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The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/stable-diffusion-v35-large", {
input: {
prompt: "A dreamlike Japanese garden in perpetual twilight, bathed in bioluminescent cherry blossoms that emit a soft pink-purple glow. Floating paper lanterns drift lazily through the scene, their warm light creating dancing reflections in a mirror-like koi pond. Ethereal mist weaves between ancient stone pathways lined with glowing mushrooms in pastel blues and purples. A traditional wooden bridge arches gracefully over the water, dusted with fallen petals that sparkle like stardust. The scene is captured through a cinematic lens with perfect bokeh, creating an otherworldly atmosphere. In the background, a crescent moon hangs impossibly large in the sky, surrounded by a sea of stars and auroral wisps in teal and violet. Crystal formations emerge from the ground, refracting the ambient light into rainbow prisms. The entire composition follows the golden ratio, with moody film-like color grading reminiscent of Studio Ghibli, enhanced by volumetric god rays filtering through the luminous foliage. 8K resolution, masterful photography, hyperdetailed, magical realism."
},
logs: true,
onQueueUpdate: (update) => {
if (update.status === "IN_PROGRESS") {
update.logs.map((log) => log.message).forEach(console.log);
}
},
});
console.log(result.data);
console.log(result.requestId);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.
The client API provides a convenient way to submit requests to the model.
import { fal } from "@fal-ai/client";
const { request_id } = await fal.queue.submit("fal-ai/stable-diffusion-v35-large", {
input: {
prompt: "A dreamlike Japanese garden in perpetual twilight, bathed in bioluminescent cherry blossoms that emit a soft pink-purple glow. Floating paper lanterns drift lazily through the scene, their warm light creating dancing reflections in a mirror-like koi pond. Ethereal mist weaves between ancient stone pathways lined with glowing mushrooms in pastel blues and purples. A traditional wooden bridge arches gracefully over the water, dusted with fallen petals that sparkle like stardust. The scene is captured through a cinematic lens with perfect bokeh, creating an otherworldly atmosphere. In the background, a crescent moon hangs impossibly large in the sky, surrounded by a sea of stars and auroral wisps in teal and violet. Crystal formations emerge from the ground, refracting the ambient light into rainbow prisms. The entire composition follows the golden ratio, with moody film-like color grading reminiscent of Studio Ghibli, enhanced by volumetric god rays filtering through the luminous foliage. 8K resolution, masterful photography, hyperdetailed, magical realism."
},
webhookUrl: "https://optional.webhook.url/for/results",
});You can fetch the status of a request to check if it is completed or still in progress.
import { fal } from "@fal-ai/client";
const status = await fal.queue.status("fal-ai/stable-diffusion-v35-large", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.
import { fal } from "@fal-ai/client";
const result = await fal.queue.result("fal-ai/stable-diffusion-v35-large", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.
You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.
You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.
We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.
import { fal } from "@fal-ai/client";
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).
Read more about file handling in our file upload guide.
prompt string* requiredThe prompt to generate an image from.
negative_prompt stringThe negative prompt to use. Use it to address details that you don't want
in the image. This could be colors, objects, scenery and even the small details
(e.g. moustache, blurry, low resolution). Default value: ""
num_inference_steps integerThe number of inference steps to perform. Default value: 28
seed integerThe same seed and the same prompt given to the same version of the model will output the same image every time.
guidance_scale floatThe CFG (Classifier Free Guidance) scale is a measure of how close you want
the model to stick to your prompt when looking for a related image to show you. Default value: 3.5
sync_mode booleanIf True, the media will be returned as a data URI and the output data won't be available in the request history.
num_images integerThe number of images to generate. Default value: 1
enable_safety_checker booleanIf set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked, and images flagged as unsafe are returned as black images. Default value: true
output_format OutputFormatEnumThe format of the generated image. Default value: "jpeg"
Possible enum values: jpeg, png
ControlNet for inference.
The size of the generated image. Defaults to landscape_4_3 if no controlnet has been passed, otherwise defaults to the size of the controlnet conditioning image.
Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9
Note: For custom image sizes, you can pass the width and height as an object:
"image_size": {
"width": 1280,
"height": 720
}The LoRAs to use for the image generation. You can use any number of LoRAs and they will be merged together to generate the final image.
IP-Adapter to use during inference.
{
"prompt": "A dreamlike Japanese garden in perpetual twilight, bathed in bioluminescent cherry blossoms that emit a soft pink-purple glow. Floating paper lanterns drift lazily through the scene, their warm light creating dancing reflections in a mirror-like koi pond. Ethereal mist weaves between ancient stone pathways lined with glowing mushrooms in pastel blues and purples. A traditional wooden bridge arches gracefully over the water, dusted with fallen petals that sparkle like stardust. The scene is captured through a cinematic lens with perfect bokeh, creating an otherworldly atmosphere. In the background, a crescent moon hangs impossibly large in the sky, surrounded by a sea of stars and auroral wisps in teal and violet. Crystal formations emerge from the ground, refracting the ambient light into rainbow prisms. The entire composition follows the golden ratio, with moody film-like color grading reminiscent of Studio Ghibli, enhanced by volumetric god rays filtering through the luminous foliage. 8K resolution, masterful photography, hyperdetailed, magical realism.",
"negative_prompt": "",
"num_inference_steps": 28,
"guidance_scale": 3.5,
"num_images": 1,
"enable_safety_checker": true,
"output_format": "jpeg"
}The generated image files info.
seed integer* requiredSeed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.
Whether the generated images contain NSFW concepts.
prompt string* requiredThe prompt used for generating the image.
{
"images": [
{
"url": "",
"content_type": "image/jpeg"
}
],
"prompt": ""
}width integerThe width of the generated image. Default value: 512
height integerThe height of the generated image. Default value: 512
path string* requiredHugging Face path to the IP-Adapter
subfolder stringSubfolder in which the ip_adapter weights exist
weight_name stringName of the safetensors file containing the ip-adapter weights
image_encoder_path string* requiredPath to the Image Encoder for the IP-Adapter, for example 'openai/clip-vit-large-patch14'
image_encoder_subfolder stringSubfolder in which the image encoder weights exist.
image_encoder_weight_name stringName of the image encoder.
image_url string* requiredURL of Image for IP-Adapter conditioning.
mask_image_url stringURL of the mask for the control image.
mask_threshold floatThreshold for mask. Default value: 0.5
scale float* requiredScale for ip adapter.
path string* requiredURL or the path to the LoRA weights.
scale floatThe scale of the LoRA weight. This is used to scale the LoRA weight
before merging it with the base model. Default value: 1
path string* requiredURL or the path to the control net weights.
control_image_url string* requiredURL of the image to be used as the control image.
conditioning_scale floatThe scale of the control net weight. This is used to scale the control net weight
before merging it with the base model. Default value: 1
start_percentage floatThe percentage of the image to start applying the controlnet in terms of the total timesteps.
end_percentage floatThe percentage of the image to end applying the controlnet in terms of the total timesteps. Default value: 1
url string* requiredwidth integer* requiredheight integer* requiredcontent_type stringDefault value: "image/jpeg"