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*as falfrom"@fal-ai/serverless-client";const result =await fal.subscribe("fal-ai/fast-lcm-diffusion",{input:{prompt:"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."},logs:true,onQueueUpdate:(update)=>{if(update.status==="IN_PROGRESS"){ update.logs.map((log)=> log.message).forEach(console.log);}},});
This model has a real-time mode via websockets, this is supported via the fal.realtime client.
import*as falfrom"@fal-ai/serverless-client";const connection = fal.realtime.connect("fal-ai/fast-lcm-diffusion",{onResult:(result)=>{console.log(result);},onError:(error)=>{console.error(error);}});connection.send({prompt:"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."});
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.
For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.
The client API provides a convenient way to submit requests to the model.
import*as falfrom"@fal-ai/serverless-client";const{ request_id }=await fal.queue.submit("fal-ai/fast-lcm-diffusion",{input:{prompt:"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."},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*as falfrom"@fal-ai/serverless-client";const status =await fal.queue.status("fal-ai/fast-lcm-diffusion",{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*as falfrom"@fal-ai/serverless-client";const result =await fal.queue.result("fal-ai/fast-lcm-diffusion",{requestId:"764cabcf-b745-4b3e-ae38-1200304cf45b"});
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.
The name of the model to use. Default value: "stabilityai/stable-diffusion-xl-base-1.0"
Possible enum values: stabilityai/stable-diffusion-xl-base-1.0, runwayml/stable-diffusion-v1-5
promptstring* required
The prompt to use for generating the image. Be as descriptive as possible for best results.
negative_promptstring
The 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: ""
The size of the generated image. Default value: square_hd
Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9
num_inference_stepsinteger
The number of inference steps to perform. Default value: 6
seedinteger
The same seed and the same prompt given to the same version of Stable Diffusion
will output the same image every time.
guidance_scalefloat
The 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: 1.5
sync_modeboolean
If set to true, the function will wait for the image to be generated and uploaded
before returning the response. This will increase the latency of the function but
it allows you to get the image directly in the response without going through the CDN. Default value: true
num_imagesinteger
The number of images to generate. Default value: 1
enable_safety_checkerboolean
If set to true, the safety checker will be enabled. Default value: true
safety_checker_versionSafetyCheckerVersionEnum
The version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model. Default value: "v1"
Possible enum values: v1, v2
expand_promptboolean
If set to true, the prompt will be expanded with additional prompts.
formatFormatEnum
The format of the generated image. Default value: "jpeg"
Possible enum values: jpeg, png
guidance_rescalefloat
The rescale factor for the CFG.
request_idstring
An id bound to a request, can be used with response to identify the request
itself. Default value: ""
{"model_name":"stabilityai/stable-diffusion-xl-base-1.0","prompt":"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k.","negative_prompt":"cartoon, illustration, animation. face. male, female","image_size":"square_hd","num_inference_steps":6,"guidance_scale":1.5,"sync_mode":true,"num_images":1,"enable_safety_checker":true,"safety_checker_version":"v1","format":"jpeg"}