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Endpoint
https://fal.run/fal-ai/flux-1/dev
Model ID
fal-ai/flux-1/dev
Category
Text-to-image · inference
Tags
FLUX.1 [dev]
FLUX.1 [dev] is a 12B-parameter flow transformer that turns text into high-quality images for both personal and commercial projects.
flow transformer
commercial use
fast
Status
Idle
Response
Waiting for first request...
Generated Images
Input Schema
prompt (string, required)
The text description that drives the render.
image_size (enum | object)
Tokens like landscape_4_3 or a custom object: { width, height }.
num_inference_steps (integer)
How many diffusion steps to run. Default 28.
seed (integer)
Repeat inputs to recreate the same image.
guidance_scale (float)
CFG strength: 1-20, default 3.5.
sync_mode (boolean)
Return data URI inline (no request history) when true.
num_images (integer)
Number of outputs per request. Range 1-4.
enable_safety_checker (boolean)
Default true. Disable at your own risk.
output_format (enum)
jpeg (default) or png.
acceleration (enum)
Select none, regular, or high to change throughput.
Output Schema
images
Array of { url, content_type } objects.
timings
Queue + generation duration metrics.
seed
The seed echo for reproducibility.
has_nsfw_concepts
Boolean array reporting NSFW detections.
prompt
The prompt that generated the output.
Usage Examples
cURL
curl --request POST \
--url https://fal.run/fal-ai/flux-1/dev \
--header "Authorization: Key $FAL_KEY" \
--header "Content-Type: application/json" \
--data '{
"prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture."
}'
Python
import fal_client
def on_queue_update(update):
if isinstance(update, fal_client.InProgress):
for log in update.logs:
print(log["message"])
result = fal_client.subscribe(
"fal-ai/flux-1/dev",
arguments={
"prompt": "Extreme close-up of a single tiger eye..."
},
with_logs=True,
on_queue_update=on_queue_update,
)
print(result)
JavaScript
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/flux-1/dev", {
input: {
prompt: "Extreme close-up of a single tiger eye..."
},
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);