Image Generation

Mibyan 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 mibyan 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

Nous SubscribersIf 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 mibyan 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 mibyan tools.The Nous Subscription row is the only managed row. Its model picker spans every gateway the subscription runs — the FAL catalog above, native Krea 2 (krea-2-medium, krea-2-large, krea-2-medium-turbo) and any Nous Portal image models — each model listed once, and the model you pick decides which gateway serves the request. Free tool-pool accounts see the FAL models only; Krea and Portal models are paid-subscription. With a Krea 2 model selected, image_generate also offers Krea’s creativity setting and its intensity, complexity and movement sliders (-100 to 100).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 (switch to FAL.ai in mibyan tools with your own 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:

mibyan 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:
  provider: fal                 # `nous` if you picked Nous Subscription
  model: fal-ai/flux-2/klein/9b
  max_parallel_requests: 4      # concurrent images in one tool-call batch

image_gen.provider is the single selection key: nous routes through the managed Tool Gateway; a vendor name (fal, openai, xai, krea, …) goes direct with your own key. The runtime always follows this stored selection — a FAL_KEY in .env is ignored while provider: nous, and provider: fal without FAL_KEY errors with image_gen is configured to use fal (set via mibyan tools), but FAL_KEY is not set. Run 'mibyan tools' to change it. rather than silently rerouting. Change providers via mibyan tools, not by adding/removing keys. (The old use_gateway boolean is legacy — still read as nous when true, but never written anymore.) max_parallel_requests defaults to 4. Mibyan clamps it to at least one and to the global tool-worker limit, so image providers receive bounded parallel requests without allowing an image batch to bypass the agent’s concurrency cap.

OpenRouter: the full Image API catalog

With image_gen.provider: openrouter, the model picker lists OpenRouter’s entire live image catalog — the dedicated Image API models (Seedream, FLUX.2, Recraft, Qwen Image, MAI, Krea, Riverflow, Grok Imagine, and more — 40+ ids) merged with the chat-completions image models. The catalog is fetched live from GET /images/models and GET /models, so new models appear in the picker as soon as OpenRouter serves them; no Mibyan update needed. Generation routes each model to the surface that serves it (dedicated POST /images/generations vs chat-completions) automatically. Nous Portal proxies the chat-completions protocol only, so its picker offers the chat-served models. Optional per-request knobs for Image API models go under the scoped config section (or OPENROUTER_IMAGE_API_* env vars):

image_gen:
  provider: openrouter
  model: bytedance-seed/seedream-4.5
  openrouter:
    resolution: 2K        # model-dependent: 1K / 2K / 4K
    quality: high         # gpt-image models
    output_format: png

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.

Meta Model API: Muse Image

With image_gen.provider: meta-ai, images are generated through the Meta Model API (https://api.meta.ai/v1), the same OpenAI-compatible endpoint that serves the Muse Spark chat models. It is the image-gen companion to the bundled meta-ai chat provider.

Model	Speed	Strengths	Price
`muse-image-1.0` (default)	~10s	Meta Model API image generation	$0.01/image

image_gen:
  provider: meta-ai
  model: muse-image-1.0

Auth reuses the same env vars as the Meta chat provider — MODEL_API_KEY (Meta’s documented name), with META_API_KEY / META_MODEL_API_KEY accepted as aliases. Set META_BASE_URL to point at a proxy or alternate host. Text-to-image only for now; responses are saved to $mibyan_HOME/cache/images/.

FAL: GPT Image 2.5

Select GPT Image 2.5 Flare or GPT Image 2.5 Sunburst under mibyan tools → Image Generation → FAL.ai. The model IDs are:

openai/gpt-image-2.5/flare/text-to-image
openai/gpt-image-2.5/sunburst/text-to-image

For example:

mibyan config set image_gen.provider fal
mibyan config set image_gen.model openai/gpt-image-2.5/flare/text-to-image

Providing image_url or reference images automatically selects the corresponding openai/gpt-image-2.5/flare/edit or openai/gpt-image-2.5/sunburst/edit endpoint. Both accept up to 16 source images. Mibyan pins quality to medium, matching its existing FAL GPT Image policy rather than FAL’s higher-cost high default. Landscape and portrait use 4:3 presets to satisfy the minimum pixel count; square uses square_hd. Upscaling remains off unless requested. FAL bills by tokens, not a fixed image price:

5/M text input,

1.25/M cached text input,

10/M text output,

8/M image input,

2/M cached image input, and

30/M image output, rounded up to $0.0001 per request. See the Flare and Sunburst pages. Direct FAL requires a funded FAL_KEY; managed-gateway availability depends on that gateway’s endpoint allowlist and is not implied by FAL availability. Existing provider and model defaults are unchanged.

OpenAI API: GPT Image 2.5

The OpenAI provider supports GPT Image 2.5 Flare (fast everyday creation) and Sunburst (precision generation and editing), using OPENAI_API_KEY. Select them through mibyan tools → Image Generation → OpenAI, or set:

mibyan config set image_gen.provider openai
mibyan config set image_gen.openai.model gpt-image-2.5-flare

gpt-image-2.5-flare and gpt-image-2.5-sunburst use automatic quality. Append -low, -medium, -high, -xhigh, or -max to select a fixed quality, for example gpt-image-2.5-sunburst-high. Both support generation and editing with up to 16 reference images. Existing GPT Image 2 selections and the gpt-image-2-medium default are unchanged. This is paid API usage, separate from a ChatGPT/Codex subscription. Both models cost

5 per million text-input tokens,

8 per million image-input tokens, and

30 per million image-output tokens (cached input rates are

1.25 and $2, respectively). Per-image cost varies with usage; the GPT Image 2 calculator does not estimate 2.5 token consumption. See the official Flare and Sunburst docs. The OpenAI (Codex auth) provider does not offer 2.5. The Codex backend accepts any model value (including nonexistent ids) and generates with its own server-managed engine, so a “selected” Flare or Sunburst tier would be a label with no effect. Pick the direct OpenAI API provider or FAL for 2.5.

Custom OpenAI-compatible image endpoint

The OpenAI provider can point at any OpenAI-compatible /v1/images/generations endpoint (a local gateway, a task-scoped proxy, a third-party API gateway), independently of the chat provider, and take its key from a variable of your choice:

image_gen:
  provider: openai
  openai:
    model: gpt-image-2-medium
    base_url: http://localhost:18081/v1   # → OPENAI_BASE_URL → api.openai.com
    key_env: IMAGE_GATEWAY_TOKEN          # → OPENAI_API_KEY

Only the variable name is stored in config.yaml; the secret stays in .env or the process environment. Availability checks and generation use the same resolution, so a configured key_env is enough — no OPENAI_API_KEY is required. Requests go through Mibyan’ own HTTP client, which honours HTTP(S)_PROXY/NO_PROXY but ignores macOS system proxies (whose exception list is invisible to Python), so localhost endpoints connect directly. The OpenAI-Project header is sent blank on image requests: an OPENAI_PROJECT_ID set for chat otherwise makes the image endpoint return 403 model_not_found on projects with a model allow-list, while the key itself already carries the project. Gateway model names. Catalog ids are mapped for OpenAI: gpt-image-2-medium is sent as model: gpt-image-2 + quality: medium. Any other value of image_gen.openai.model (or OPENAI_IMAGE_MODEL) is sent verbatim as model with no quality field, so a gateway that serves its own image model names (custom-image-model, grok-imagine-image, …) receives exactly that id and never sees a quality enum it might reject. The shared top-level image_gen.model is never passed through — it can hold another provider’s id (a FAL path, for instance) from an earlier selection. Reusing a named custom endpoint. If the gateway is already declared under providers: for chat, point the image provider at it by name instead of repeating its URL and key:

providers:
  my-gateway:
    name: My Gateway
    api: https://gateway.example.com/v1
    key_env: MY_GATEWAY_KEY

image_gen:
  provider: openai
  openai:
    provider: my-gateway        # inherits api + key_env from providers.my-gateway
    model: grok-imagine-image   # sent verbatim, no quality

Resolution order is image_gen.openai.base_url → the named endpoint’s URL → OPENAI_BASE_URL, and the variable named by image_gen.openai.key_env → the named endpoint’s api_key/key_env → OPENAI_API_KEY; an explicit base_url or key_env next to provider therefore overrides that part of the endpoint. A name that matches no providers: entry is logged as a warning and ignored.

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 16 (per model)	routes to the model’s `/edit` endpoint
OpenAI (GPT Image 2 / 2.5 Flare / Sunburst)	✓	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`); local files are embedded in the request, up to 3 MB in total
OpenAI (Codex auth)	✓	up to 16	`POST /backend-api/codex/images/edits` with inline `images[]` data URLs (remote URLs are fetched client-side)
OpenRouter (Image API models)	✓	up to 14–16 (per model)	`input_references` on `POST /images/generations`; chat-served models use `image_url` content parts (up to 3)

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, plus GPT Image 2.5 Flare and Sunburst above. 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.

OpenAI (Codex auth): the backend decides quality and sizeMibyan posts straight to the Codex backend’s native images/generations / images/edits endpoints (the same route the official Codex client uses), so no chat model is involved and the call does not depend on which chat models your ChatGPT plan currently has. The backend, however, treats model, quality and size as advisory: it may return a different quality tier or geometry than requested (a portrait request can come back square). The result carries reported_quality, reported_size and pixel_size alongside what was requested, plus imagegen_request_id for OpenAI support. For exact control over quality and size, 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.

Upscaling

Opt-in only

No model upscales by default. Modern image models emit their best quality natively, and the available upscalers are creative enhancers (diffusion passes) that can subtly redraw content — degrading rendered text, faces, and fine detail. Upscaling only runs when the agent explicitly requests it.

The `upscale` parameter (per-call opt-in)

upscale: true — chain a high-resolution pass after generation:

Backend	Upscaler
FAL.ai	Clarity Upscaler (2×, +$0.03/MP)
Krea	Krea Enhance (2×, up to 8K ceiling)
Other backends	no upscaler; native resolution returned

upscale: false / omitted — native resolution (the default)

video_generate also accepts upscale: true on the FAL backend, chaining ByteDance’s SeedVR2 video upscaler (2×, $0.001/MP of output video) after generation. When the FAL image pass 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. The response reports upscaled: true/false so the agent knows which resolution it got.

How It Works Internally

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.
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.
Submission — _submit_fal_request() routes via direct FAL credentials or the managed Nous gateway, according to the stored image_gen.provider selection.
Upscaling — runs only when the agent passed upscale: true; every model’s catalog default is off.
Delivery — final image URL returned to the agent, which emits a MEDIA:<url> tag that platform adapters convert to native media.
Usage accounting — token-billed image models (OpenRouter chat-image and Image API models such as google/gemini-3.1-flash-lite-image, OpenAI gpt-image) return real token counts, so each call is recorded in session_model_usage as task image_generation under the billing provider and model, and shows up in mibyan insights and the dashboard’s Usage analytics alongside other model calls. Per-image backends (FAL, xAI, Krea, …) return no token usage and are not recorded there.

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; Mibyan 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

تطبيق ديسكتوب

البداية

استخدام مبيان

الميزات

منصات المراسلة

التكاملات

الأدلة والدروس

دليل المطوّر

المرجع

Supported Models

Setup

Get a FAL API Key

Configure and Pick a Model

OpenRouter: the full Image API catalog

GPT-Image Quality

Meta Model API: Muse Image

FAL: GPT Image 2.5

OpenAI API: GPT Image 2.5

Custom OpenAI-compatible image endpoint

Usage

Image-to-Image / Editing

Which backends support editing

Aspect Ratios

Upscaling

Opt-in only

The `upscale` parameter (per-call opt-in)

How It Works Internally

Debugging

Platform Delivery

Limitations

​Supported Models

​Setup

​Get a FAL API Key

​Configure and Pick a Model

​OpenRouter: the full Image API catalog

​GPT-Image Quality

​Meta Model API: Muse Image

​FAL: GPT Image 2.5

​OpenAI API: GPT Image 2.5

​Custom OpenAI-compatible image endpoint

​Usage

​Image-to-Image / Editing

​Which backends support editing

​Aspect Ratios

​Upscaling

​Opt-in only

​The upscale parameter (per-call opt-in)

​How It Works Internally

​Debugging

​Platform Delivery

​Limitations

Supported Models

Setup

Get a FAL API Key

Configure and Pick a Model

OpenRouter: the full Image API catalog

GPT-Image Quality

Meta Model API: Muse Image

FAL: GPT Image 2.5

OpenAI API: GPT Image 2.5

Custom OpenAI-compatible image endpoint

Usage

Image-to-Image / Editing

Which backends support editing

Aspect Ratios

Upscaling

Opt-in only

The `upscale` parameter (per-call opt-in)

How It Works Internally

Debugging

Platform Delivery

Limitations