feat(image_gen): multi-model FAL support with picker in hermes tools (#11265)

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* feat(image_gen): multi-model FAL support with picker in hermes tools

Adds 8 FAL text-to-image models selectable via `hermes tools` →
Image Generation → (FAL.ai | Nous Subscription) → model picker.

Models supported:
- fal-ai/flux-2/klein/9b (new default, <1s, $0.006/MP)
- fal-ai/flux-2-pro (previous default, kept backward-compat upscaling)
- fal-ai/z-image/turbo (Tongyi-MAI, bilingual EN/CN)
- fal-ai/nano-banana (Gemini 2.5 Flash Image)
- fal-ai/gpt-image-1.5 (with quality tier: low/medium/high)
- fal-ai/ideogram/v3 (best typography)
- fal-ai/recraft-v3 (vector, brand styles)
- fal-ai/qwen-image (LLM-based)

Architecture:
- FAL_MODELS catalog declares per-model size family, defaults, supports
  whitelist, and upscale flag. Three size families handled uniformly:
  image_size_preset (flux family), aspect_ratio (nano-banana), and
  gpt_literal (gpt-image-1.5).
- _build_fal_payload() translates unified inputs (prompt + aspect_ratio)
  into model-specific payloads, merges defaults, applies caller overrides,
  wires GPT quality_setting, then filters to the supports whitelist — so
  models never receive rejected keys.
- IMAGEGEN_BACKENDS registry in tools_config prepares for future imagegen
  providers (Replicate, Stability, etc.); each provider entry tags itself
  with imagegen_backend: 'fal' to select the right catalog.
- Upscaler (Clarity) defaults off for new models (preserves <1s value
  prop), on for flux-2-pro (backward-compat). Per-model via FAL_MODELS.

Config:
  image_gen.model           = fal-ai/flux-2/klein/9b  (new)
  image_gen.quality_setting = medium                  (new, GPT only)
  image_gen.use_gateway     = bool                    (existing)

Agent-facing schema unchanged (prompt + aspect_ratio only) — model
choice is a user-level config decision, not an agent-level arg.

Picker uses curses_radiolist (arrow keys, auto numbered-fallback on
non-TTY). Column-aligned: Model / Speed / Strengths / Price.

Docs: image-generation.md rewritten with the model table and picker
walkthrough. tools-reference, tool-gateway, overview updated to drop
the stale "FLUX 2 Pro" wording.

Tests: 42 new in tests/tools/test_image_generation.py covering catalog
integrity, all 3 size families, supports filter, default merging, GPT
quality wiring, model resolution fallback. 8 new in
tests/hermes_cli/test_tools_config.py for picker wiring (registry,
config writes, GPT quality follow-up prompt, corrupt-config repair).

* feat(image_gen): translate managed-gateway 4xx to actionable error

When the Nous Subscription managed FAL proxy rejects a model with 4xx
(likely portal-side allowlist miss or billing gate), surface a clear
message explaining:
  1. The rejected model ID + HTTP status
  2. Two remediation paths: set FAL_KEY for direct access, or
     pick a different model via `hermes tools`

5xx, connection errors, and direct-FAL errors pass through unchanged
(those have different root causes and reasonable native messages).

Motivation: new FAL models added to this release (flux-2-klein-9b,
z-image-turbo, nano-banana, gpt-image-1.5, ideogram-v3, recraft-v3,
qwen-image) are untested against the Nous Portal proxy. If the portal
allowlists model IDs, users on Nous Subscription will hit cryptic
4xx errors without guidance on how to work around it.

Tests: 8 new cases covering status extraction across httpx/fal error
shapes and 4xx-vs-5xx-vs-ConnectionError translation policy.

Docs: brief note in image-generation.md for Nous subscribers.

Operator action (Nous Portal side): verify that fal-queue-gateway
passes through these 7 new FAL model IDs. If the proxy has an
allowlist, add them; otherwise Nous Subscription users will see the
new translated error and fall back to direct FAL.

* feat(image_gen): pin GPT-Image quality to medium (no user choice)

Previously the tools picker asked a follow-up question for GPT-Image
quality tier (low / medium / high) and persisted the answer to
`image_gen.quality_setting`. This created two problems:

1. Nous Portal billing complexity — the 22x cost spread between tiers
   ($0.009 low / $0.20 high) forces the gateway to meter per-tier per
   user, which the portal team can't easily support at launch.
2. User footgun — anyone picking `high` by mistake burns through
   credit ~6x faster than `medium`.

This commit pins quality at medium by baking it into FAL_MODELS
defaults for gpt-image-1.5 and removes all user-facing override paths:

- Removed `_resolve_gpt_quality()` runtime lookup
- Removed `honors_quality_setting` flag on the model entry
- Removed `_configure_gpt_quality_setting()` picker helper
- Removed `_GPT_QUALITY_CHOICES` constant
- Removed the follow-up prompt call in `_configure_imagegen_model()`
- Even if a user manually edits `image_gen.quality_setting` in
  config.yaml, no code path reads it — always sends medium.

Tests:
- Replaced TestGptQualitySetting (6 tests) with TestGptQualityPinnedToMedium
  (5 tests) — proves medium is baked in, config is ignored, flag is
  removed, helper is removed, non-gpt models never get quality.
- Replaced test_picker_with_gpt_image_also_prompts_quality with
  test_picker_with_gpt_image_does_not_prompt_quality — proves only 1
  picker call fires when gpt-image is selected (no quality follow-up).

Docs updated: image-generation.md replaces the quality-tier table
with a short note explaining the pinning decision.

* docs(image_gen): drop stale 'wires GPT quality tier' line from internals section

Caught in a cleanup sweep after pinning quality to medium. The
"How It Works Internally" walkthrough still described the removed
quality-wiring step.

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Teknium 2026-04-16 20:19:53 -07:00 • committed by GitHub

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