fix(signal): bypass base is_allowed for policy-approved messages

Override _handle_message to publish directly to the bus for messages
that have already passed _check_inbound_policy. The denied DM pairing
path calls super()._handle_message() to issue pairing codes via the
base class. This avoids cross-policy leakage where e.g. group open
policy would cause is_allowed to incorrectly allow denied DM senders.

Also includes:
- SSE: strip one optional leading space after 'data:' per spec
- Convert 20+ f-string log calls to loguru lazy formatting
- Add end-to-end tests for DM/group routing through the full chain
- Add cross-policy test (dm allowlist + group open) for pairing
- Add Signal channel documentation to docs/chat-apps.md

docs/chat-apps.md

@@ -17,6 +17,7 @@ Connect nanobot to your favorite chat platform. Want to build your own? See the
 | **Wecom** | Bot ID + Bot Secret |
 | **Microsoft Teams** | App ID + App Password + public HTTPS endpoint |
 | **Mochat** | Claw token (auto-setup available) |
+| **Signal** | signal-cli daemon + phone number |
 
 <details>
 <summary><b>Telegram</b> (Recommended)</summary>
@@ -669,3 +670,69 @@ nanobot gateway
 ```
 
 </details>
+
+<details>
+<summary><b>Signal</b></summary>
+
+Uses **signal-cli** daemon in HTTP mode — receive messages via SSE, send via JSON-RPC.
+
+**1. Install signal-cli**
+
+Install [signal-cli](https://github.com/AsamK/signal-cli) and register a phone number:
+
+```bash
+signal-cli -u +1234567890 register
+signal-cli -u +1234567890 verify <CODE>
+```
+
+Start the daemon:
+
+```bash
+signal-cli -a +1234567890 daemon --http localhost:8080
+```
+
+**2. Configure**
+
+```json
+{
+  "channels": {
+    "signal": {
+      "enabled": true,
+      "phoneNumber": "+1234567890",
+      "daemonHost": "localhost",
+      "daemonPort": 8080,
+      "dm": {
+        "enabled": true,
+        "policy": "open"
+      },
+      "group": {
+        "enabled": true,
+        "policy": "open",
+        "requireMention": true
+      }
+    }
+  }
+}
+```
+
+> - `phoneNumber`: Your registered Signal phone number.
+> - `daemonHost` / `daemonPort`: Where signal-cli daemon is listening (default `localhost:8080`).
+> - `dm.policy`: `"open"` (anyone can DM) or `"allowlist"` (only listed numbers/UUIDs). When `"allowlist"`, unlisted DM senders receive a pairing code.
+> - `dm.allowFrom`: List of allowed phone numbers or UUIDs (used when policy is `"allowlist"`).
+> - `group.policy`: `"open"` (all groups) or `"allowlist"` (only listed group IDs).
+> - `group.requireMention`: When `true` (default), the bot only responds in groups when @mentioned.
+> - `group.allowFrom`: List of allowed group IDs (used when group policy is `"allowlist"`).
+> - `attachmentsDir`: Override the directory where signal-cli stores inbound attachments. Defaults to `~/.local/share/signal-cli/attachments` (the Linux default). Set this if signal-cli runs with a custom `XDG_DATA_HOME` or on macOS/Windows.
+> - `groupMessageBufferSize`: Number of recent group messages kept for context (default `20`, must be > 0).
+
+**3. Run**
+
+```bash
+nanobot gateway
+```
+
+> [!TIP]
+> The channel automatically reconnects to the signal-cli daemon with exponential backoff if the connection drops.
+> Markdown in bot replies is automatically converted to Signal text styles (bold, italic, code, etc.).
+
+</details>

docs/image-generation.md

@@ -48,6 +48,28 @@ AIHubMix example:
 }
 ```
 
+Gemini example (Imagen 4):
+
+```json
+{
+  "providers": {
+    "gemini": {
+      "apiKey": "${GEMINI_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "gemini",
+      "model": "imagen-4.0-generate-001",
+      "defaultAspectRatio": "1:1"
+    }
+  }
+}
+```
+
+For Gemini Flash (which supports reference-image edits) see the [Gemini](#gemini) section below.
+
 > [!TIP]
 > Prefer environment variables for API keys. nanobot resolves `${VAR_NAME}` values from the environment at startup.
 
@@ -69,7 +91,7 @@ The WebUI hides provider storage details from the user. The agent sees the saved
 | Option | Type | Default | Description |
 |--------|------|---------|-------------|
 | `tools.imageGeneration.enabled` | boolean | `false` | Register the `generate_image` tool |
-| `tools.imageGeneration.provider` | string | `"openrouter"` | Image provider name. Currently `openrouter` and `aihubmix` are supported |
+| `tools.imageGeneration.provider` | string | `"openrouter"` | Image provider name. Supported values: `openrouter`, `aihubmix`, `gemini` |
 | `tools.imageGeneration.model` | string | `"openai/gpt-5.4-image-2"` | Provider model name |
 | `tools.imageGeneration.defaultAspectRatio` | string | `"1:1"` | Default ratio when the prompt/tool call does not specify one |
 | `tools.imageGeneration.defaultImageSize` | string | `"1K"` | Default size hint, for example `1K`, `2K`, `4K`, or `1024x1024` |
@@ -139,6 +161,36 @@ Configure:
 
 `quality: low` is optional. It can make free image models faster and less likely to time out, but it is not required for correctness.
 
+### Gemini
+
+nanobot supports two Gemini image generation model families via Google's Generative Language API:
+
+| Model | Endpoint | Reference images |
+|-------|----------|-----------------|
+| `imagen-4.0-generate-001` | `:predict` | Not supported by this integration |
+| `gemini-2.5-flash-image` | `:generateContent` | Supported |
+
+For reference-image edits, use a Gemini Flash image model:
+
+```json
+{
+  "providers": {
+    "gemini": {
+      "apiKey": "${GEMINI_API_KEY}"
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "gemini",
+      "model": "gemini-2.5-flash-image"
+    }
+  }
+}
+```
+
+Imagen 4 supports the aspect ratios `1:1`, `9:16`, `16:9`, `3:4`, and `4:3`. Unsupported ratios are ignored and the model uses its default. The `defaultImageSize` setting has no effect on Gemini models; sizing is controlled by `defaultAspectRatio` only. Reference images passed with an Imagen model are ignored (with a warning logged).
+
 ## Artifacts
 
 Generated images are stored under the active nanobot instance's media directory:
@@ -193,7 +245,7 @@ Use the reference image. Keep the same robot and composition, change the palette
 |---------|-------|
 | `generate_image` is not available | Set `tools.imageGeneration.enabled` to `true` and restart the gateway |
 | Missing API key error | Configure `providers.<provider>.apiKey`; if using `${VAR_NAME}`, confirm the environment variable is visible to the gateway process |
-| `unsupported image generation provider` | Use `openrouter` or `aihubmix` |
+| `unsupported image generation provider` | Use `openrouter`, `aihubmix`, or `gemini` |
 | AIHubMix says `Incorrect model ID` | Use `model: "gpt-image-2-free"`; nanobot expands it to the required `openai/gpt-image-2-free` model path internally |
 | Generation times out | Try a smaller/default image size, set AIHubMix `extraBody.quality` to `"low"`, or retry later |
 | Reference image rejected | Reference image paths must be inside the workspace or nanobot media directory and must be valid image files |

nanobot/agent/tools/image_generation.py

@@ -18,7 +18,9 @@ from nanobot.config.paths import get_media_dir
 from nanobot.config.schema import Base
 from nanobot.providers.image_generation import (
     AIHubMixImageGenerationClient,
+    GeminiImageGenerationClient,
     ImageGenerationError,
+    MiniMaxImageGenerationClient,
     OpenRouterImageGenerationClient,
 )
 from nanobot.utils.artifacts import (
@@ -117,7 +119,9 @@ class ImageGenerationTool(Tool):
     def _provider_config(self) -> ProviderConfig | None:
         return self.provider_configs.get(self.config.provider)
 
-    def _provider_client(self) -> OpenRouterImageGenerationClient | AIHubMixImageGenerationClient | None:
+    def _provider_client(
+        self,
+    ) -> OpenRouterImageGenerationClient | AIHubMixImageGenerationClient | MiniMaxImageGenerationClient | GeminiImageGenerationClient | None:
         provider = self._provider_config()
         kwargs = {
             "api_key": provider.api_key if provider else None,
@@ -129,6 +133,10 @@ class ImageGenerationTool(Tool):
             return OpenRouterImageGenerationClient(**kwargs)
         if self.config.provider == "aihubmix":
             return AIHubMixImageGenerationClient(**kwargs)
+        if self.config.provider == "minimax":
+            return MiniMaxImageGenerationClient(**kwargs)
+        if self.config.provider == "gemini":
+            return GeminiImageGenerationClient(**kwargs)
         return None
 
     def _missing_api_key_error(self) -> str:
@@ -137,6 +145,10 @@ class ImageGenerationTool(Tool):
             return "Error: OpenRouter API key is not configured. Set providers.openrouter.apiKey."
         if provider == "aihubmix":
             return "Error: AIHubMix API key is not configured. Set providers.aihubmix.apiKey."
+        if provider == "minimax":
+            return "Error: MiniMax API key is not configured. Set providers.minimax.apiKey."
+        if provider == "gemini":
+            return "Error: Gemini API key is not configured. Set providers.gemini.apiKey."
         return f"Error: {provider} API key is not configured."
 
     def _resolve_reference_image(self, value: str) -> str:

nanobot/cli/commands.py

@@ -642,6 +642,8 @@ def serve(
             image_generation_provider_configs={
                 "openrouter": runtime_config.providers.openrouter,
                 "aihubmix": runtime_config.providers.aihubmix,
+                "minimax": runtime_config.providers.minimax,
+                "gemini": runtime_config.providers.gemini,
             },
         )
     except ValueError as exc:
@@ -755,6 +757,8 @@ def _run_gateway(
         image_generation_provider_configs={
             "openrouter": config.providers.openrouter,
             "aihubmix": config.providers.aihubmix,
+            "minimax": config.providers.minimax,
+            "gemini": config.providers.gemini,
         },
         provider_snapshot_loader=load_provider_snapshot,
         runtime_model_publisher=lambda model, preset: publish_runtime_model_update(

nanobot/nanobot.py

@@ -66,6 +66,8 @@ class Nanobot:
             image_generation_provider_configs={
                 "openrouter": config.providers.openrouter,
                 "aihubmix": config.providers.aihubmix,
+                "minimax": config.providers.minimax,
+                "gemini": config.providers.gemini,
             },
         )
         return cls(loop)

nanobot/providers/image_generation.py

@@ -8,6 +8,7 @@ from pathlib import Path
 from typing import Any
 
 import httpx
+from loguru import logger
 
 from nanobot.providers.registry import find_by_name
 from nanobot.utils.helpers import detect_image_mime
@@ -26,6 +27,8 @@ _AIHUBMIX_ASPECT_RATIO_SIZES = {
     "4:3": "1536x1024",
     "16:9": "1536x1024",
 }
+_GEMINI_DEFAULT_TIMEOUT_S = 120.0
+_GEMINI_IMAGEN_ASPECT_RATIOS = {"1:1", "9:16", "16:9", "3:4", "4:3"}
 
 
 class ImageGenerationError(RuntimeError):
@@ -50,17 +53,28 @@ def _provider_base_url(provider: str, api_base: str | None, fallback: str) -> st
     return fallback
 
 
-def image_path_to_data_url(path: str | Path) -> str:
+def _read_image_b64(path: str | Path) -> tuple[str, str]:
-    """Convert a local image path to an image data URL."""
+    """Return ``(mime, base64)`` for the image at ``path``."""
     p = Path(path).expanduser()
     raw = p.read_bytes()
     mime = detect_image_mime(raw)
     if mime is None:
         raise ImageGenerationError(f"unsupported reference image: {p}")
-    encoded = base64.b64encode(raw).decode("ascii")
+    return mime, base64.b64encode(raw).decode("ascii")
+
+
+def image_path_to_data_url(path: str | Path) -> str:
+    """Convert a local image path to an image data URL."""
+    mime, encoded = _read_image_b64(path)
     return f"data:{mime};base64,{encoded}"
 
 
+def image_path_to_inline_data(path: str | Path) -> dict[str, str]:
+    """Convert a local image path to a Gemini ``inlineData`` payload dict."""
+    mime, encoded = _read_image_b64(path)
+    return {"mimeType": mime, "data": encoded}
+
+
 def _b64_png_data_url(value: str) -> str:
     return f"data:image/png;base64,{value}"
 
@@ -341,6 +355,203 @@ class AIHubMixImageGenerationClient:
         return GeneratedImageResponse(images=images, content="", raw=payload)
 
 
+def _http_error_detail(response: httpx.Response) -> str:
+    """Extract a readable error message from an HTTP error response."""
+    try:
+        data = response.json()
+        if isinstance(data, dict):
+            err = data.get("error")
+            if isinstance(err, dict):
+                return err.get("message") or str(err)
+            if err:
+                return str(err)
+    except Exception:
+        pass
+    return response.text[:500] or "<empty response body>"
+
+
+class GeminiImageGenerationClient:
+    """Async client for Gemini/Imagen image generation via the Generative Language API."""
+
+    def __init__(
+        self,
+        *,
+        api_key: str | None,
+        api_base: str | None = None,
+        extra_headers: dict[str, str] | None = None,
+        extra_body: dict[str, Any] | None = None,
+        timeout: float = _GEMINI_DEFAULT_TIMEOUT_S,
+        client: httpx.AsyncClient | None = None,
+    ) -> None:
+        self.api_key = api_key
+        # The Gemini provider's registry default_api_base is the OpenAI-compat
+        # shim (.../v1beta/openai/), which has no image endpoints. Image
+        # generation needs the native Generative Language API base, so we don't
+        # use _provider_base_url() here.
+        self.api_base = (
+            api_base or "https://generativelanguage.googleapis.com/v1beta"
+        ).rstrip("/")
+        self.extra_headers = extra_headers or {}
+        self.extra_body = extra_body or {}
+        self.timeout = timeout
+        self._client = client
+
+    async def generate(
+        self,
+        *,
+        prompt: str,
+        model: str,
+        reference_images: list[str] | None = None,
+        aspect_ratio: str | None = None,
+        image_size: str | None = None,
+    ) -> GeneratedImageResponse:
+        if not self.api_key:
+            raise ImageGenerationError(
+                "Gemini API key is not configured. Set providers.gemini.apiKey."
+            )
+        if "imagen" in model.lower():
+            if reference_images:
+                logger.warning(
+                    "Imagen models do not support reference images; "
+                    "ignoring {} reference image(s) for {}",
+                    len(reference_images),
+                    model,
+                )
+            return await self._generate_imagen(
+                prompt=prompt, model=model, aspect_ratio=aspect_ratio
+            )
+        return await self._generate_gemini_flash(
+            prompt=prompt, model=model, reference_images=reference_images or []
+        )
+
+    async def _generate_imagen(
+        self,
+        *,
+        prompt: str,
+        model: str,
+        aspect_ratio: str | None,
+    ) -> GeneratedImageResponse:
+        parameters: dict[str, Any] = {"sampleCount": 1}
+        if aspect_ratio in _GEMINI_IMAGEN_ASPECT_RATIOS:
+            parameters["aspectRatio"] = aspect_ratio
+        body: dict[str, Any] = {
+            "instances": [{"prompt": prompt}],
+            "parameters": parameters,
+        }
+        body.update(self.extra_body)
+
+        url = f"{self.api_base}/models/{model}:predict"
+        headers = {
+            "x-goog-api-key": self.api_key or "",
+            "Content-Type": "application/json",
+            **self.extra_headers,
+        }
+
+        if self._client is not None:
+            response = await self._client.post(url, headers=headers, json=body)
+        else:
+            async with httpx.AsyncClient(timeout=self.timeout) as client:
+                response = await client.post(url, headers=headers, json=body)
+
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as exc:
+            detail = _http_error_detail(response)
+            logger.error("Gemini Imagen generation failed (HTTP {}): {}", response.status_code, detail)
+            raise ImageGenerationError(
+                f"Gemini Imagen generation failed (HTTP {response.status_code}): {detail}"
+            ) from exc
+
+        data = response.json()
+        images: list[str] = []
+        for prediction in data.get("predictions") or []:
+            if not isinstance(prediction, dict):
+                continue
+            b64 = prediction.get("bytesBase64Encoded")
+            mime = prediction.get("mimeType", "image/png")
+            if isinstance(b64, str) and b64:
+                images.append(f"data:{mime};base64,{b64}")
+
+        if not images:
+            provider_error = data.get("error") if isinstance(data, dict) else None
+            if provider_error:
+                raise ImageGenerationError(f"Gemini Imagen returned no images: {provider_error}")
+            raise ImageGenerationError("Gemini Imagen returned no images for this request")
+
+        return GeneratedImageResponse(images=images, content="", raw=data)
+
+    async def _generate_gemini_flash(
+        self,
+        *,
+        prompt: str,
+        model: str,
+        reference_images: list[str],
+    ) -> GeneratedImageResponse:
+        parts: list[dict[str, Any]] = [
+            {"inlineData": image_path_to_inline_data(path)} for path in reference_images
+        ]
+        parts.append({"text": prompt})
+
+        body: dict[str, Any] = {
+            "contents": [{"role": "user", "parts": parts}],
+            "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]},
+        }
+        body.update(self.extra_body)
+
+        url = f"{self.api_base}/models/{model}:generateContent"
+        headers = {
+            "x-goog-api-key": self.api_key or "",
+            "Content-Type": "application/json",
+            **self.extra_headers,
+        }
+
+        if self._client is not None:
+            response = await self._client.post(url, headers=headers, json=body)
+        else:
+            async with httpx.AsyncClient(timeout=self.timeout) as client:
+                response = await client.post(url, headers=headers, json=body)
+
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as exc:
+            detail = _http_error_detail(response)
+            logger.error("Gemini image generation failed (HTTP {}): {}", response.status_code, detail)
+            raise ImageGenerationError(
+                f"Gemini image generation failed (HTTP {response.status_code}): {detail}"
+            ) from exc
+
+        data = response.json()
+        images: list[str] = []
+        text_parts: list[str] = []
+        for candidate in data.get("candidates") or []:
+            if not isinstance(candidate, dict):
+                continue
+            content = candidate.get("content") or {}
+            for part in content.get("parts") or []:
+                if not isinstance(part, dict):
+                    continue
+                if "text" in part:
+                    text_parts.append(part["text"])
+                inline = part.get("inlineData")
+                if isinstance(inline, dict):
+                    mime = inline.get("mimeType", "image/png")
+                    b64 = inline.get("data", "")
+                    if b64:
+                        images.append(f"data:{mime};base64,{b64}")
+
+        if not images:
+            provider_error = data.get("error") if isinstance(data, dict) else None
+            if provider_error:
+                raise ImageGenerationError(f"Gemini returned no images: {provider_error}")
+            raise ImageGenerationError("Gemini returned no images for this request")
+
+        return GeneratedImageResponse(
+            images=images,
+            content="\n".join(t for t in text_parts if t).strip(),
+            raw=data,
+        )
+
+
 async def _aihubmix_images_from_payload(
     client: httpx.AsyncClient,
     payload: dict[str, Any],
@@ -393,3 +604,136 @@ async def _aihubmix_images_from_payload(
     for candidate in candidates:
         await collect(candidate)
     return images
+
+
+_MINIMAX_TIMEOUT_S = 300.0
+
+_MINIMAX_ASPECT_RATIO_SIZES = {
+    "1:1": "1:1",
+    "16:9": "16:9",
+    "4:3": "4:3",
+    "3:2": "3:2",
+    "2:3": "2:3",
+    "3:4": "3:4",
+    "9:16": "9:16",
+    "21:9": "21:9",
+}
+
+
+class MiniMaxImageGenerationClient:
+    """Async client for MiniMax image generation API."""
+
+    def __init__(
+        self,
+        *,
+        api_key: str | None,
+        api_base: str | None = None,
+        extra_headers: dict[str, str] | None = None,
+        extra_body: dict[str, Any] | None = None,
+        timeout: float = _MINIMAX_TIMEOUT_S,
+        client: httpx.AsyncClient | None = None,
+    ) -> None:
+        self.api_key = api_key
+        self.api_base = _provider_base_url(
+            "minimax",
+            api_base,
+            "https://api.minimaxi.com/v1",
+        )
+        self.extra_headers = extra_headers or {}
+        self.extra_body = extra_body or {}
+        self.timeout = timeout
+        self._client = client
+
+    def _resolve_aspect_ratio(self, aspect_ratio: str | None) -> str:
+        if aspect_ratio and aspect_ratio in _MINIMAX_ASPECT_RATIO_SIZES:
+            return _MINIMAX_ASPECT_RATIO_SIZES[aspect_ratio]
+        return "1:1"
+
+    async def generate(
+        self,
+        *,
+        prompt: str,
+        model: str,
+        reference_images: list[str] | None = None,
+        aspect_ratio: str | None = None,
+        image_size: str | None = None,
+    ) -> GeneratedImageResponse:
+        if not self.api_key:
+            raise ImageGenerationError(
+                "MiniMax API key is not configured. Set providers.minimax.apiKey."
+            )
+
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+            **self.extra_headers,
+        }
+
+        body: dict[str, Any] = {
+            "model": model,
+            "prompt": prompt,
+            "response_format": "base64",
+        }
+
+        resolved_ratio = self._resolve_aspect_ratio(aspect_ratio)
+        body["aspect_ratio"] = resolved_ratio
+
+        refs = list(reference_images or [])
+        if refs:
+            image_refs = [image_path_to_data_url(path) for path in refs]
+            body["subject_reference"] = [
+                {"type": "character", "image_file": ref} for ref in image_refs
+            ]
+
+        body.update(self.extra_body)
+
+        if self._client is not None:
+            return await self._generate_with_client(self._client, body, headers)
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            return await self._generate_with_client(client, body, headers)
+
+    async def _generate_with_client(
+        self,
+        client: httpx.AsyncClient,
+        body: dict[str, Any],
+        headers: dict[str, str],
+    ) -> GeneratedImageResponse:
+        url = f"{self.api_base}/image_generation"
+        try:
+            response = await client.post(url, headers=headers, json=body)
+        except httpx.TimeoutException as exc:
+            raise ImageGenerationError("MiniMax image generation timed out") from exc
+        except httpx.RequestError as exc:
+            raise ImageGenerationError(f"MiniMax image generation request failed: {exc}") from exc
+
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as exc:
+            detail = response.text[:500]
+            raise ImageGenerationError(f"MiniMax image generation failed: {detail}") from exc
+
+        payload = response.json()
+        images = _minimax_images_from_payload(payload)
+
+        if not images:
+            provider_error = payload.get("error") if isinstance(payload, dict) else None
+            if provider_error:
+                raise ImageGenerationError(f"MiniMax returned no images: {provider_error}")
+            raise ImageGenerationError("MiniMax returned no images for this request")
+
+        return GeneratedImageResponse(images=images, content="", raw=payload)
+
+
+def _minimax_images_from_payload(payload: dict[str, Any]) -> list[str]:
+    """Extract base64 images from MiniMax API response.
+
+    MiniMax returns images in ``data.image_base64`` (list of base64 strings).
+    """
+    images: list[str] = []
+    data = payload.get("data")
+    if not isinstance(data, dict):
+        return images
+    for b64 in data.get("image_base64") or []:
+        if isinstance(b64, str) and b64:
+            images.append(_b64_png_data_url(b64))
+    return images

nanobot/skills/image-generation/SKILL.md

@@ -88,6 +88,27 @@ AIHubMix `gpt-image-2-free` uses AIHubMix's unified predictions endpoint interna
 
 `providers.aihubmix.extraBody` can be used for provider-specific options. For example, `"extraBody": {"quality": "low"}` is optional but can make `gpt-image-2-free` faster and less likely to time out.
 
+For Gemini, the image tool supports two model families. Imagen 4 (`imagen-4.0-generate-001`) supports text-to-image only. Gemini Flash (`gemini-2.5-flash-image`) also supports reference-image edits. Configuration:
+
+```json
+{
+  "providers": {
+    "gemini": {
+      "apiKey": "AIza..."
+    }
+  },
+  "tools": {
+    "imageGeneration": {
+      "enabled": true,
+      "provider": "gemini",
+      "model": "imagen-4.0-generate-001"
+    }
+  }
+}
+```
+
+For Gemini models, `defaultImageSize` has no effect; use `defaultAspectRatio` instead. Imagen 4 supports `1:1`, `9:16`, `16:9`, `3:4`, and `4:3`.
+
 ## Examples
 
 Generate a new image:

tests/channels/test_signal_markdown.py

@@ -0,0 +1,525 @@
+"""Unit tests for the Signal markdown → plain text + textStyle converter."""
+
+from nanobot.channels.signal import _markdown_to_signal, _partition_styles
+from nanobot.utils.helpers import split_message
+
+
+def _utf16_len(s: str) -> int:
+    return len(s.encode("utf-16-le")) // 2
+
+
+def styles_for(plain: str, text_styles: list[str]) -> dict[str, list[str]]:
+    """Return a dict mapping each styled substring to its style list."""
+    result: dict[str, list[str]] = {}
+    for entry in text_styles:
+        start_s, length_s, style = entry.split(":", 2)
+        start, length = int(start_s), int(length_s)
+        span = plain[start : start + length]
+        result.setdefault(span, []).append(style)
+    return result
+
+
+def utf16_styles_for(plain: str, text_styles: list[str]) -> dict[str, list[str]]:
+    """Like styles_for, but slices `plain` using UTF-16 offsets (Signal's units)."""
+    encoded = plain.encode("utf-16-le")
+    result: dict[str, list[str]] = {}
+    for entry in text_styles:
+        start_s, length_s, style = entry.split(":", 2)
+        start, length = int(start_s), int(length_s)
+        span = encoded[start * 2 : (start + length) * 2].decode("utf-16-le")
+        result.setdefault(span, []).append(style)
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Basic cases
+# ---------------------------------------------------------------------------
+
+
+def test_empty():
+    plain, styles = _markdown_to_signal("")
+    assert plain == ""
+    assert styles == []
+
+
+def test_plain_text():
+    plain, styles = _markdown_to_signal("hello world")
+    assert plain == "hello world"
+    assert styles == []
+
+
+def test_bold_stars():
+    plain, styles = _markdown_to_signal("say **hello** now")
+    assert plain == "say hello now"
+    assert styles_for(plain, styles) == {"hello": ["BOLD"]}
+
+
+def test_bold_underscores():
+    plain, styles = _markdown_to_signal("say __hello__ now")
+    assert plain == "say hello now"
+    assert styles_for(plain, styles) == {"hello": ["BOLD"]}
+
+
+def test_italic_star():
+    plain, styles = _markdown_to_signal("say *hello* now")
+    assert plain == "say hello now"
+    assert styles_for(plain, styles) == {"hello": ["ITALIC"]}
+
+
+def test_italic_underscore():
+    plain, styles = _markdown_to_signal("say _hello_ now")
+    assert plain == "say hello now"
+    assert styles_for(plain, styles) == {"hello": ["ITALIC"]}
+
+
+def test_strikethrough():
+    plain, styles = _markdown_to_signal("say ~~hello~~ now")
+    assert plain == "say hello now"
+    assert styles_for(plain, styles) == {"hello": ["STRIKETHROUGH"]}
+
+
+# ---------------------------------------------------------------------------
+# Code
+# ---------------------------------------------------------------------------
+
+
+def test_inline_code():
+    plain, styles = _markdown_to_signal("run `ls -la` here")
+    assert plain == "run ls -la here"
+    assert styles_for(plain, styles) == {"ls -la": ["MONOSPACE"]}
+
+
+def test_code_block():
+    plain, styles = _markdown_to_signal("```\nprint('hi')\n```")
+    assert "print('hi')" in plain
+    assert styles_for(plain, styles).get("print('hi')\n") == ["MONOSPACE"] or "MONOSPACE" in str(
+        styles_for(plain, styles)
+    )
+
+
+def test_code_block_with_lang():
+    plain, styles = _markdown_to_signal("```python\ncode\n```")
+    assert "code" in plain
+    assert any("MONOSPACE" in s for s in styles)
+
+
+def test_code_block_not_processed_further():
+    """Markdown inside a code block must not be styled."""
+    plain, styles = _markdown_to_signal("```\n**not bold**\n```")
+    assert "**not bold**" in plain
+    # Only MONOSPACE should be applied, no BOLD
+    for entry in styles:
+        assert "BOLD" not in entry
+
+
+def test_inline_code_not_processed_further():
+    """Markdown inside inline code must not be styled."""
+    plain, styles = _markdown_to_signal("use `**raw**` please")
+    assert "**raw**" in plain
+    for entry in styles:
+        assert "BOLD" not in entry
+
+
+# ---------------------------------------------------------------------------
+# Headers
+# ---------------------------------------------------------------------------
+
+
+def test_header_becomes_bold():
+    plain, styles = _markdown_to_signal("# My Title")
+    assert plain == "My Title"
+    assert styles_for(plain, styles) == {"My Title": ["BOLD"]}
+
+
+def test_h2_becomes_bold():
+    plain, styles = _markdown_to_signal("## Sub-section")
+    assert plain == "Sub-section"
+    assert styles_for(plain, styles) == {"Sub-section": ["BOLD"]}
+
+
+# ---------------------------------------------------------------------------
+# Blockquotes
+# ---------------------------------------------------------------------------
+
+
+def test_blockquote_strips_marker():
+    plain, styles = _markdown_to_signal("> some quote")
+    assert plain == "some quote"
+    assert styles == []
+
+
+# ---------------------------------------------------------------------------
+# Lists
+# ---------------------------------------------------------------------------
+
+
+def test_bullet_dash():
+    plain, styles = _markdown_to_signal("- item one")
+    assert plain == "• item one"
+
+
+def test_bullet_star():
+    plain, styles = _markdown_to_signal("* item two")
+    assert plain == "• item two"
+
+
+def test_numbered_list():
+    plain, styles = _markdown_to_signal("1. first\n2. second")
+    assert "1. first" in plain
+    assert "2. second" in plain
+
+
+# ---------------------------------------------------------------------------
+# Links
+# ---------------------------------------------------------------------------
+
+
+def test_link_text_differs_from_url():
+    plain, styles = _markdown_to_signal("[Click here](https://example.com)")
+    assert plain == "Click here (https://example.com)"
+    assert styles == []
+
+
+def test_link_text_equals_url():
+    plain, styles = _markdown_to_signal("[https://example.com](https://example.com)")
+    assert plain == "https://example.com"
+    assert styles == []
+
+
+def test_link_text_equals_url_without_scheme():
+    plain, styles = _markdown_to_signal("[example.com](https://example.com)")
+    assert plain == "https://example.com"
+
+
+# ---------------------------------------------------------------------------
+# Mixed / nesting
+# ---------------------------------------------------------------------------
+
+
+def test_bold_and_italic_adjacent():
+    plain, styles = _markdown_to_signal("**bold** and *italic*")
+    assert plain == "bold and italic"
+    sd = styles_for(plain, styles)
+    assert sd.get("bold") == ["BOLD"]
+    assert sd.get("italic") == ["ITALIC"]
+
+
+def test_header_with_inline_code():
+    """Header becomes BOLD; code inside becomes MONOSPACE (not double-BOLD)."""
+    plain, styles = _markdown_to_signal("# Use `grep`")
+    assert plain == "Use grep"
+    sd = styles_for(plain, styles)
+    assert "BOLD" in sd.get("Use ", []) or "BOLD" in str(styles)
+    assert "MONOSPACE" in sd.get("grep", [])
+
+
+def test_multiline_mixed():
+    md = "**Title**\n\nSome *italic* text.\n\n- bullet\n- another"
+    plain, styles = _markdown_to_signal(md)
+    assert "Title" in plain
+    assert "italic" in plain
+    assert "• bullet" in plain
+    sd = styles_for(plain, styles)
+    assert "BOLD" in sd.get("Title", [])
+    assert "ITALIC" in sd.get("italic", [])
+
+
+# ---------------------------------------------------------------------------
+# Table rendering
+# ---------------------------------------------------------------------------
+
+
+def test_table_rendered_as_monospace():
+    md = "| A | B |\n| - | - |\n| 1 | 2 |"
+    plain, styles = _markdown_to_signal(md)
+    assert "A" in plain and "B" in plain
+    assert any("MONOSPACE" in s for s in styles)
+
+
+# ---------------------------------------------------------------------------
+# Style range format
+# ---------------------------------------------------------------------------
+
+
+def test_style_range_format():
+    """Each style entry must be 'start:length:STYLE'."""
+    _, styles = _markdown_to_signal("**bold** text")
+    for entry in styles:
+        parts = entry.split(":")
+        assert len(parts) == 3
+        assert parts[0].isdigit()
+        assert parts[1].isdigit()
+        assert parts[2] in {"BOLD", "ITALIC", "STRIKETHROUGH", "MONOSPACE", "SPOILER"}
+
+
+def test_style_ranges_are_within_bounds():
+    text = "hello **world** end"
+    plain, styles = _markdown_to_signal(text)
+    for entry in styles:
+        start_s, length_s, _ = entry.split(":", 2)
+        start, length = int(start_s), int(length_s)
+        assert start >= 0
+        assert start + length <= len(plain)
+
+
+# ---------------------------------------------------------------------------
+# Non-BMP / UTF-16 offsets
+#
+# Signal's BodyRange (and signal-cli's textStyle) interprets start/length in
+# UTF-16 code units. Python's len() counts code points, so characters outside
+# the BMP (emojis, supplementary CJK) shift offsets by +1 per occurrence.
+# ---------------------------------------------------------------------------
+
+
+def assert_within_utf16_bounds(plain: str, styles: list[str]) -> None:
+    limit = _utf16_len(plain)
+    for entry in styles:
+        start_s, length_s, _ = entry.split(":", 2)
+        start, length = int(start_s), int(length_s)
+        assert start >= 0
+        assert start + length <= limit, f"range {entry} exceeds utf-16 length {limit} of {plain!r}"
+
+
+def test_bold_with_emoji_inside():
+    plain, styles = _markdown_to_signal("**hi 🎉 bye**")
+    assert plain == "hi 🎉 bye"
+    assert utf16_styles_for(plain, styles) == {"hi 🎉 bye": ["BOLD"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_italic_with_trailing_emoji():
+    plain, styles = _markdown_to_signal("*bye 🎉*")
+    assert plain == "bye 🎉"
+    assert utf16_styles_for(plain, styles) == {"bye 🎉": ["ITALIC"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_bold_after_emoji_prefix():
+    plain, styles = _markdown_to_signal("🎉 **bold**")
+    assert plain == "🎉 bold"
+    assert utf16_styles_for(plain, styles) == {"bold": ["BOLD"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_bold_after_and_inside_emoji():
+    plain, styles = _markdown_to_signal("🎉 **a 🎊 b**")
+    assert plain == "🎉 a 🎊 b"
+    assert utf16_styles_for(plain, styles) == {"a 🎊 b": ["BOLD"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_supplementary_cjk_in_bold():
+    """Non-BMP CJK (U+20BB7) proves the bug is UTF-16, not emoji-specific."""
+    plain, styles = _markdown_to_signal("**𠮷野家**")
+    assert plain == "𠮷野家"
+    assert utf16_styles_for(plain, styles) == {"𠮷野家": ["BOLD"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_zwj_emoji_in_bold():
+    """ZWJ family sequence = multiple surrogate pairs + BMP ZWJs."""
+    plain, styles = _markdown_to_signal("**hi 👨‍👩‍👧 bye**")
+    assert plain == "hi 👨‍👩‍👧 bye"
+    assert utf16_styles_for(plain, styles) == {"hi 👨‍👩‍👧 bye": ["BOLD"]}
+    assert_within_utf16_bounds(plain, styles)
+
+
+def test_ascii_offsets_unchanged():
+    """ASCII-only path must produce the same offsets as before the UTF-16 fix."""
+    plain, styles = _markdown_to_signal("**bold** plain *it*")
+    assert plain == "bold plain it"
+    assert sorted(styles) == sorted(["0:4:BOLD", "11:2:ITALIC"])
+
+
+def test_reported_daily_brief_pattern():
+    """Regression for the reported bug: a single non-BMP emoji shifts every
+    subsequent styled span left by 1 UTF-16 unit, lopping off the last letter.
+    """
+    md = (
+        "**Weather**\n"
+        "- Conditions: 🌩️ Thunderstorms\n\n"
+        "**News**\n"
+        "*World*\n"
+        "*Local*\n\n"
+        "**Quote of the Day**"
+    )
+    plain, styles = _markdown_to_signal(md)
+    sd = utf16_styles_for(plain, styles)
+    assert sd.get("Weather") == ["BOLD"]
+    assert sd.get("News") == ["BOLD"]
+    assert sd.get("World") == ["ITALIC"]
+    assert sd.get("Local") == ["ITALIC"]
+    assert sd.get("Quote of the Day") == ["BOLD"]
+    assert_within_utf16_bounds(plain, styles)
+
+
+# ---------------------------------------------------------------------------
+# Chunk redistribution
+#
+# split_message can break a long Signal payload into multiple chunks. The
+# style ranges from _markdown_to_signal are anchored to the full text, so
+# they must be redistributed per-chunk with rebased offsets — otherwise
+# styles for chunks 1..N are silently lost.
+# ---------------------------------------------------------------------------
+
+
+def _resolve_chunk_styles(text: str, max_len: int) -> tuple[list[str], list[list[str]]]:
+    """Helper: full markdown → signal pipeline, including chunking."""
+    plain, styles = _markdown_to_signal(text)
+    chunks = split_message(plain, max_len) if plain else [""]
+    return chunks, _partition_styles(plain, chunks, styles)
+
+
+def test_partition_styles_single_chunk_passthrough():
+    plain, styles = _markdown_to_signal("**bold** plain *it*")
+    parts = _partition_styles(plain, [plain], styles)
+    assert parts == [styles]
+
+
+def test_partition_styles_no_styles():
+    plain = "hello world"
+    assert _partition_styles(plain, [plain], []) == [[]]
+    assert _partition_styles(plain, ["hello", "world"], []) == [[], []]
+
+
+def test_partition_styles_drops_styles_outside_chunks():
+    """Whitespace trimmed by split_message must not carry a style range."""
+    plain = "a   b"
+    # Fake a style spanning the trimmed whitespace only.
+    chunks = ["a", "b"]
+    parts = _partition_styles(plain, chunks, ["1:3:BOLD"])
+    assert parts == [[], []]
+
+
+def test_partition_styles_long_message_preserves_chunk_one_styles():
+    """A bold span deep in the message must follow the message into chunk 1."""
+    # Two ~30-char paragraphs separated by a blank line, then **tail**.
+    line_a = "alpha " * 5  # 30 chars, ends with space
+    line_b = "beta " * 5
+    md = f"{line_a.strip()}\n\n{line_b.strip()}\n\n**tail**"
+    plain, styles = _markdown_to_signal(md)
+    # Force a split between the paragraphs.
+    max_len = len(line_a.strip()) + 2  # fits paragraph A + the "\n\n"
+    chunks = split_message(plain, max_len)
+    assert len(chunks) >= 2, "test setup must produce a split"
+    parts = _partition_styles(plain, chunks, styles)
+    # The bold "tail" should land in the last chunk, with chunk-relative offset.
+    final_chunk = chunks[-1]
+    final_styles = parts[-1]
+    assert any("BOLD" in s for s in final_styles)
+    for entry in final_styles:
+        s, ln, _ = entry.split(":", 2)
+        start, length = int(s), int(ln)
+        slice_ = final_chunk.encode("utf-16-le")[start * 2 : (start + length) * 2].decode(
+            "utf-16-le"
+        )
+        assert slice_ == "tail"
+
+
+def test_partition_styles_chunk_zero_styles_unchanged():
+    """Styles entirely in chunk 0 keep their original offsets."""
+    md = "**head** middle and **tail**"
+    plain, styles = _markdown_to_signal(md)
+    # Split so chunk 0 contains "head" and part of the rest, chunk 1 contains "tail".
+    chunks = split_message(plain, 12)
+    assert len(chunks) >= 2
+    parts = _partition_styles(plain, chunks, styles)
+    # "head" lives in chunk 0; assert its offset is unchanged (chunk 0 starts at 0).
+    head_entries = [s for s in parts[0] if "BOLD" in s]
+    assert any(s.startswith("0:4:") for s in head_entries)
+
+
+def test_partition_styles_with_non_bmp_chunk_offset():
+    """Chunk-start offsets must be expressed in UTF-16 code units."""
+    # Emoji in chunk 0, bold in chunk 1.
+    md = "🎉 alpha beta gamma\n\n**tail**"
+    plain, styles = _markdown_to_signal(md)
+    chunks = split_message(plain, 18)
+    assert len(chunks) >= 2
+    parts = _partition_styles(plain, chunks, styles)
+    final_styles = parts[-1]
+    assert any("BOLD" in s for s in final_styles)
+    final_chunk = chunks[-1]
+    for entry in final_styles:
+        s, ln, _ = entry.split(":", 2)
+        start, length = int(s), int(ln)
+        slice_ = final_chunk.encode("utf-16-le")[start * 2 : (start + length) * 2].decode(
+            "utf-16-le"
+        )
+        assert slice_ == "tail"
+
+
+def test_partition_styles_range_spanning_chunks_is_split():
+    """A style range that straddles a chunk boundary gets sliced into both chunks."""
+    # Construct manually: plain = "abc def", style covers "abc def" (whole thing).
+    plain = "abc def"
+    chunks = split_message(plain, 4)  # "abc" / "def"
+    assert chunks == ["abc", "def"]
+    parts = _partition_styles(plain, chunks, ["0:7:BOLD"])
+    # Chunk 0 holds 0:3:BOLD, chunk 1 holds 0:3:BOLD (length=3 each, "def" only
+    # since the space was trimmed by lstrip).
+    assert parts[0] == ["0:3:BOLD"]
+    assert parts[1] == ["0:3:BOLD"]
+
+
+# ---------------------------------------------------------------------------
+# Adjacency, nesting, and malformed input
+# ---------------------------------------------------------------------------
+
+
+def test_bold_italic_combo_outer_bold_inner_italic():
+    """`**_combo_**` carries both BOLD and ITALIC over the same span."""
+    plain, styles = _markdown_to_signal("**_combo_**")
+    assert plain == "combo"
+    sd = styles_for(plain, styles)
+    assert set(sd.get("combo", [])) == {"BOLD", "ITALIC"}
+
+
+def test_bold_and_italic_adjacent_no_separator():
+    """`**bold***italic*` produces BOLD on `bold` and ITALIC on `italic`."""
+    plain, styles = _markdown_to_signal("**bold***italic*")
+    assert plain == "bolditalic"
+    sd = styles_for(plain, styles)
+    assert sd.get("bold") == ["BOLD"]
+    assert sd.get("italic") == ["ITALIC"]
+
+
+def test_unclosed_bold_falls_through_as_plain():
+    """An unmatched `**` opener round-trips as literal text with no style."""
+    plain, styles = _markdown_to_signal("**bold")
+    assert plain == "**bold"
+    assert styles == []
+
+
+def test_unclosed_inline_code_falls_through_as_plain():
+    """An unmatched backtick round-trips as literal text with no style."""
+    plain, styles = _markdown_to_signal("use `grep")
+    assert plain == "use `grep"
+    assert styles == []
+
+
+def test_inline_code_inside_blockquote():
+    """Blockquote prefix is stripped; inline code becomes MONOSPACE."""
+    plain, styles = _markdown_to_signal("> use `grep`")
+    assert plain == "use grep"
+    sd = styles_for(plain, styles)
+    assert sd.get("grep") == ["MONOSPACE"]
+
+
+def test_header_with_inner_bold_produces_contiguous_bold_ranges():
+    """`# **wrap** me` — header forces BOLD over the whole line; the inner `**`
+    splits the run, yielding two contiguous BOLD ranges that together cover
+    "wrap me". This is intentional — Signal renders adjacent same-style ranges
+    as a single visual span.
+    """
+    plain, styles = _markdown_to_signal("# **wrap** me")
+    assert plain == "wrap me"
+    # Both ranges are BOLD; collectively they cover the whole "wrap me".
+    bold_ranges = [s for s in styles if s.endswith(":BOLD")]
+    assert len(bold_ranges) == 2
+    covered = set()
+    for entry in bold_ranges:
+        start, length, _ = entry.split(":", 2)
+        for i in range(int(start), int(start) + int(length)):
+            covered.add(i)
+    assert covered == set(range(len(plain)))

tests/providers/test_image_generation.py

@@ -8,6 +8,7 @@ import pytest
 
 from nanobot.providers.image_generation import (
     AIHubMixImageGenerationClient,
+    GeminiImageGenerationClient,
     GeneratedImageResponse,
     ImageGenerationError,
     OpenRouterImageGenerationClient,
@@ -202,3 +203,137 @@ async def test_aihubmix_image_generation_downloads_url_response() -> None:
 
     assert response.images[0].startswith("data:image/png;base64,")
     assert fake.get_calls[0]["url"] == "https://cdn.example/image.png"
+
+
+RAW_B64 = PNG_DATA_URL.removeprefix("data:image/png;base64,")
+
+
+@pytest.mark.asyncio
+async def test_gemini_imagen_payload_and_response() -> None:
+    fake = FakeClient(
+        FakeResponse({"predictions": [{"bytesBase64Encoded": RAW_B64, "mimeType": "image/png"}]})
+    )
+    client = GeminiImageGenerationClient(
+        api_key="AIza-test",
+        api_base="https://generativelanguage.googleapis.com/v1beta",
+        client=fake,  # type: ignore[arg-type]
+    )
+
+    response = await client.generate(
+        prompt="a sunset",
+        model="imagen-4.0-generate-001",
+        aspect_ratio="16:9",
+    )
+
+    assert response.images == [PNG_DATA_URL]
+    assert response.content == ""
+    call = fake.calls[0]
+    assert call["url"].endswith(":predict")
+    assert call["headers"]["x-goog-api-key"] == "AIza-test"
+    assert "params" not in call
+    body = call["json"]
+    assert body["instances"] == [{"prompt": "a sunset"}]
+    assert body["parameters"]["sampleCount"] == 1
+    assert body["parameters"]["aspectRatio"] == "16:9"
+
+
+@pytest.mark.asyncio
+async def test_gemini_imagen_ignores_unsupported_aspect_ratio() -> None:
+    fake = FakeClient(
+        FakeResponse({"predictions": [{"bytesBase64Encoded": RAW_B64, "mimeType": "image/png"}]})
+    )
+    client = GeminiImageGenerationClient(api_key="AIza-test", client=fake)  # type: ignore[arg-type]
+
+    await client.generate(prompt="a sunset", model="imagen-4.0-generate-001", aspect_ratio="2:3")
+
+    body = fake.calls[0]["json"]
+    assert "aspectRatio" not in body["parameters"]
+
+
+@pytest.mark.asyncio
+async def test_gemini_flash_payload_and_response() -> None:
+    fake = FakeClient(
+        FakeResponse(
+            {
+                "candidates": [
+                    {
+                        "content": {
+                            "parts": [
+                                {"text": "here is your image"},
+                                {"inlineData": {"mimeType": "image/png", "data": RAW_B64}},
+                            ]
+                        }
+                    }
+                ]
+            }
+        )
+    )
+    client = GeminiImageGenerationClient(
+        api_key="AIza-test",
+        api_base="https://generativelanguage.googleapis.com/v1beta",
+        client=fake,  # type: ignore[arg-type]
+    )
+
+    response = await client.generate(
+        prompt="draw a cat",
+        model="gemini-2.0-flash-preview-image-generation",
+    )
+
+    assert response.images == [PNG_DATA_URL]
+    assert response.content == "here is your image"
+    call = fake.calls[0]
+    assert call["url"].endswith(":generateContent")
+    assert call["headers"]["x-goog-api-key"] == "AIza-test"
+    assert "params" not in call
+    body = call["json"]
+    assert body["generationConfig"]["responseModalities"] == ["TEXT", "IMAGE"]
+    assert body["contents"][0]["parts"][-1] == {"text": "draw a cat"}
+
+
+@pytest.mark.asyncio
+async def test_gemini_flash_reference_images(tmp_path: Path) -> None:
+    ref = tmp_path / "ref.png"
+    ref.write_bytes(PNG_BYTES)
+    fake = FakeClient(
+        FakeResponse(
+            {
+                "candidates": [
+                    {
+                        "content": {
+                            "parts": [{"inlineData": {"mimeType": "image/png", "data": RAW_B64}}]
+                        }
+                    }
+                ]
+            }
+        )
+    )
+    client = GeminiImageGenerationClient(api_key="AIza-test", client=fake)  # type: ignore[arg-type]
+
+    response = await client.generate(
+        prompt="edit this",
+        model="gemini-2.0-flash-preview-image-generation",
+        reference_images=[str(ref)],
+    )
+
+    assert response.images == [PNG_DATA_URL]
+    parts = fake.calls[0]["json"]["contents"][0]["parts"]
+    assert parts[0]["inlineData"]["mimeType"] == "image/png"
+    assert parts[0]["inlineData"]["data"].startswith("iVBOR")
+    assert parts[1] == {"text": "edit this"}
+
+
+@pytest.mark.asyncio
+async def test_gemini_requires_api_key() -> None:
+    client = GeminiImageGenerationClient(api_key=None)
+
+    with pytest.raises(ImageGenerationError, match="API key"):
+        await client.generate(prompt="draw", model="imagen-4.0-generate-001")
+
+
+@pytest.mark.asyncio
+async def test_gemini_no_images_raises() -> None:
+    fake = FakeClient(FakeResponse({"candidates": [{"content": {"parts": [{"text": "sorry"}]}}]}))
+    client = GeminiImageGenerationClient(api_key="AIza-test", client=fake)  # type: ignore[arg-type]
+
+    with pytest.raises(ImageGenerationError, match="returned no images"):
+        await client.generate(prompt="draw", model="gemini-2.0-flash-preview-image-generation")