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Flux Dev LoRA

wavespeed-ai /

FLUX.1 [dev] endpoint with LoRA support for fast, high-quality image generation and simple personalization via pre-trained LoRA adapters. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

lora-support
इनपुट

निष्क्रिय

Super Realism, Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6

$0.015प्रति रन·~66 / $1

उदाहरणसभी देखें

Super Realism, Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6

Super Realism, Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6

A glowing, ethereal figure floats in the dark space, making it look almost like an angel. It has a calm and peaceful feeling, as if exuding a peaceful atmosphere, and its wings are composed of white light. This figure is completely composed of a soft and delicate beam of energy, which has an impressionist effect on the surrounding environment.

A glowing, ethereal figure floats in the dark space, making it look almost like an angel. It has a calm and peaceful feeling, as if exuding a peaceful atmosphere, and its wings are composed of white light. This figure is completely composed of a soft and delicate beam of energy, which has an impressionist effect on the surrounding environment.

Octopus vs. lam chess game, underwater setting, vibrant colors

Octopus vs. lam chess game, underwater setting, vibrant colors

The sun shines all over the courtyard, and the breeze blows gently, bringing bursts of flowers. Several girls were dressed in fine gauze and their skirts were blown by the wind, chasing and playing in the garden. Their laughter is as clear as a silver bell, sometimes jumping and sometimes spinning, like a group of dancing butterflies. One girl held a wreath and put it gently on her companion's head, while the other hid behind a tree and sneaked out her head, her eyes shining with naughty light. Their figures are looming in the flowers, sometimes holding hands and sometimes separating, as if they were integrated with the surrounding nature. The air is filled with youthful vitality and carefree happiness, as if this moment of time will never pass.The girl is a small dance in Douro Mainland.

The sun shines all over the courtyard, and the breeze blows gently, bringing bursts of flowers. Several girls were dressed in fine gauze and their skirts were blown by the wind, chasing and playing in the garden. Their laughter is as clear as a silver bell, sometimes jumping and sometimes spinning, like a group of dancing butterflies. One girl held a wreath and put it gently on her companion's head, while the other hid behind a tree and sneaked out her head, her eyes shining with naughty light. Their figures are looming in the flowers, sometimes holding hands and sometimes separating, as if they were integrated with the surrounding nature. The air is filled with youthful vitality and carefree happiness, as if this moment of time will never pass.The girl is a small dance in Douro Mainland.

a cat

a cat

a cat

a cat

yarn art style, Sexy blonde Christmas girl, wearing a revealing red Santa outfit with white fur trim, holding a gift box, festive lighting, soft glowing background,

yarn art style, Sexy blonde Christmas girl, wearing a revealing red Santa outfit with white fur trim, holding a gift box, festive lighting, soft glowing background,

an elderly man sitting by a window, warm sunlight casting soft shadows, ultra-realistic, high detail skin texture, weathered hands, cinematic photography, depth of field, 50mm lens, cozy room interior, storytelling mood

an elderly man sitting by a window, warm sunlight casting soft shadows, ultra-realistic, high detail skin texture, weathered hands, cinematic photography, depth of field, 50mm lens, cozy room interior, storytelling mood

a traveler standing on a hilltop at sunrise, light wind blowing through her hair, ultra-realistic lighting, soft golden glow, bright clear sky, detailed fabric, serene atmosphere, realistic landscape, cinematic photo realism

a traveler standing on a hilltop at sunrise, light wind blowing through her hair, ultra-realistic lighting, soft golden glow, bright clear sky, detailed fabric, serene atmosphere, realistic landscape, cinematic photo realism

a stylish woman sitting by a window, reading a book, sunlight streaming in, ultra-realistic, bright soft lighting, cozy modern interior, subtle skin texture, natural smile, casual elegance, lifestyle photography, crisp details, 85mm lens

a stylish woman sitting by a window, reading a book, sunlight streaming in, ultra-realistic, bright soft lighting, cozy modern interior, subtle skin texture, natural smile, casual elegance, lifestyle photography, crisp details, 85mm lens

a young woman with long flowing hair walking through a sunlit park, cherry blossoms falling, ultra-realistic, soft sunlight, clear blue sky, radiant smile, cinematic lighting, glowing skin, dreamy atmosphere, vibrant colors, 85mm lens

a young woman with long flowing hair walking through a sunlit park, cherry blossoms falling, ultra-realistic, soft sunlight, clear blue sky, radiant smile, cinematic lighting, glowing skin, dreamy atmosphere, vibrant colors, 85mm lens

संबंधित मॉडल

README

Flux-dev-lora

FLUX.1 [dev] is a 12B parameter rectified flow transformer for advanced text-to-image generation. It supports prompt-only generation as well as image inpainting and LoRA customization, making it a flexible tool for both research and creative workflows.

Why it looks great

  • High-quality output: Cutting-edge visual fidelity, second only to FLUX.1 [pro].
  • Prompt alignment: Strong competitive prompt following, rivaling closed-source alternatives.
  • Efficient training: Trained with guidance distillation for better speed-performance balance.
  • Flexible editing: Supports image + mask editing, LoRA fine-tuning, and custom strength control.
  • Open weights: Enables research, experimentation, and innovative creative pipelines.

Limits and Performance

  • Max resolution: up to 1536 × 1536 pixels

  • Optional inputs:

  • image (for img2img)

  • mask_image (for inpainting)

  • LoRA support: add multiple .safetensors with adjustable scale

  • Inference controls:

  • num_inference_steps (default ~28)

  • guidance_scale (default ~3.5)

  • strength (the strength of transform the reference image)

  • Output format: JPEG / PNG / WEBP

  • Seed: reproducibility (-1 = random)

Pricing

Just $0.015 per image !!

How to Use

  1. Write a prompt — detailed scene + style (lighting, realism, mood).
  2. (Optional) Upload an image to guide generation.
  3. (Optional) Add a mask image for inpainting.
  4. Adjust parameters:
  • Strength (the strength of transform the reference image).
  • LoRAs (add path/URL + scale).
  • Size (width & height, up to 1024×1024).
  • Inference steps and guidance scale.
  1. Set num_images (default 1).
  2. (Optional) Fix seed for reproducibility.
  3. Choose output format and run.

Pro tips

  • Use higher inference steps for more detail, lower for speed.
  • Adjust guidance scale to balance prompt strength vs. creativity (3–7 recommended).
  • Apply mask + strength for clean local edits (inpainting).
  • Blend multiple LoRAs for hybrid style outputs.
  • Use consistent seeds when testing parameter changes for controlled comparison.

Notes

  • The image URL must be valid and accessible; otherwise, the job may fail.
  • For mask_image, do not upload the original or unprocessed image directly — ensure the mask is correctly prepared.
  • LoRA files must be uploaded from trusted platforms and set to public access to be usable.
  • Parameters such as num_inference_steps (and others) directly affect runtime: the larger the value, the longer the generation will take.

Reference

नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Flux Dev Lora API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-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 output values from data.outputs. Examples for Flux Dev Lora 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",
    "strength": 0.8,
    "size": "1024*1024",
    "num_inference_steps": 28,
    "guidance_scale": 3.5,
    "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-dev-lora" \
  -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-dev-lora";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "strength": 0.8,
        "size": "1024*1024",
        "num_inference_steps": 28,
        "guidance_scale": 3.5,
        "num_images": 1,
        "seed": -1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "strength": 0.8,
    "size": "1024*1024",
    "num_inference_steps": 28,
    "guidance_scale": 3.5,
    "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-dev-lora", 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 Dev Lora API — Frequently asked questions

What is the Flux Dev Lora API?

Flux Dev Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. FLUX.1 [dev] endpoint with LoRA support for fast, high-quality image generation and simple personalization via pre-trained LoRA adapters. 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 Dev Lora 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-dev-lora.

How much does Flux Dev Lora cost per run?

Flux Dev Lora starts at $0.015 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 Dev Lora 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-dev-lora.

How long does Flux Dev Lora take to generate?

Median end-to-end generation time on WaveSpeedAI is around 18 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 Dev Lora 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 Dev LoRA | Custom LoRA Image API | WaveSpeedAI