Flux Kontext Dev Multi | Fast Image Editing API

FLUX Kontext Dev Multi — wavespeed-ai/flux-kontext-dev/multi

FLUX.1 Kontext Dev Multi extends instruction-based image editing to a multi-image workflow. You can provide up to 4 reference images alongside a text instruction, enabling richer context, stronger consistency, and more controllable edits across subjects, styles, and scenes—especially useful when one image alone is not enough to describe what you want.

Key capabilities

Multi-image contextual editing with up to 4 reference images
Better subject/style consistency by grounding edits in multiple references
Supports both local edits (specific changes) and global edits (overall look)
Ideal for iterative workflows: refine results step-by-step while keeping identity and style stable

Typical use cases

Multi-reference character consistency (face/hair/outfit cues from multiple photos)
Product edits with reference packs (angle, material, branding consistency)
Style guidance from multiple exemplars (illustration style + lighting reference + texture reference)
Scene recomposition while preserving subject identity
Branding/text edits that must match reference typography and layout

Pricing

$0.03 per generation.

If you generate multiple outputs in one run, total cost = num_images × $0.03 Example: num_images = 4 → $0.12

Inputs and outputs

Input:

Up to 4 reference images (upload or public URLs)
One edit instruction (prompt)

Output:

One or more edited images (controlled by num_images)

Parameters

prompt: Edit instruction describing what to change and what to keep
images: Up to 4 reference images
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)

Prompting guide

For multi-reference runs, be explicit about how each reference should be used:

Template: Use reference 1 for [identity]. Use reference 2 for [outfit/material]. Use reference 3 for [style/lighting]. Use reference 4 for [background/scene]. Keep [must-preserve]. Change [edit request]. Match [lighting/shadows/perspective].

Example prompts

Use reference 1 for face identity and reference 2 for hairstyle. Keep the pose from the base image. Replace the background with a modern office and match lighting direction.
Use reference 1 for the product shape and reference 2 for label design. Replace the label text with “WaveSpeedAI”, keeping typography, perspective, and print texture consistent.
Use reference 3 as the style guide (soft illustration look) and reference 4 for lighting mood (golden hour). Preserve the subject identity from reference 1.

Best practices

Provide clean references: sharp subjects, consistent lighting, minimal occlusion.
Assign roles to references (identity vs. style vs. scene) to avoid conflicting signals.
Make one change per run, then iterate for tighter control.
Fix seed when you need stable comparisons across prompt variants.

Flux Kontext Dev Multi API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev/multi 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 Multi below.

HTTP example

# 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/multi" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "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",
    "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.

Node.js example

// 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/multi", {
        "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",
        "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;
}

Python example

# 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/multi",
        {
    "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",
    "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 error

Flux Kontext Dev Multi API — Frequently asked questions

What is the Flux Kontext Dev Multi API?

Flux Kontext Dev Multi is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. Experimental FLUX.1 Kontext [dev] with multi-image handling for contextual multi-input inference and image workflows. 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 Multi 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-multi.

How much does Flux Kontext Dev Multi cost per run?

Flux Kontext Dev Multi 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.

What inputs does Flux Kontext Dev Multi accept?

Key inputs: `prompt`, `images`, `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-multi.

How long does Flux Kontext Dev Multi take to generate?

Average end-to-end generation time on WaveSpeedAI is around 17 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 Multi 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.

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