations**: Text-to-image only (no editing), single image for Ultra model 10. **Watermarks**: Google Imagen images include SynthID watermark ## Related Skills - `python-plotting` - For data visualization and charts - `brainstorming` - For ideating visual concepts - `scientific-writing` - For figure captions and documentation - `python-best-practices` - For writing clean API integration code ## Additional Resources - **Google GenAI SDK Migration Guide**: https://ai.google.dev/gemini-api/docs/migrate - **Gemini Image Generation**: https://ai.google.dev/gemini-api/docs/image-generation - **Imagen API Documentation**: https://ai.google.dev/gemini-api/docs/imagen - **OpenAI Images API**: https://platform.openai.com/docs/api-reference/images - **gpt-image-1.5 Prompting Guide**: https://cookbook.openai.com/examples/multimodal/image-gen-1.5-prompting_guide - **Deprecated SDK Info**: https://github.com/google-gemini/deprecated-generative-ai-python - **Prompt Engineering Guide**: See `references/prompt-engineering.md`