// Guide to image generation and editing in MassGen. Use when creating images, editing existing images, iterating on image designs, or choosing between image backends (OpenAI, Google Gemini/Imagen, Grok, OpenRouter).
Run MassGen experiments and analyze logs using automation mode, logfire tracing, and SQL queries. Use this skill for performance analysis, debugging agent behavior, evaluating coordination patterns, and improving the logging structure, or whenever an ANALYSIS_REPORT.md is needed in a log directory.
Invoke MassGen's multi-agent system. Use when the user wants multiple AI agents on a task: writing, code, review, planning, specs, research, design, or any task where parallel iteration beats working alone.
Complete guide for integrating a new LLM backend into MassGen. Use when adding a new provider (e.g., Codex, Mistral, DeepSeek) or when auditing an existing backend for missing integration points. Covers all ~15 files that need touching.
Guide to video generation in MassGen. Use when creating videos from text prompts or images across Grok, Google Veo, and OpenAI Sora backends.
Reference guide for adding new media generation backends to MassGen's unified generate_media tool.
Guide to audio generation and understanding in MassGen. Covers text-to-speech, music, sound effects, and audio understanding across ElevenLabs and OpenAI backends.
| name | image-generation |
| description | Guide to image generation and editing in MassGen. Use when creating images, editing existing images, iterating on image designs, or choosing between image backends (OpenAI, Google Gemini/Imagen, Grok, OpenRouter). |
Generate images using generate_media with mode="image". The system auto-selects the best backend based on available API keys.
# Simple text-to-image (auto-selects backend)
generate_media(prompt="A cat in space", mode="image")
# Specify backend and quality
generate_media(prompt="A logo for a coffee shop", mode="image",
backend_type="openai", quality="high")
# Batch generation (parallel)
generate_media(prompts=["sunset over ocean", "mountain landscape", "city at night"],
mode="image", max_concurrent=3)
| Backend | Default Model | Strengths | API Key |
|---|---|---|---|
| Google (priority 1) | gemini-3.1-flash-image-preview (Nano Banana 2) | Fast, flexible sizes, image editing, multi-turn | GOOGLE_API_KEY or GEMINI_API_KEY |
| OpenAI (priority 2) | gpt-5.4 | High quality, transparent backgrounds, continuation via response ID | OPENAI_API_KEY |
| Grok (priority 3) | grok-imagine-image | 1k resolution, continuation via stored data URI | XAI_API_KEY |
| OpenRouter (priority 4) | google/gemini-3.1-flash-image-preview | Access to multiple models via single API | OPENROUTER_API_KEY |
| Parameter | Description | Example |
|---|---|---|
prompt | Text description of the image | "A watercolor painting of mountains" |
backend_type | Force a specific backend | "google", "openai", "grok", "openrouter" |
model | Override default model | "gemini-3-pro-image-preview" for studio quality |
quality | Image quality (OpenAI) | "low", "medium", "high", "auto" |
size | Image dimensions | See backends reference |
aspect_ratio | Aspect ratio | "16:9", "1:1", "4:5" |
input_images | Source images for image-to-image editing | ["photo.jpg"] |
continue_from | Continuation ID for multi-turn editing | result["continuation_id"] |
Transform existing images by providing input_images:
generate_media(
prompt="Make it look like a watercolor painting",
mode="image",
input_images=["photo.jpg"]
)
Supported backends for image-to-image: Google (Gemini), OpenAI, Grok. The system auto-selects if your current backend doesn't support it.
Iteratively refine images using continue_from:
# First generation
result = generate_media(prompt="A logo for a coffee shop", mode="image")
# Refine using the continuation ID
result2 = generate_media(
prompt="Make the text larger and add a cup icon",
mode="image",
continue_from=result["continuation_id"]
)
Each backend uses a different continuation mechanism:
previous_response_id (stateless)Continuation only works for single image generation (not batch).
Google supports two API paths. Gemini (Nano Banana 2) is the default and recommended for most use cases. Imagen is only needed for advanced reference-image editing features.
gemini-*): generate_content() — text-to-image, image editing via input_images, multi-turn continuationimagen-*): generate_images() / edit_image() — text-to-image with negative_prompt/seed/guidance_scale, plus style transfer, control editing, and subject consistency via reference imagesFor studio-quality precision and text rendering, use: model="gemini-3-pro-image-preview" (Pro-tier).
extra_params reference: See references/extra_params.md