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

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