feat(image-gen): add GPT Image 2 to FAL catalog (#13677)

Adds OpenAI's new GPT Image 2 model via FAL.ai, selectable through
`hermes tools` → Image Generation. SOTA text rendering (including CJK)
and world-aware photorealism.

- FAL_MODELS entry with image_size_preset style
- 4:3 presets on all aspect ratios — 16:9 (1024x576) falls below
  GPT-Image-2's 655,360 min-pixel floor and would be rejected
- quality pinned to medium (same rule as gpt-image-1.5) for
  predictable Nous Portal billing
- BYOK (openai_api_key) deliberately omitted from supports so all
  users stay on shared FAL billing
- 6 new tests covering preset mapping, quality pinning, and
  supports-whitelist integrity
- Docs table + aspect-ratio map updated

Live-tested end-to-end: 39.9s cold request, clean 1024x768 PNG

tests/tools/test_image_generation.py

@@ -136,6 +136,49 @@ class TestGptLiteralFamily:
         assert p["image_size"] == "1024x1536"
 
 
+class TestGptImage2Presets:
+    """GPT Image 2 uses preset enum sizes (not literal strings like 1.5).
+    Mapped to 4:3 variants so we stay above the 655,360 min-pixel floor
+    (16:9 presets at 1024x576 = 589,824 would be rejected)."""
+
+    def test_gpt2_landscape_uses_4_3_preset(self, image_tool):
+        p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "landscape")
+        assert p["image_size"] == "landscape_4_3"
+
+    def test_gpt2_square_uses_square_hd(self, image_tool):
+        p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "square")
+        assert p["image_size"] == "square_hd"
+
+    def test_gpt2_portrait_uses_4_3_preset(self, image_tool):
+        p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "portrait")
+        assert p["image_size"] == "portrait_4_3"
+
+    def test_gpt2_quality_pinned_to_medium(self, image_tool):
+        p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hi", "square")
+        assert p["quality"] == "medium"
+
+    def test_gpt2_strips_byok_and_unsupported_overrides(self, image_tool):
+        """openai_api_key (BYOK) is deliberately not in supports — all users
+        route through shared FAL billing. guidance_scale/num_inference_steps
+        aren't in the model's API surface either."""
+        p = image_tool._build_fal_payload(
+            "fal-ai/gpt-image-2", "hi", "square",
+            overrides={
+                "openai_api_key": "sk-...",
+                "guidance_scale": 7.5,
+                "num_inference_steps": 50,
+            },
+        )
+        assert "openai_api_key" not in p
+        assert "guidance_scale" not in p
+        assert "num_inference_steps" not in p
+
+    def test_gpt2_strips_seed_even_if_passed(self, image_tool):
+        # seed isn't in the GPT Image 2 API surface either.
+        p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hi", "square", seed=42)
+        assert "seed" not in p
+
+
 # ---------------------------------------------------------------------------
 # Supports whitelist — the main safety property
 # ---------------------------------------------------------------------------
@@ -231,10 +274,11 @@ class TestGptQualityPinnedToMedium:
         assert p["quality"] == "medium"
 
     def test_non_gpt_model_never_gets_quality(self, image_tool):
-        """quality is only meaningful for gpt-image-1.5 — other models should
-        never have it in their payload."""
+        """quality is only meaningful for GPT-Image models (1.5, 2) — other
+        models should never have it in their payload."""
+        gpt_models = {"fal-ai/gpt-image-1.5", "fal-ai/gpt-image-2"}
         for mid in image_tool.FAL_MODELS:
-            if mid == "fal-ai/gpt-image-1.5":
+            if mid in gpt_models:
                 continue
             p = image_tool._build_fal_payload(mid, "hi", "square")
             assert "quality" not in p, f"{mid} unexpectedly has 'quality' in payload"

tools/image_generation_tool.py

@@ -188,6 +188,38 @@ FAL_MODELS: Dict[str, Dict[str, Any]] = {
         },
         "upscale": False,
     },
+    "fal-ai/gpt-image-2": {
+        "display": "GPT Image 2",
+        "speed": "~20s",
+        "strengths": "SOTA text rendering + CJK, world-aware photorealism",
+        "price": "$0.04–0.06/image",
+        # GPT Image 2 uses FAL's standard preset enum (unlike 1.5's literal
+        # dimensions). We map to the 4:3 variants — the 16:9 presets
+        # (1024x576) fall below GPT-Image-2's 655,360 min-pixel requirement
+        # and would be rejected. 4:3 keeps us above the minimum on all
+        # three aspect ratios.
+        "size_style": "image_size_preset",
+        "sizes": {
+            "landscape": "landscape_4_3",   # 1024x768
+            "square": "square_hd",            # 1024x1024
+            "portrait": "portrait_4_3",       # 768x1024
+        },
+        "defaults": {
+            # Same quality pinning as gpt-image-1.5: medium keeps Nous
+            # Portal billing predictable. "high" is 3-4x the per-image
+            # cost at the same size; "low" is too rough for production use.
+            "quality": "medium",
+            "num_images": 1,
+            "output_format": "png",
+        },
+        "supports": {
+            "prompt", "image_size", "quality", "num_images", "output_format",
+            "sync_mode",
+            # openai_api_key (BYOK) intentionally omitted — all users go
+            # through the shared FAL billing path.
+        },
+        "upscale": False,
+    },
     "fal-ai/ideogram/v3": {
         "display": "Ideogram V3",
         "speed": "~5s",

website/docs/user-guide/features/image-generation.md

@@ -1,13 +1,13 @@
 ---
 title: Image Generation
-description: Generate images via FAL.ai — 8 models including FLUX 2, GPT-Image, Nano Banana Pro, Ideogram, Recraft V4 Pro, and more, selectable via `hermes tools`.
+description: Generate images via FAL.ai — 9 models including FLUX 2, GPT Image (1.5 & 2), Nano Banana Pro, Ideogram, Recraft V4 Pro, and more, selectable via `hermes tools`.
 sidebar_label: Image Generation
 sidebar_position: 6
 ---
 
 # Image Generation
 
-Hermes Agent generates images from text prompts via FAL.ai. Eight models are supported out of the box, each with different speed, quality, and cost tradeoffs. The active model is user-configurable via `hermes tools` and persists in `config.yaml`.
+Hermes Agent generates images from text prompts via FAL.ai. Nine models are supported out of the box, each with different speed, quality, and cost tradeoffs. The active model is user-configurable via `hermes tools` and persists in `config.yaml`.
 
 ## Supported Models
 
@@ -18,6 +18,7 @@ Hermes Agent generates images from text prompts via FAL.ai. Eight models are sup
 | `fal-ai/z-image/turbo` | ~2s | Bilingual EN/CN, 6B params | $0.005/MP |
 | `fal-ai/nano-banana-pro` | ~8s | Gemini 3 Pro, reasoning depth, text rendering | $0.15/image (1K) |
 | `fal-ai/gpt-image-1.5` | ~15s | Prompt adherence | $0.034/image |
+| `fal-ai/gpt-image-2` | ~20s | SOTA text rendering + CJK, world-aware photorealism | $0.04–0.06/image |
 | `fal-ai/ideogram/v3` | ~5s | Best typography | $0.03–0.09/image |
 | `fal-ai/recraft/v4/pro/text-to-image` | ~8s | Design, brand systems, production-ready | $0.25/image |
 | `fal-ai/qwen-image` | ~12s | LLM-based, complex text | $0.02/MP |
@@ -65,7 +66,7 @@ image_gen:
 
 ### GPT-Image Quality
 
-The `fal-ai/gpt-image-1.5` request quality is pinned to `medium` (~$0.034/image at 1024×1024). We don't expose the `low` / `high` tiers as a user-facing option so that Nous Portal billing stays predictable across all users — the cost spread between tiers is ~22×. If you want a cheaper GPT-Image option, pick a different model; if you want higher quality, use Klein 9B or Imagen-class models.
+The `fal-ai/gpt-image-1.5` and `fal-ai/gpt-image-2` request quality is pinned to `medium` (~$0.034–$0.06/image at 1024×1024). We don't expose the `low` / `high` tiers as a user-facing option so that Nous Portal billing stays predictable across all users — the cost spread between tiers is 3–22×. If you want a cheaper option, pick Klein 9B or Z-Image Turbo; if you want higher quality, use Nano Banana Pro or Recraft V4 Pro.
 
 ## Usage
 
@@ -87,11 +88,13 @@ Make me a futuristic cityscape, landscape orientation
 
 Every model accepts the same three aspect ratios from the agent's perspective. Internally, each model's native size spec is filled in automatically:
 
-| Agent input | image_size (flux/z-image/qwen/recraft/ideogram) | aspect_ratio (nano-banana-pro) | image_size (gpt-image) |
-|---|---|---|---|
-| `landscape` | `landscape_16_9` | `16:9` | `1536x1024` |
-| `square` | `square_hd` | `1:1` | `1024x1024` |
-| `portrait` | `portrait_16_9` | `9:16` | `1024x1536` |
+| Agent input | image_size (flux/z-image/qwen/recraft/ideogram) | aspect_ratio (nano-banana-pro) | image_size (gpt-image-1.5) | image_size (gpt-image-2) |
+|---|---|---|---|---|
+| `landscape` | `landscape_16_9` | `16:9` | `1536x1024` | `landscape_4_3` (1024×768) |
+| `square` | `square_hd` | `1:1` | `1024x1024` | `square_hd` (1024×1024) |
+| `portrait` | `portrait_16_9` | `9:16` | `1024x1536` | `portrait_4_3` (768×1024) |
+
+GPT Image 2 maps to 4:3 presets rather than 16:9 because its minimum pixel count is 655,360 — the `landscape_16_9` preset (1024×576 = 589,824) would be rejected.
 
 This translation happens in `_build_fal_payload()` — agent code never has to know about per-model schema differences.