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When you call a model on fal, everything you pass in the arguments dict (Python) or input object (JavaScript) goes into the request body as the model’s input. Each model defines its own schema, and you can always find the exact schema on the model’s API page (e.g., Nano Banana 2 API). Many image generation models on fal share a set of common arguments that behave consistently across models. These are not platform-level controls (those are platform headers passed via the headers dict). They are model inputs that affect what the model generates and how. This page documents the most common ones for image generation so you don’t have to look them up on every model’s API page individually.

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seed

Random seed for reproducible outputs. The same seed with the same inputs produces the same result. Useful for debugging, A/B testing, or letting users “lock” a generation they like and iterate on the prompt.
TypeInteger or null
DefaultRandom
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "seed": 42
})

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num_images

Number of images to generate per request. Higher values produce more results in a single call but increase processing time and cost proportionally.
TypeInteger
Default1
RangeTypically 1-4
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "num_images": 4
})

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image_size

Output image dimensions, used by models built with fal’s toolkit (such as FLUX models). You can pass a preset string for common aspect ratios, or a custom {width, height} object for precise control.
PresetDimensions
square_hd1024x1024
square512x512
portrait_4_3768x1024
portrait_16_9576x1024
landscape_4_31024x768
landscape_16_91024x576
result = fal_client.subscribe("fal-ai/flux/schnell", arguments={
    "prompt": "a sunset",
    "image_size": "landscape_16_9"
})

result = fal_client.subscribe("fal-ai/flux/schnell", arguments={
    "prompt": "a sunset",
    "image_size": {"width": 1280, "height": 720}
})

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aspect_ratio

Some models (such as Nano Banana 2) use aspect_ratio instead of image_size. This accepts a ratio string and the model determines the output resolution internally.
TypeString
DefaultVaries by model
Common values"1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "21:9", "4:5", "5:4"
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "aspect_ratio": "16:9"
})
Check the model’s API page to see whether it uses image_size or aspect_ratio. Passing the wrong one will have no effect.

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enable_safety_checker

Enable or disable the content safety filter on generated outputs. When enabled, fal runs an ML-based classifier on each generated image to detect NSFW content (nudity and sexual content).
TypeBoolean
Defaulttrue
Also accepted asenable_safety_checks
Each generated image is analyzed by a classifier after generation. Images flagged as unsafe are replaced with a black image of the same dimensions. The response includes a has_nsfw_concepts array indicating which images were flagged, so you can detect and handle filtered outputs in your code. 
{
  "images": [
    {"url": "https://v3.fal.media/files/...", "width": 1024, "height": 1024},
    {"url": "https://v3.fal.media/files/...", "width": 1024, "height": 1024}
  ],
  "has_nsfw_concepts": [false, true]
}
In this example, the second image was flagged and replaced with a black image. 
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "enable_safety_checker": True
})

if any(result.get("has_nsfw_concepts", [])):
    print("Some images were filtered by the safety checker")
To disable the safety checker: 
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "enable_safety_checker": False
})
Not all models support disabling the safety checker. Check the model’s API page (e.g., Nano Banana 2 API) for the available parameters.

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enable_prompt_expansion

When enabled, fal runs your prompt through an LLM before passing it to the model. The LLM rewrites short or vague prompts into richer descriptions with detail on composition, textures, colors, lighting, and mood. For example, “a sunset” might become a detailed scene description with specific lighting conditions, color palettes, and framing. This typically improves output quality for brief prompts but may alter the result if your prompt is already detailed and precise.
TypeBoolean
DefaultVaries by model
Also accepted asexpand_prompt (some models use this name instead)
Additional costSmall per-request charge for the LLM inference step
The expansion adds an LLM inference step before generation, so expect slightly higher latency per request. The expanded prompt is not returned in the response, so if you want to see what the model actually received, run the model with expansion disabled and handle prompt enhancement in your own pipeline. 
result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a sunset",
    "expand_prompt": True
})
Check the model’s API page for the exact parameter name. Some models use enable_prompt_expansion, others use expand_prompt.

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output_format

Format of the generated image file. Choose based on your use case: jpeg for smaller file sizes with lossy compression, png for lossless quality with transparency support, or webp for a modern format that balances both.
TypeString
DefaultVaries by model (typically "jpeg" or "png")
Values"jpeg", "jpg", "png", "webp", "gif"
result = fal_client.subscribe("fal-ai/flux/schnell", arguments={
    "prompt": "a sunset",
    "output_format": "png"
})