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

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

1. Sign up at fal.ai
2. 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)	✗	—	text-to-image only

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.

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

1. Model resolution — _resolve_fal_model() reads image_gen.model from config.yaml, falls back to the FAL_IMAGE_MODEL env var, then to fal-ai/flux-2/klein/9b.
2. Payload building — _build_fal_payload() translates your aspect_ratio into 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's supports whitelist so unsupported keys are never sent.
3. Submission — _submit_fal_request() routes via direct FAL credentials or the managed Nous gateway.
4. Upscaling — runs only if the model's metadata has upscale: True.
5. 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
WhatsApp	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. The supports / edit_supports filter silently drops unsupported params; this is expected behavior