Bytedance Seedream V4 Edit

Bytedance Seedream V4 Edit

Playground

Try it on WaveSpeedAI!

Seedream V4 Edit is state-of-the-art image editing model that outperforms Nano Banana in fidelity and edit quality. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Seedream 4.0/Edit is a specialized image-to-image model for accurate edits to existing images - swap outfits and accessories, adjust hair or makeup, recolor or re-materialize products, and replace interior finishes like floors, walls, or furniture while maintaining subject identity, lighting, and overall composition.

Highlights

  • High fidelity on people & products: Reliable skin tones, fabric/material details, logos, and fine edges.
  • Production-friendly consistency: Generate multiple variants quickly with locked camera/look and brand color grading.
  • Structured prompts that scale: Works best when the goal is clear up front (action + object + target feature + constraints).

Use cases

  • Portrait & influencer workflows: Outfit/makeup/hair changes, branded KV series, quick campaign variants.
  • E-commerce & product teams: Colorways, material/texture swaps, packaging updates.
  • Interior/archviz: Wall/floor finish replacements, upholstery changes, lighting-aware set dressing.
  • Marketing & growth: Fast A/B testing with consistent brand color and camera setup.

Price

Only $0.027 per image!!!

How to use

  1. Prepare source: Upload the base images (Up to 10 images).
  2. Set size: The maximum size of the image is 8192 * 8192.
  3. Write a clear prompt: Write clearly in the prompt about object, feature, and constraints.

Prompt patterns (copy-ready)

Use: change action + change object + target feature + constraints (keep/avoid)

Portrait (KV series)

portrait KV series, {STYLE} style, consistent color grading {BRAND_COLOR}, fixed camera look (85mm shallow depth), interchangeable persona: {PERSONA}, reserved lower-third text “{NAME} — {ROLE}”

Change Clothes / Jewellery / Makeup

Outfit swap for portrait, replace clothing with {OUTFIT_DESC}; keep pose and composition; accessories {JEWELRY_DESC}; makeup/hair {MAKEUP_HAIR}; preserve skin tone and lighting; clean edges, no artifacts

Background Replacement

Background replacement for subject, keep subject edges; new environment: {SCENE_DESC}; match light direction and color temperature; soft contact shadows; no haloing

Interior / Outdoor Replacement

Interior finish swap, update wall {WALL_MATERIAL}, floor {FLOOR_MATERIAL}, furniture upholstery {FABRIC}; layout and lighting unchanged; realistic PBR textures


Aspect RatioExact (W×H)Exact PixelsRounded (W×H, ÷64)Rounded Pixels
1:11448 × 14482,096,7041408 × 14081,982,464
3:21773 × 11822,095,6861728 × 11521,990,656
4:31672 × 12542,096,6881664 × 12162,023,424
16:91936 × 10892,108,3041920 × 10882,088,960
21:92212 × 9482,096,9762176 × 9602,088,960
1:11024 × 10241,048,5761024 × 10241,048,576
3:21254 × 8361,048,3441216 × 8321,011,712
4:31182 × 8871,048,4341152 × 8961,032,192
16:91365 × 7681,048,3201344 × 7681,032,192
21:91564 × 6701,047,8801536 × 640983,040
1:1323 × 323104,329320 × 320102,400
3:2397 × 264104,808384 × 25698,304
4:3374 × 280104,720448 × 320143,360
16:9432 × 243104,976448 × 256114,688
21:9495 × 212104,940576 × 256147,456

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "images": [
    "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
  ]
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bytedance/seedream-v4/edit" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(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' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  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

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
imagesarray<string>Yes-1 ~ 10 itemsThe images to edit. A maximum of 10 reference images can be uploaded.
enable_sync_modebooleanNofalse-If set to `true`, the request attempts to wait for the generated result and return outputs in the same response. If the result is not ready within the sync wait window, the API can return a timeout body while the task continues processing. This option is only available via the API and is supported only by some models.
enable_base64_outputbooleanNofalse-If set to `true`, the prediction's `output` strings are returned as **naked base64** (no `data:<mime>;base64,` prefix). When `false` (default), outputs are returned as URLs pointing to our CDN.

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.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (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

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
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to poll for the prediction result
data.statusstringStatus: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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