Flux Kontext Dev LoRA

Flux Kontext Dev LoRA

Playground

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.

Features

FLUX Kontext Dev LoRA — wavespeed-ai/flux-kontext-dev-lora

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.

Key capabilities

• Image-to-image editing from a source image + text instruction
• LoRA-enabled inference: apply up to 3 LoRAs via input parameters
• Works for both local edits (specific changes) and global transforms (overall look)
• Ideal for batch consistency: reuse the same LoRA set for a stable visual identity

Pricing

$0.03 per image.

Cost per run = num_images × $0.03 Example: num_images = 4 → $0.12

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

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)

Prompting guide

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.

Example prompts

• Keep the person’s face, hairstyle, and pose unchanged. Replace the background with a clean studio gradient. Match lighting and shadows.
• Keep the product shape and label layout unchanged. Replace only the label text with “WaveSpeedAI”. Preserve typography perspective and print texture.
• Remove small blemishes and reduce glare on the forehead while keeping natural skin texture and pores.

Best practices

• Use fewer LoRAs when possible; add a second/third only if you need combined effects.
• If results drift or over-style, reduce LoRA scale or guidance_scale and strengthen the preserve clause.
• For consistent batches, keep the same LoRA set and scales, and fix seed when comparing prompt variants.

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "num_inference_steps": 28,
    "guidance_scale": 2.5,
    "num_images": 1,
    "seed": -1,
    "loras": [],
    "output_format": "jpeg",
    "enable_base64_output": false,
    "enable_sync_mode": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"