Fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.03per run·~33 / $1

a dog frstingln illustration

Replace 'Feel POWER' with 'WaveSpeedAI'

Transform the image into detailed anime style. Keep the original composition and layout, but render all characters with smooth outlines, large expressive eyes, vibrant hair colors, and soft cel-shading. Background should resemble high-quality anime scenes — painterly skies, stylized lighting, and subtle gradients. Make it feel like a frame from a cinematic anime.

Turn it into a lego style

Turning clothes into bikinis

change the car color to pink

v3ct0r style, simple flat vector art, isolated on white bg, Mona Lisa

v3ct0r style, simple flat vector art, isolated on white bg, a girl

Mona Lisa, blockprint style

A young man with sunglasses, portrait, blockprint style
FLUX Kontext Dev LoRA is an instruction-based image-to-image editing model with built-in LoRA support. Provide a source image plus a natural-language edit request, and optionally attach up to 3 LoRAs to steer style, subject consistency, or domain-specific aesthetics during the edit.
$0.03 per image.
Cost per run = num_images × $0.03 Example: num_images = 4 → $0.12
Input:
Output:
Core:
LoRA (up to 3 items):
loras: A list of LoRA entries (max 3)
path: Either owner/model-name or a direct.safetensors URL from the Internet
scale: LoRA strength (typically start around 0.6–1.0 and adjust)
Use “preserve + edit + constraints” and let LoRAs drive the look:
Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Apply the LoRA style consistently without changing identity.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read URLs from data.outputs. Examples for Flux Kontext Dev Lora below.
# Submit the prediction
curl --fail-with-body --connect-timeout 10 --max-time 60 \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"num_inference_steps": 28,
"guidance_scale": 2.5,
"num_images": 1,
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
}'
# Wait at least 2 seconds, then poll. Safe GET requests may be retried.
curl --fail-with-body --connect-timeout 10 --max-time 30 \
--retry 4 --retry-all-errors --retry-delay 1 \
-X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# Start at 2 seconds and increase the interval for long-running tasks.
# Stop on completed, failed, cancelled, or timeout.// npm install wavespeed
const { Client } = require('wavespeed');
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
const client = new Client(apiKey, {
maxConnectionRetries: 5,
retryInterval: 1.0,
});
try {
const result = await client.run("wavespeed-ai/flux-kontext-dev-lora", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"num_inference_steps": 28,
"guidance_scale": 2.5,
"num_images": 1,
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
}, {
timeout: 3600,
pollInterval: 2.0,
});
console.log(result.outputs);
} catch (error) {
console.error('Generation failed:', error);
process.exitCode = 1;
}# pip install wavespeed
import os
from wavespeed import Client
client = Client(
api_key=os.environ["WAVESPEED_API_KEY"],
max_connection_retries=5,
retry_interval=1.0,
)
try:
output = client.run(
"wavespeed-ai/flux-kontext-dev-lora",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"num_inference_steps": 28,
"guidance_scale": 2.5,
"num_images": 1,
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
},
timeout=3600.0,
poll_interval=2.0,
)
print(output["outputs"])
except Exception as error:
raise SystemExit(f"Generation failed: {error}") from errorFlux Kontext Dev Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-kontext-dev-lora.
Flux Kontext Dev Lora starts at $0.030 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `image`, `size`, `seed`, `guidance_scale`, `num_inference_steps`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-kontext-dev-lora.
Average end-to-end generation time on WaveSpeedAI is around 18 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.