meta/muse-image/text-to-image + paired meta/muse-image/edit, the FAL
listing of Meta's Muse Image model (launched on the Meta Model API in
Aug 2026 at $0.01/image).
- aspect_ratio size family (16:9 / 1:1 / 9:16 from the vendor's
21:9..9:21 enum); always sent on t2i for deterministic framing,
deliberately omitted on edits so Muse follows the input image.
- No seed in the vendor schema (Grok Imagine 2.0 precedent) - the
supports whitelist filters it.
- Edit takes 1-10 reference image_urls (max_reference_images=10).
Schema verified against FAL's OpenAPI for both endpoints. Live E2E
blocked by the FAL account balance lock (403), same as prior catalog
additions.
Keep the two tests that fail on main without the fix:
- test_fal_common: a keyed managed submit makes exactly one POST (plus the
negative arm: an unkeyed submit still goes through the SDK retry ladder)
- test_image_generation: the 409 BILLING_ERROR body surfaces
`unsupported_pricing_meter` instead of the generic "not yet enabled" text
Dropped from #106484: the duplicate video-plugin billing test (same helper,
same assertion), the `_fal_client = fake` / `import_fal_client` stub churn
and the `tools.lazy_deps` stub — fal-client is installed in CI (`--extra fal`)
so those fixtures were not needed; the `_load_fal_client` no-op fixture on
TestManagedGatewayErrorTranslation for the same reason.
Also drop the redundant `retry_request is None` re-check in
`_ManagedFalSyncClient.submit` — `__init__` already raises when the helper
is missing.
Avoid retrying idempotent managed FAL submissions because the retry can mask the initial billing failure. Surface structured Nous billing diagnostics consistently for image and video paths, with hermetic regression coverage.
(cherry picked from commit 289ce039e9a522dc8016ae4a512214c05d0a8bc0)
The Aug 8 default-on upscaling policy (66ea4e686) chained the Clarity
Upscaler after every sub-2MP generation. Clarity is an SD1.5 creative
tile-diffusion enhancer (creativity 0.35, "masterpiece" prompt prefix) —
it redraws content, which degraded output on 100% of generations for
models like GPT Image 2 and Ideogram whose value is precise text
rendering, CJK, and photorealistic detail.
Policy now: no model upscales by default, on FAL or Krea. The `upscale`
tool param remains as a per-call opt-in (`upscale: true`); explicit
requests still chain Clarity (FAL) / Krea Enhance as before.
- FAL catalog: all 17 default-on entries flipped to upscale=False
- Krea plugin: medium + medium-turbo per-model defaults flipped off
- Tool schema: upscale param described as opt-in with a fidelity warning
- Tests updated: catalog invariant now pins all-off; default-on cases
now assert no upscaler call
- Docs (en + zh) updated to the opt-in policy
Per review: upscaling should be the default behavior (like the original
flux-2-pro chain), not agent opt-in. Policy: every image model whose
native output is below ~2MP now sets upscale=True in its catalog —
users never silently get low-res images. Native hi-res models
(Seedream 5 Pro/Lite, Krea 2 Large) stay off to avoid paying to
upscale already-large output.
- FAL catalog: 16 models flipped to upscale=True (klein, z-image,
nano-banana pro/2/2-lite, gpt-image 1.5/2, ideogram v3/v4, recraft
v4/v4.1, qwen image/3, krea-2 medium on FAL, MAI 2.5 pro).
- Krea plugin: per-model upscale defaults (medium + medium-turbo ON at
1.5K native; large OFF at 2K native), precedence explicit kwarg >
image_gen.krea.upscale config > catalog default.
- The 'upscale' tool param remains as a per-call override in both
directions (false = fast draft, true = force on hi-res/edits).
- Video unchanged: opt-in only (default-on would double every video's
cost and latency).
- Sibling tests updated: routing/payload tests pass upscale=False where
the assertion targets the generation submit; catalog test now pins
the native-resolution policy instead of the flux-2-pro snapshot.
The generated-media surface previously had almost no upscaler coverage:
only fal-ai/flux-2-pro chained Clarity Upscaler (hardcoded catalog
default), every other image model returned ~1MP output with no high-res
path, and video had no upscaler at all. Krea's API treats the enhancer
as a standard second pass; this brings the same shape to Hermes.
- image_generate: new optional 'upscale' boolean in the tool schema.
Explicit true chains the backend upscaler on ANY model (including
edits); explicit false disables flux-2-pro's automatic default;
omitted keeps per-model catalog behavior. Response now reports
'upscaled' so the agent knows which resolution it got.
- FAL image path: explicit flag overrides the catalog 'upscale' default
(Clarity Upscaler, 2x). Failure falls back to the native image.
- Krea plugin: upscale=true chains Krea Enhance
(/generate/enhance/krea/enhance, 2x, prompt-guided) through the same
BYO/managed base URL + auth as generation, with a best-effort poll
loop that never fails a successful generation.
- video_generate: new optional 'upscale' boolean; FAL video plugin
chains ByteDance SeedVR2 (fal-ai/seedvr/upscale/video, 2x factor
mode). Providers without upscalers ignore the kwarg per the ABC
contract (documented in both ABCs).
Validation: targeted suites green (123 tests across 6 files, including
new coverage for override-wins/default-kept/failure-fallback on all
three paths); live E2E on direct FAL verified both chains end-to-end
(klein 9b + Clarity upscaled image; pixverse-v6 1s 360p + SeedVR2
upscaled video).
Follow-up on the cherry-picked contribution from @michaelsam94 (#51794):
replace display-string/exact-value snapshot assertions with invariant
checks per the no-change-detector-tests policy. Live-tested
fal-ai/nano-banana-2 and fal-ai/nano-banana-2/edit through the real
payload builders: both pass.
Second, deeper pass over tools/gateway/hermes_cli plus first pass over
the trees wave 1 missed (acp, acp_adapter, skills, computer_use, docker,
dashboard, conformance, monitoring, secret_sources, hermes_state,
providers). Same rubric as wave 1 (AGENTS.md test policy); security,
alternation/caching invariants, issue-number regressions, and E2E kept.
Real test-quality fixes found and rooted out along the way:
- tests/tools/test_command_guards.py made real auxiliary-LLM HTTPS calls
(DEFAULT_CONFIG smart-approval leaked in) — pinned approval
mode=manual via autouse fixture: 17.4s → 0.4s.
- test_model_switch_custom_providers.py / test_user_providers_model_switch.py
silently probed live provider catalogs (~2s/test) — stubbed
cached_provider_model_ids/provider_model_ids/fetch_api_models.
- test_telegram_noise_filter.py: 15-platform copy-paste matrix over
shared gateway.run logic → 3 representative platforms (55s → 3.9s).
- test_gateway_shutdown.py: stop()'s 5s interrupt-deadline loop spun on
MagicMock agents — interrupt.side_effect now clears _running_agents
(22s → 1.0s).
- test_gateway_inactivity_timeout.py poll-harness timings shrunk 3-5x
(24s → 1.1s); test_mcp_stability.py backoff/SIGTERM-grace sleeps
patched (15.4s → 2.5s); test_async_delegation.py negative-drain wait
5s → 0.5s.
- test_telegram_init_deadline.py: loop-block margin restored to 1.0s
with rationale comment — the watchdog-dump assertion needs the loop
blocked well past deadline+grace under parallel load (flaked once in
the 40-worker verification run at a 0.2s margin).
Verification: full hermetic suite via scripts/run_tests.sh —
2,438 files, 21,718 tests passed, 0 failed, 293.9s wall.
Suite totals vs original baseline: 46,820 → 19,757 test functions
(−57.8%), wall 583.5s → 293.9s (−50%), subprocess CPU 13,564s → 11,623s.
* feat(image-gen): add image-to-image / editing to image_generate
Brings image generation to parity with video generation: the unified
image_generate tool now edits/transforms a source image (image-to-image)
when given image_url / reference_image_urls, routing to each backend's
edit endpoint, exactly as video_generate routes to image-to-video.
- ImageGenProvider ABC: generate() gains keyword-only image_url +
reference_image_urls; new capabilities() declares modalities +
max_reference_images (defaults to text-only, backward compatible).
success_response gains a modality field; adds normalize_reference_images.
- image_generate tool: schema exposes image_url + reference_image_urls;
dynamic schema reflects the active model's actual edit capability so the
agent knows when image_url is honored. Handler + plugin dispatch forward
the new inputs; legacy/text-only providers get a clear modality_unsupported
error instead of silently dropping the source image.
- In-tree FAL: 7 models gain edit endpoints (flux-2-klein, flux-2-pro,
nano-banana-pro, gpt-image-1.5, gpt-image-2, ideogram/v3, qwen-image)
with per-model edit_supports whitelists + reference caps; routes to the
/edit endpoint and skips the upscaler for edits.
- Plugins: openai (images.edit, 16 refs), xai (/v1/images/edits via
grok-imagine-image-quality, JSON body per xAI docs), krea
(image_style_references, 10 refs). openai-codex stays text-only and
rejects edits with an actionable error.
- Tests: 15 new (payload, routing, dispatch forwarding, dynamic schema,
capabilities); updated 2 change-detector/lambda tests for the new schema.
- Docs: image-generation feature page, image-gen provider plugin guide,
tools reference.
* fix(image-gen): preserve legacy passthrough in fal/krea plugin tests
Two existing plugin tests asserted pre-image-to-image behavior:
- fal: forward image_url/reference_image_urls only when supplied, so a
text-to-image delegation stays byte-identical (no None kwargs).
- krea: keep dict-shaped image_style_references refs verbatim (the unified
string refs go through normalize_reference_images; legacy non-string ref
objects pass through unchanged) — fixes KeyError when callers pass the
richer Krea ref-object shape.
* fix(image-gen): clearer not-capable message for text-to-image-only models
When a text-to-image-only model (incl. gpt-image-2 on the Codex OAuth path,
which can't do editing through the Responses image_generation tool) gets a
source image, say 'this model is not capable of image-to-image / editing —
provide a text-only prompt' rather than sending the user shopping for other
backends. Applies to the openai-codex guard, the in-tree FAL no-edit-endpoint
error, and the dynamic tool-schema text-only line.
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
* feat(image_gen): upgrade Recraft V3 → V4 Pro, Nano Banana → Pro
Upstream asked for these two upgrades ASAP — the old entries show
stale models when newer, higher-quality versions are available on FAL.
Recraft V3 → Recraft V4 Pro
ID: fal-ai/recraft-v3 → fal-ai/recraft/v4/pro/text-to-image
Price: $0.04/image → $0.25/image (6x — V4 Pro is premium tier)
Schema: V4 dropped the required `style` enum entirely; defaults
handle taste now. Added `colors` and `background_color`
to supports for brand-palette control. `seed` is not
supported by V4 per the API docs.
Nano Banana → Nano Banana Pro
ID: fal-ai/nano-banana → fal-ai/nano-banana-pro
Price: $0.08/image → $0.15/image (1K); $0.30 at 4K
Schema: Aspect ratio family unchanged. Added `resolution`
(1K/2K/4K, default 1K for billing predictability),
`enable_web_search` (real-time info grounding, +$0.015),
and `limit_generations` (force exactly 1 image).
Architecture: Gemini 2.5 Flash → Gemini 3 Pro Image. Quality
and reasoning depth improved; slower (~6s → ~8s).
Migration: users who had the old IDs in `image_gen.model` will
fall through the existing 'unknown model → default' warning path
in `_resolve_fal_model()` and get the Klein 9B default on the next
run. Re-run `hermes tools` → Image Generation to pick the new
version. No silent cost-upgrade aliasing — the 2-6x price jump
on these tiers warrants explicit user re-selection.
Portal note: both new model IDs need to be allowlisted on the
Nous fal-queue-gateway alongside the previous 7 additions, or
users on Nous Subscription will see the 'managed gateway rejected
model' error we added previously (which is clear and
self-remediating, just noisy).
* docs: wrap '<1s' in backticks to unblock MDX compilation
Docusaurus's MDX parser treats unquoted '<' as the start of JSX, and
'<1s' fails because '1' isn't a valid tag-name start character. This
was broken on main since PR #11265 (never noticed because
docs-site-checks was failing on OTHER issues at the time and we
admin-merged through it).
Wrapping in backticks also gives the cell monospace styling which
reads more cleanly alongside the inline-code model ID in the same row.
The other '<1s' occurrence (line 52) is inside a fenced code block
and is already safe — code fences bypass MDX parsing.
* 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.
Teknium