Gemini Image Generation

Gemini Image Generation

Overview

Generate and edit images with Gemini 3 native image models (Nano Banana). Supports text-to-image and image-to-image (reference-based) generation.

Core rules:

1. Describe style with precise visual vocabulary; do not rely on Gemini for exact text or typography in generated images.
2. Lock what must stay; describe what must change. Be specific but concise — overlong prompts trigger MALFORMED_FUNCTION_CALL.
3. For image-to-image, the model inherits the source image's watermarks. Remove watermarks via prompt instructions, not by pre-patching the source.

When to Use

• Generate images from text prompts.
• Transfer or restyle a reference image while preserving its composition.
• Edit a single attribute of an existing image (outfit, lighting, background) while keeping character and pose identical.
• Maintain character consistency across multiple generated images by feeding prior outputs back as references.

Do not use this skill when:

• The output requires exact rendered text inside the image.
• The task is only cropping, resizing, or compressing existing assets.
• The source contains material that should not be sent to a third-party API.

Prerequisites

• One of the following auth paths in .env.local or .env:

  • Gemini Developer API (AI Studio): GOOGLE_AI_STUDIO_API_KEY or GEMINI_API_KEY or GOOGLE_API_KEY
  • Vertex AI Express Mode: VERTEX_AI_KEY (API-key string, typically prefixed AQ.) — requires Vertex AI API enabled in the bound project once
  • Vertex AI ADC: GOOGLE_GENAI_USE_VERTEXAI=true + GOOGLE_CLOUD_PROJECT + GOOGLE_CLOUD_LOCATION (default us-central1); credentials via gcloud auth application-default login or GOOGLE_APPLICATION_CREDENTIALS

• Python with google-genai, Pillow, python-dotenv. With uv, declare deps inline at the top of the script:

  # /// script
  # dependencies = ["google-genai", "Pillow", "python-dotenv"]
  # ///
  

• Default model: gemini-3.1-flash-image-preview (same name on AI Studio and Vertex).

Model Capabilities

Text-to-Image

client = _make_client()  # defined above
resp = client.models.generate_content(
    model="gemini-3.1-flash-image-preview",
    contents=["A serene mountain landscape at dawn, watercolor style."],
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"],
        image_config=_image_config(aspect_ratio="16:9", image_size="2K"),
    ),
)
for part in resp.parts:
    if image := part.as_image():
        image.save("output.png")

Image-to-Image (Reference-Based Edit)

Pass the source image alongside a prompt that locks composition and describes only the targeted change.

from PIL import Image

client = _make_client()  # defined above
src = Image.open("source.png")

prompt = (
    "Redraw this image keeping the pose, composition, character, hair, face, "
    "expression, and the background absolutely identical. "
    "Preserve the original anime illustration style. "
    "Change ONLY the outfit to: a pure white short-sleeve linen dress, "
    "completely plain, no print, knee-length."
)

resp = client.models.generate_content(
    model="gemini-3.1-flash-image-preview",
    contents=[prompt, src],
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"],
        image_config=_image_config(aspect_ratio="1:1", image_size="1K"),
    ),
)
for part in resp.parts:
    if image := part.as_image():
        image.save("edited.png")

Prompting Rules

1. Lock first, change second. Open with what must stay identical (pose, composition, character, background, art style), then state the single change.
2. Use concrete visual mechanics — linework weight, shadow depth, color palette, fabric type, cel-shaded vs painterly — instead of vague style words.
3. Stay concise. Long, multi-clause IMPORTANT: must NOT ... paragraphs reliably trigger MALFORMED_FUNCTION_CALL on Gemini 3 image models. Prefer one short positive instruction over an exhaustive list of negatives.
4. One reference image per call unless the task explicitly requires mixing — multiple refs cause content blending.
5. Character consistency across turns: feed previously generated images back into subsequent prompts as references.

Removing Source Watermarks (Gemini Sparkle)

Images generated via Google AI Studio carry a small white four-point sparkle ✦ logo (typically bottom-right corner). In image-to-image, the model preserves the source background — including this watermark.

Wrong approach: pre-patching the source

Pasting clean pixels over the watermark area in PIL:

# ANTI-PATTERN — do not do this for watermark removal
patch = im.crop((50, 1820, 450, 2048)).transpose(Image.FLIP_LEFT_RIGHT)
im.paste(patch, (1648, 1820, 2048, 2048))

Why it fails:

• The hard seam is detectable to the model and breaks continuity.
• For some prompts the model rejects the modified input with FinishReason.MALFORMED_FUNCTION_CALL and never emits an image.
• AI-fill / outpaint over the patch shows the same instability.

Correct approach: instruct the model to remove it

Keep the original image as input; add one short clause to the prompt:

Also remove the small white four-point sparkle / star icon on the sand
in the bottom-right corner; repaint that area with the same clean
background so no icon, sparkle, logo, or watermark remains.

Be specific about what the icon is (white four-point sparkle / star, not "watermark") and where it sits (bottom-right corner, on the sand / sky / etc.). Vague "remove watermark" instructions are often ignored.

Note: the API output never adds a new visible watermark. Gemini still embeds a non-visible SynthID — that is expected and unrelated.

Robust Call Pattern

Three transient failure modes occur regularly and must be handled:

resp.text may also be None — guard before slicing.

for attempt in range(6):
    try:
        resp = client.models.generate_content(model=MODEL, contents=[prompt, src], config=cfg)
    except Exception as e:
        msg = str(e)
        if any(s in msg for s in ("503", "UNAVAILABLE", "429", "RESOURCE_EXHAUSTED")) and attempt < 5:
            time.sleep(2 ** attempt * 5)
            continue
        raise

    parts = resp.parts or []
    for part in parts:
        if img := part.as_image():
            img.save(out_path)
            break
    else:
        cands = getattr(resp, "candidates", None) or []
        finish = [getattr(c, "finish_reason", None) for c in cands]
        txt = (getattr(resp, "text", None) or "")[:120]
        print(f"no image (attempt {attempt+1}); finish={finish} text={txt}")
        time.sleep(3)
        continue
    break

Loading the API Key / Creating the Client

Use a single _make_client() helper that resolves the auth path from env. Place it at the top of every script that uses this skill; scripts should never branch on auth in their business logic.

import os
from dotenv import load_dotenv
from google import genai
from google.genai import types


def _make_client() -> genai.Client:
    """Resolve auth path from env. Priority: Vertex express → Vertex ADC → AI Studio."""
    for p in (".env.local", ".env"):
        if os.path.exists(p):
            load_dotenv(p)

    if vkey := os.environ.get("VERTEX_AI_KEY"):
        # Vertex AI Express Mode: single API key, no project/location needed
        return genai.Client(vertexai=True, api_key=vkey)

    if os.environ.get("GOOGLE_GENAI_USE_VERTEXAI", "").lower() in ("1", "true", "yes"):
        # Vertex AI with ADC / service account
        return genai.Client(
            vertexai=True,
            project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
            location=os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
        )

    key = (
        os.environ.get("GOOGLE_AI_STUDIO_API_KEY")
        or os.environ.get("GEMINI_API_KEY")
        or os.environ.get("GOOGLE_API_KEY")
    )
    if not key:
        raise EnvironmentError(
            "No API key. Set one of: VERTEX_AI_KEY, GOOGLE_AI_STUDIO_API_KEY, "
            "GEMINI_API_KEY, GOOGLE_API_KEY; or GOOGLE_GENAI_USE_VERTEXAI=true "
            "with GOOGLE_CLOUD_PROJECT/LOCATION."
        )
    return genai.Client(api_key=key)


def _is_vertex() -> bool:
    return bool(os.environ.get("VERTEX_AI_KEY")) or os.environ.get(
        "GOOGLE_GENAI_USE_VERTEXAI", ""
    ).lower() in ("1", "true", "yes")


def _image_config(aspect_ratio: str = "1:1", image_size: str = "1K") -> types.ImageConfig:
    """Build ImageConfig. Vertex does NOT support image_size — drop it there."""
    if _is_vertex():
        return types.ImageConfig(aspect_ratio=aspect_ratio)
    return types.ImageConfig(aspect_ratio=aspect_ratio, image_size=image_size)


client = _make_client()

Never read a .env file by hand to print the key — load it into the environment via python-dotenv and reference the variable.

Output Configuration

1K is sufficient for most preview / iteration work; reserve 2K/4K for finals. Use _image_config() (above) to drop image_size automatically when running on Vertex.

Verification

Before claiming completion:

• Confirm the API key loaded and at least one image part returned.
• Open each generated image to verify composition, character identity, and the targeted change.
• Inspect the bottom-right corner to confirm the sparkle is gone (when sourcing from a Gemini-generated image).
• If style drifted, tighten the lock clause and re-run; do not patch the source.