Bytedance Seedream V4 Edit Sequential

Bytedance Seedream V4 Edit Sequential

Playground

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Seedream 4.0: 4K image generation and editing with character and object consistency and sequential multi-image outputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

What is it?

Seedream v4 Edit Sequential is image-to-image model for editing a source image and generating a sequence/group of consistent outputs in one pass. It’s built for high feature retention (identity, logos, layout) and cross-image style continuity—ideal for matched sets, story panels, or product variants.

What makes it stand out?

  • I2I + sequential in one pipeline – Edit a single source and produce N coherent images (series/panels) without switching models.
  • Five core strengths – Precise instruction editing, high feature retention, deep scene understanding, ultra-fast inference, ultra-high-res output.
  • Consistency controls – Strong identity/style preservation across all images in the set.
  • Rich edit ops – Add/remove elements, attribute/style change, structural tweaks (e.g., pose/face swap), texture/brush/frame edits.

Designed for

  • Commercial design — Posters, apparel, packaging, e-commerce sets; fast, brand-safe varianting.
  • Entertainment IP — Character look-dev, key art sequences with locked identity.
  • Fine art & Illustration — Multi-piece series with coherent palette and linework.
  • Architecture — Material/lighting variations across consistent viewpoints.
  • Brand content — Campaign sets and social carousels with fixed logo/palette.

How to use

  1. Inputs: Upload several source images.
  2. Prompt: Write the instruction and repeat the same N in text (e.g., “a series of N images / Panels 1–N”) to lock count & continuity.
  3. Count: Set max_images = N for the number of images.
  4. Control the size: the max size is 8192 * 8192.
  5. Generate → review → iterate (reuse or change seed for A/B).

Please Note: Declare the number of images twicemax_images = N and inside the prompt!

Price

  • $0.027 per image.
  • Total price = max_images * $0.027

Prompting guide

  • Edit instruction (per set) Replace [object A] with [object B]; keep [logo/identity/features]; preserve [lighting/style].
  • Sequential consistency (count locked) Generate a series of [N] edited images (Panels 1–[N]) from the source, maintaining the same [character/product/logo] identity, palette, and composition style.

Panel 1 — [edit/shot] Panel 2 — [edit/shot] … Panel N — [edit/shot]

  • Terminology Use precise, domain-native terms (photography, fashion, architecture) to match expectations.

Example (product variant set, N=4)

Set max_images = 4 and use:

Generate a series of 4 edited images (Panels 1–4) from the source photo, keeping the same shoe model and logo placement. Maintain identical angle, lighting, and background; change only the colorway per panel:

Panel 1 — classic white + black swoosh

Panel 2 — navy + gold accents

Panel 3 — matte red + white outsole

Panel 4 — forest green + gum sole

Ensure consistent proportions, stitching detail, and material texture across all 4 panels.

Note

Please set the max_image first, and then input how many images you want to generate in prompt! Such as:

  • max_image = 4.
  • Prompt: I want to generate 4 images… + (your prompt)
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"
  ],
  "max_images": 1
}
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-sequential" \
  -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.
max_imagesintegerNo11 ~ 15The maximum number of images that can be generated (up to 15). This value must align with the number of images specified in the prompt above.
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.
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.

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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