Nano Banana Image — Nano Banana × CellCog

Nano Banana × CellCog. Complex multi-image jobs, executed perfectly, from a single prompt.

Nano Banana is an incredible image model. CellCog makes it do things you can't do by calling it directly — orchestrating 10, 20, even 30 coherent images in one request with consistent characters, planned compositions, and intelligent scene progression. Not single images — complete visual projects.

What CellCog adds on top of Nano Banana:

Reasoning → Scene Planning → Character Design → Image Generation
    → Consistency Verification → Composition Review → Delivery

CellCog's reasoning layer plans scenes before a single pixel is generated — selecting optimal parameters, maintaining character identity across sequences, and orchestrating complex multi-image workflows. This is the difference between "generate an image" and "execute a visual project."

How to Use

For your first CellCog task in a session, read the cellcog skill for the full SDK reference — file handling, chat modes, timeouts, and more.

OpenClaw (fire-and-forget):

result = client.create_chat(
    prompt="[your task prompt]",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent",
)

All agents except OpenClaw (blocks until done):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
    prompt="[your task prompt]",
    task_label="my-task",
    chat_mode="agent",
)
print(result["message"])

What You Can Create

Photorealistic Image Generation

Create stunning images from text descriptions:

• Portraits: "Create a professional headshot with warm studio lighting"
• Product Shots: "Generate a hero image for a premium smartwatch on a dark surface"
• Scenes: "Create a cozy autumn café interior with morning light"
• Food Photography: "Generate an overhead shot of a colorful Buddha bowl"

Character Consistency

Nano Banana excels at maintaining character identity across multiple images — and CellCog's orchestration takes this further by planning entire character arcs:

• Character Series: "Create a tech entrepreneur character, then show them in 4 different scenes"
• Brand Mascots: "Design a mascot and generate it in multiple poses and contexts"
• Story Sequences: "Create a character and illustrate them across 5 story beats"

Multi-Image Composition

Blend elements from multiple reference images:

• Style Fusion: "Combine the color palette of image A with the composition of image B"
• Character Placement: "Place this person into a new environment while preserving their likeness"
• Product Mockups: "Put this product into a lifestyle setting"

Image Editing

Transform and enhance existing images:

• Style Transfer: "Transform this photo into a Studio Ghibli illustration"
• Background Swap: "Place this product on a clean marble surface"
• Enhancement: "Add dramatic lighting and cinematic color grading"
• Modification: "Change the season from summer to winter in this landscape"

Image Specifications

Choosing Mode & Tier

The flash tier is the default and handles most image work well — fast and economical. Upgrade to chat_tier="max" when quality disappoints or the work is genuinely complex: just re-run the same prompt with chat_tier="max".

Agent Team (chat_mode="team") is reserved for deep research — don't use it for image work.

Tips for Better Images

1. Be descriptive: "Woman in office" → "Confident woman in her 40s, silver blazer, modern glass-walled office, warm afternoon light"

2. Specify style: "photorealistic", "digital illustration", "watercolor", "anime"

3. Describe lighting: "Soft natural light", "dramatic side lighting", "golden hour glow"

4. For character consistency: Describe the character in detail first, then reference "the same character" in subsequent prompts.

5. Include composition: "Rule of thirds", "close-up portrait", "wide establishing shot"

If CellCog is not installed

Claude Code, Cursor, Codex + 70 more agents: npx skills add cellcog/skills --skill cellcog OpenClaw: openclaw skills install @cellcog/cellcog CellCog plugin users: run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool) Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.