064b6e40c5
The Codex image backend rejected our own request shape for every account, and we then translated that rejection into "Image generation is not enabled for the current Codex account. Switch the image provider to OpenAI API key, FAL, or xAI." — telling every affected user to abandon a provider that had never actually been tried. That message is why this reads as a setup failure rather than a bug: the wire error was replaced with a confident, wrong diagnosis. Removes the classifier and its exception, so any HTTP failure surfaces verbatim. The paired request-shape fix (previous commit) is what makes the 400 stop happening; this commit makes the next one diagnosable. Also fixes error-body truncation: bodies were head-truncated at 500 chars, and Codex error payloads can carry hundreds of bytes of leading metadata, so the user got a wall of padding and no message. _summarize_error_body() prefers the parsed error.message and falls back to a truncated raw body. Docs: drop the unqualified image-to-image claim for the Codex backend and note that the hosted tool call cannot be forced, so it is best-effort. Verified E2E against a local fake Codex backend: success path writes a real PNG with no tool_choice on the wire; the 400 path now returns api_error carrying "Tool choice 'image_generation' not found in 'tools' parameter" (148 chars) instead of the entitlement message. Sabotage run confirms all 4 regression tests fail when the old behavior is restored. Refs #19505, #49008, #31335.
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| title | description | sidebar_label | sidebar_position |
|---|---|---|---|
| Image Generation | Generate images via FAL.ai — 11 models including FLUX 2, GPT Image (1.5 & 2), Nano Banana Pro, Ideogram, Recraft V4 Pro, Krea 2, and more, selectable via `hermes tools`. | Image Generation | 6 |
Image Generation
Hermes Agent generates images from text prompts via FAL.ai. Eleven 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
| Model | Speed | Strengths | Price |
|---|---|---|---|
fal-ai/flux-2/klein/9b (default) |
<1s |
Fast, crisp text | $0.006/MP |
fal-ai/flux-2-pro |
~6s | Studio photorealism | $0.03/MP |
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 |
fal-ai/krea/v2/medium/text-to-image |
~15-25s | Illustration, anime, painting, expressive/artistic styles | $0.030–0.035/image |
fal-ai/krea/v2/large/text-to-image |
~25-60s | Photorealism, raw textured looks (motion blur, grain, film) | $0.060–0.065/image |
Prices are FAL's pricing at time of writing; check fal.ai for current numbers.
Setup
:::tip Nous Subscribers
If you have a paid Nous Portal subscription, you can use image generation through the Tool Gateway without a FAL API key. Your model selection persists across both paths. New installs can run hermes setup --portal to log in and turn on every gateway tool at once; existing installs can pick Nous Subscription as the image-gen backend via hermes tools.
If the managed gateway returns HTTP 4xx for a specific model, that model isn't yet proxied on the portal side — the agent will tell you so, with remediation steps (set FAL_KEY for direct access, or pick a different model).
:::
Get a FAL API Key
- Sign up at fal.ai
- Generate an API key from your dashboard
Configure and Pick a Model
Run the tools command:
hermes tools
Navigate to 🎨 Image Generation, pick your backend (Nous Subscription or FAL.ai), then the picker shows all supported models in a column-aligned table — arrow keys to navigate, Enter to select:
Model Speed Strengths Price
fal-ai/flux-2/klein/9b <1s Fast, crisp text $0.006/MP ← currently in use
fal-ai/flux-2-pro ~6s Studio photorealism $0.03/MP
fal-ai/z-image/turbo ~2s Bilingual EN/CN, 6B $0.005/MP
...
Your selection is saved to config.yaml:
image_gen:
model: fal-ai/flux-2/klein/9b
use_gateway: false # true if using Nous Subscription
GPT-Image Quality
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
The agent-facing schema is intentionally minimal — the model picks up whatever you've configured:
Generate an image of a serene mountain landscape with cherry blossoms
Create a square portrait of a wise old owl — use the typography model
Make me a futuristic cityscape, landscape orientation
Image-to-Image / Editing
The same image_generate tool also edits existing images when the active
model supports it — pass a source image and the backend routes to its editing
endpoint automatically (mirrors how video_generate handles image-to-video).
Omit the source image and it's plain text-to-image.
Take this photo and make it a rainy Tokyo street at night → <image>
Blend these two product shots into one hero image → <image1> <image2>
Two inputs drive the edit:
image_url— the primary source image to edit/transform (public URL or local path).reference_image_urls— additional style/composition references (capped per-model).
Which backends support editing
| Backend | Image-to-image | Reference cap | How |
|---|---|---|---|
| FAL.ai (edit-capable models below) | ✓ | up to 9 | routes to the model's /edit endpoint |
OpenAI (gpt-image-2) |
✓ | up to 16 | images.edit() |
| xAI (Grok Imagine) | ✓ | 1 | /v1/images/edits (grok-imagine-image-quality) |
Krea (Krea 2) |
✓ | up to 10 | reference-guided generation (image_style_references) |
| OpenAI (Codex auth) | ✓ | up to 16 | Codex Responses image_generation tool with input_image content parts |
FAL models with an editing endpoint: flux-2/klein/9b, flux-2-pro,
nano-banana-pro, gpt-image-1.5, gpt-image-2, ideogram/v3, and
qwen-image. Pure text-to-image FAL models (z-image/turbo, recraft,
krea/*) reject image inputs with a clear error pointing you at an
edit-capable model.
:::note OpenAI (Codex auth) is best-effort
The Codex surface (chatgpt.com/backend-api/codex) hosts image_generation
as a tool the chat model may call, and Hermes cannot force the call — the
backend rejects every tool_choice shape for hosted tools, so the request
relies on instructions to steer the model. When the host model declines to
invoke the tool, the call fails with empty_response. Whether the hosted
image tool is reachable at all has also been reported to vary between
accounts. If you need image generation to work deterministically, configure
the OpenAI (API key), FAL, or xAI backend instead.
:::
The active model's editing capability is surfaced in the tool description at
runtime, so the agent knows whether image_url will be honored before it
calls the tool.
Aspect Ratios
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-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.
Automatic Upscaling
Upscaling via FAL's Clarity Upscaler is gated per-model:
| Model | Upscale? | Why |
|---|---|---|
fal-ai/flux-2-pro |
✓ | Backward-compat (was the pre-picker default) |
| All others | ✗ | Fast models would lose their sub-second value prop; hi-res models don't need it |
When upscaling runs, it uses these settings:
| Setting | Value |
|---|---|
| Upscale factor | 2× |
| Creativity | 0.35 |
| Resemblance | 0.6 |
| Guidance scale | 4 |
| Inference steps | 18 |
If upscaling fails (network issue, rate limit), the original image is returned automatically.
How It Works Internally
- Model resolution —
_resolve_fal_model()readsimage_gen.modelfromconfig.yaml, falls back to theFAL_IMAGE_MODELenv var, then tofal-ai/flux-2/klein/9b. - Payload building —
_build_fal_payload()translates youraspect_ratiointo the model's native format (preset enum, aspect-ratio enum, or GPT literal), merges the model's default params, applies any caller overrides, then filters to the model'ssupportswhitelist so unsupported keys are never sent. - Submission —
_submit_fal_request()routes via direct FAL credentials or the managed Nous gateway. - Upscaling — runs only if the model's metadata has
upscale: True. - Delivery — final image URL returned to the agent, which emits a
MEDIA:<url>tag that platform adapters convert to native media.
Debugging
Enable debug logging:
export IMAGE_TOOLS_DEBUG=true
Debug logs go to ./logs/image_tools_debug_<session_id>.json with per-call details (model, parameters, timing, errors).
Platform Delivery
| Platform | Delivery |
|---|---|
| CLI | Image URL printed as markdown  — click to open |
| Telegram | Photo message with the prompt as caption |
| Discord | Embedded in a message |
| Slack | URL unfurled by Slack |
| Media message | |
| Others | URL in plain text |
Limitations
- Requires credentials for the active backend (FAL
FAL_KEY/ Nous Subscription,OPENAI_API_KEY, xAI OAuth,KREA_API_KEY) - Editing is model-dependent — image-to-image works only on edit-capable models (see the table above); text-to-image-only models reject image inputs with a clear error
- Temporary URLs — backends return hosted URLs that expire after hours/days; Hermes materializes them to the local cache so delivery still works after expiry
- Per-model constraints — some models don't support
seed,num_inference_steps, etc. Thesupports/edit_supportsfilter silently drops unsupported params; this is expected behavior