
Ballpoint pen sketch drawing style

HDR surrealistic effect with intense colors
![Super fast endpoint for the FLUX.1 [dev] model with LoRA support, enabling rapid and high-quality image generation using pre-trained LoRA adaptations for personalization, specific styles, brand identities, and product-specific outputs.](https://refinery.fal.media/url/https%3A%2F%2Fv3b.fal.media%2Ffiles%2Fb%2F0a9f9a61%2FT4z71gOSeWv0wALDdi2-b_qVoN8eec.png/tr:w-1920,q-80/T4z71gOSeWv0wALDdi2-b_qVoN8eec.webp)
Super fast endpoint for the FLUX.1 [dev] model with LoRA support, enabling rapid and high-quality image generation using pre-trained LoRA adaptations for personalization, specific styles, brand identities, and product-specific outputs.

Default parameters with automated optimizations and quality improvements.
![Super fast endpoint for the FLUX.1 [dev] model with LoRA support, enabling rapid and high-quality image generation using pre-trained LoRA adaptations for personalization, specific styles, brand identities, and product-specific outputs.](https://refinery.fal.media/url/https%3A%2F%2Fstorage.googleapis.com%2Ffal_cdn%2Ffal%2FUpscale-5.jpeg/tr:w-1920,q-80/Upscale-5.webp)
Super fast endpoint for the FLUX.1 [dev] model with LoRA support, enabling rapid and high-quality image generation using pre-trained LoRA adaptations for personalization, specific styles, brand identities, and product-specific outputs.

Train Ideogram on your photos, your style, your subject, your look, from a small set of reference images to images that feel consistently yours

Fooocus extreme speed mode as a standalone app.

Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Stable Cascade: Image generation on a smaller & cheaper latent space.

Anime finetune of Würstchen V3.

Nano Banana 2.1 by Google generates images from text prompts, with output resolutions up to 4K, adjustable aspect ratios, and optional web search grounding. This API is for integration purposes only and cannot be used.

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

Qwen-Image-2.0 is a next-generation foundational unified generation-and-editing model

Use the faster speed of piflow to generate images with same quality to that of slower models.

Dreamina showcases superior picture effects, with significant improvements in picture aesthetics, precise and diverse styles, and rich details.

Google’s highest quality image generation model

Seedream 3.0 is a bilingual (Chinese and English) text-to-image model that excels at text-to-image generation.

Google’s highest quality image generation model

Google’s highest quality image generation model

DreamO is an image customization framework designed to support a wide range of tasks while facilitating seamless integration of multiple conditions.

F Lite is a 10B parameter diffusion model created by Fal and Freepik, trained exclusively on copyright-safe and SFW content.

F Lite is a 10B parameter diffusion model created by Fal and Freepik, trained exclusively on copyright-safe and SFW content. This is a high texture density variant of the model.

Imagen3 is a high-quality text-to-image model that generates realistic images from text prompts.

Imagen3 Fast is a high-quality text-to-image model that generates realistic images from text prompts.
fal is the best developer-friendly, one-stop shop for AI image generation models. Every image model on fal runs through the same SDK pattern, so once you’ve integrated one, switching between Nano Banana 2, FLUX.2 [dev], or GPT Image 2 is a one-line endpoint change.
The way to get started with image generation on fal is to install the client, set your FAL_KEY environment variable, and call any image endpoint.
bashnpm install --save @fal-ai/client
bashexport FAL_KEY="YOUR_API_KEY"
jsimport { fal } from "@fal-ai/client"; const result = await fal.subscribe("fal-ai/nano-banana-2", { input: { prompt: "A photorealistic Tokyo cafe at golden hour" } });
Every image model on fal shares this pattern. That means swapping Nano Banana 2 for FLUX.2 [dev] or GPT Image 2 is a one-line endpoint change, with the auth flow and queue behavior unchanged.
For image generation workflows where typography matters, fal hosts several models that treat text rendering as a primary capability.
Use these models when the image itself needs to contain readable names, slogans, UI labels, posters, packaging, or branded typography.
Photorealistic image generation rewards models that can handle lighting, materials, camera language, and fine visual detail.
The FLUX family covers most photoreal production work on fal, while partner models fill specific needs around prompt reasoning, brand consistency, and scene fidelity.
For high-volume image generation, Turbo and distilled models let you explore ideas quickly before spending more on final outputs.
A practical workflow is to generate low-resolution drafts on a Turbo model to scout the prompt space, then run only the keepers through a higher-fidelity model at full resolution.
Image generation on fal is priced per output. Some models charge per megapixel of image area, while others charge per image.
| Model | Price |
|---|---|
| FLUX.2 [dev] Turbo | ~$0.008 / image |
| Seedream V4.5 | $0.04 / image |
| Nano Banana 2 | $0.08 / image at 1K |
| Nano Banana Pro | $0.15 / image |
Nano Banana 2 also supports higher-resolution pricing, with 2K and 4K outputs priced at 1.5x and 2x the 1K rate.
As a worked example, a team generating 1,000 marketing images per month at 1K resolution costs roughly:
You only pay for what you generate, which lets you test prompts, compare models, and scale production without subscriptions or minimums.
bashnpm install --save @fal-ai/client
bashexport FAL_KEY="YOUR_API_KEY"
jsimport { fal } from "@fal-ai/client"; const result = await fal.subscribe("fal-ai/nano-banana-2", { input: { prompt: "A photorealistic Tokyo cafe at golden hour" } });
The same auth, billing, and queue logic carry across every image generation endpoint, so you can compare models side by side without rewriting integration code.
For large batches or longer generations, submit to the queue and rely on webhooks instead of blocking on the result.