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Flux Kontext Dev LoRA Ultra Fast

wavespeed-ai /

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.

lora-support
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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.

$0.025cho mỗi lần chạy·~40 / $1

Ví dụXem tất cả

style of 80s cyberpunk, a car

style of 80s cyberpunk, a car

A young girl, slightly hair, portrait, blockprint style

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.

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 this image into a sketch

Turn a man's tie yellow.

Turn a man's tie yellow.

Turn the grass into a glacier.

Turn the grass into a glacier.

style of 80s cyberpunk, a girl

style of 80s cyberpunk, a girl

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

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

a man frstingln illustration

a man frstingln illustration

A young man wearing a hunters cap, portrait, blockprint style

A young man wearing a hunters cap, portrait, blockprint style

Mô hình liên quan

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.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

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. 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 output values from data.outputs. Examples for Flux Kontext Dev Lora Ultra Fast below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "num_inference_steps": 28,
    "guidance_scale": 2.5,
    "num_images": 1,
    "seed": -1,
    "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora-ultra-fast" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora-ultra-fast";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "num_inference_steps": 28,
        "guidance_scale": 2.5,
        "num_images": 1,
        "seed": -1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "num_inference_steps": 28,
    "guidance_scale": 2.5,
    "num_images": 1,
    "seed": -1,
    "output_format": "jpeg"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora-ultra-fast", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Flux 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 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-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?

Median end-to-end generation time on WaveSpeedAI is around 10 seconds per request, based on recent successful runs. Queue time varies 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.

Flux Kontext Dev LoRA Ultra Fast | Custom LoRA Image API | WaveSpeedAI