Ultra-fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing and brand/style adaptation using pre-trained LoRA. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.025per run·~40 / $1
ExamplesView all
style of 80s cyberpunk, a car
A young girl, slightly hair, portrait, blockprint style
A restored and colorized vintage black-and-white photograph, removing scratches, dust, and tears. The image features enhanced clarity, natural skin tones, and realistic colors while preserving the original nostalgic atmosphere. The photo looks vivid and fresh, with balanced lighting and rich detail, as if carefully brought back to life from the past.
Turn this image into a sketch
Turn a man's tie yellow.
Turn the grass into a glacier.
style of 80s cyberpunk, a girl
v3ct0r style, simple flat vector art, isolated on white bg, girl
a man frstingln illustration
A young man wearing a hunters cap, portrait, blockprint style
Related Models
README
FLUX Kontext Dev LoRA Ultra Fast — wavespeed-ai/flux-kontext-dev-lora-ultra-fast
FLUX Kontext Dev LoRA Ultra Fast is a low-latency image-to-image editing model that supports LoRA adapters directly in the request. Provide a source image plus a natural-language edit instruction, and optionally attach up to 3 LoRAs to steer style, identity consistency, or domain aesthetics—optimized for rapid iteration and production workflows.
Key capabilities
- Ultra-fast instruction-based image editing from a single input image
- LoRA-enabled inference: apply up to 3 LoRAs via input parameters
- Strong preservation when you explicitly state what must remain unchanged
- Great for iterative editing: quick refinements across multiple passes with minimal drift
Typical use cases
- Fast retouching + consistent styling using a “house look” LoRA
- Batch product edits (color variants, background swaps) with brand LoRAs
- Text edits on packaging/signage while keeping typography/perspective consistent
- Rapid A/B testing by switching LoRAs instead of rewriting prompts
Pricing
$0.025 per image.
Cost per run = num_images × $0.025 Example: num_images = 4 → $0.10
Inputs and outputs
Input:
- One source image (upload or public URL)
- One edit instruction (prompt)
- Optional: up to 3 LoRA items
Output:
- One or more edited images (controlled by num_images)
Parameters
Core:
- prompt: Edit instruction describing what to change and what to preserve
- image: Source image
- width / height: Output resolution
- num_inference_steps: More steps can improve fidelity but increases latency
- guidance_scale: Higher values follow the prompt more strongly; too high may over-edit
- num_images: Number of variations generated per run
- seed: Fixed value for reproducibility; -1 for random
- output_format: jpeg or png
- enable_base64_output: Return BASE64 instead of a URL (API only)
- enable_sync_mode: Wait for generation and return results directly (API only)
LoRA (up to 3 items):
-
loras: A list of LoRA entries (max 3)
-
path: owner/model-name or a direct.safetensors URL
-
scale: LoRA strength (start around 0.6–1.0 and adjust)
Prompting guide
Use a clear “preserve + edit + constraints” structure and let LoRAs control the look:
Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Apply LoRA style consistently without altering identity.
Example prompts
- Keep the person’s face, hairstyle, and pose unchanged. Replace the background with a clean studio backdrop. Match lighting direction and shadow softness.
- Keep the product shape and label layout unchanged. Replace only the label text with “WaveSpeedAI”, preserving font style, size, and perspective.
- Remove the background clutter and keep the main subject sharp. Preserve natural skin texture while reducing shine.
Best practices
- Start with one LoRA first; add a second/third only when needed.
- If the output is over-stylized, reduce LoRA scale and/or guidance_scale.
- For consistent batches, reuse the same LoRA set + scales and fix seed for comparisons.
- Match width/height to the original aspect ratio to avoid distortions.
Flux Kontext Dev Lora Ultra Fast API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora-ultra-fast with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Flux Kontext Dev Lora Ultra Fast below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora-ultra-fast" \
-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
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].Node.js example
// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/flux-kontext-dev-lora-ultra-fast", {
"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
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/flux-kontext-dev-lora-ultra-fast",
{
"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
}
)
print(output["outputs"][0]) # → URL of the generated outputFlux Kontext Dev Lora Ultra Fast API — Frequently asked questions
What is the Flux Kontext Dev Lora Ultra Fast API?
Flux Kontext Dev Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Ultra-fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing and brand/style adaptation using pre-trained LoRA. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
How do I call the Flux Kontext Dev Lora Ultra Fast API?
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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-kontext-dev-lora-ultra-fast.
How much does Flux Kontext Dev Lora Ultra Fast cost per run?
Flux Kontext Dev Lora Ultra Fast starts at $0.025 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.
What inputs does Flux Kontext Dev Lora Ultra Fast accept?
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-ultra-fast.
How long does Flux Kontext Dev Lora Ultra Fast take to generate?
Average end-to-end generation time on WaveSpeedAI is around 16 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Can I use Flux Kontext Dev Lora Ultra Fast outputs commercially?
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.