Flux Kontext Dev

Flux Kontext Dev

Playground

Try it on WavespeedAI!

FLUX.1 Kontext Dev is an open-weight, open-code image-to-image model that edits images from text prompts for precise, text-guided retouching and style transfer. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

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

FLUX.1 Kontext Dev is an open-weight, open-code image-to-image model built for instruction-based editing. Provide a source image plus a natural-language edit request, and the model rewrites the image while preserving the original context when asked—making it suitable for targeted retouching, object changes, background swaps, text edits, and controlled style transforms.

Key capabilities

  • Instruction-based image editing from a single input image
  • Strong subject and scene preservation when you explicitly request it
  • Local and global edits: change specific regions or the whole image
  • Iterative editing workflow: apply multiple edits step-by-step with minimal drift

Typical use cases

  • Retouching: lighting, exposure, cleanup, blemish removal
  • Object edits: add/remove/replace items, change colors/materials
  • Background replacement: swap environments while keeping the subject consistent
  • Text edits: add or replace words on signs, posters, packaging
  • Style transforms: convert to clay, illustration, cinematic, etc., while preserving composition

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)

Output:

  • One or more edited images (controlled by num_images)

Parameters

  • prompt: Edit instruction describing what to change and what to keep
  • image: Source image
  • width / height: Output resolution
  • num_inference_steps: More steps usually improves quality 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

Write prompts like an editor’s brief:

  1. Preserve clause: what must stay the same
  2. Edit clause: what must change
  3. Constraints: realism level, lighting, placement, typography, materials
  4. Consistency: match shadows/highlights to the new scene

Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Match [lighting/shadows/style consistency].

Example prompts

  • Keep the person’s face, pose, and clothing unchanged. Change the background to a foggy gothic castle. Match lighting and shadows to the new environment.
  • Change the car color to red. Preserve reflections and keep the rest of the scene unchanged.
  • Add the text “COOL” on the sign in the same perspective, with realistic shadows, and do not alter anything else.
  • Turn the image into a clay style with handcrafted texture and soft studio lighting, while keeping the composition and subject identity.
  • Remove the background crowd and keep the main subject sharp and unchanged.

Best practices

  • Start simple, then iterate: do one change per run for maximum control.
  • If the edit is too aggressive, lower guidance_scale and strengthen the preserve clause.
  • For A/B comparisons, keep seed fixed and change only one parameter at a time.
  • Use aspect-matched width/height to avoid unintended stretching.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev" \
--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,
    "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}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringNo-The positive prompt for the generation.
imagestringNo-The image to generate an image from.
sizestringNo-256 ~ 1536 per dimensionThe size of the generated media in pixels (width*height).
num_inference_stepsintegerNo281 ~ 50The number of inference steps to perform.
guidance_scalenumberNo2.51.0 ~ 20.0The guidance scale to use for the generation.
num_imagesintegerNo11 ~ 4The number of images to generate.
seedintegerNo-1-1 ~ 2147483647The random seed to use for the generation. -1 means a random seed will be used.
output_formatstringNojpegjpeg, png, webpThe format of the output image.
enable_base64_outputbooleanNofalse-If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
enable_sync_modebooleanNofalse-If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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