Inference Logoinference shell

Seedance 2.0

Professional multimodal video generation from text, images, video, and audio references using ByteDance's Seedance 2.0 model via BytePlus ARK API. Supports up to 4K (10-bit color), text-to-video, image-to-video, and multimodal reference-to-video with synchronized audio.

text to videoimage to videoreference to videonative audio
run in browser →run via APIrun with your agent
maker
bytedance
category
video
input → output
text → video, image → video, reference → video
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-01
price
$2.40-$7.70/M tokens by resolution and visual input

api reference

about

professional multimodal video generation from text, images, video, and audio references using bytedance's seedance 2.0 model via byteplus ark api. supports up to 4k (10-bit color), text-to-video, image-to-video, and multimodal reference-to-video with synchronized audio.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "bytedance/seedance-2-0",9    "input": {10      "prompt": "@Image1 is the character reference. @Video1 provides the motion. A cat stretches lazily on a sunlit windowsill."11    },12    "wait": true13  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "bytedance/seedance-2-0",8    "input": {9      "prompt": "@Image1 is the character reference. @Video1 provides the motion. A cat stretches lazily on a sunlit windowsill."10    },11    "stream": true12  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "bytedance/seedance-2-0",7    "input": {8      "prompt": "@Image1 is the character reference. @Video1 provides the motion. A cat stretches lazily on a sunlit windowsill."9    }10  }' | jq -r '.data.id')1112# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)13curl -s https://api.inference.sh/tasks/$TASK_ID \14  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "bytedance/seedance-2-0",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "bytedance/seedance-2-0",6    "input": {7        "prompt": "@Image1 is the character reference. @Video1 provides the motion. A cat stretches lazily on a sunlit windowsill."8    },9    "webhook": "https://your-server.com/webhook"10  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

promptstring*

text prompt describing the video content. use @image1, @image2, @video1, @audio1 to reference inputs in order (e.g. '@image1 is the style reference, @video1 provides the motion'). supports english, japanese, indonesian, spanish, and portuguese.

example: "@Image1 is the character reference. @Video1 provides the motion. A cat stretches lazily on a sunlit windowsill."
imagestring(file)

first-frame image for image-to-video generation. mutually exclusive with reference inputs.

end_imagestring(file)

last-frame image for first+last frame video generation. requires image to be set as the first frame.

reference_imagesarray

reference images for multimodal generation (up to 9). use prompt to describe how to use each, e.g. 'image 1', 'image 2'. mutually exclusive with image/end_image.

default: []
reference_videosarray

reference videos for multimodal generation (up to 3). max 15s each, total max 15s. formats: mp4/mov. mutually exclusive with image/end_image.

default: []
reference_audiosarray

reference audios for multimodal generation (up to 3). max 15s each, total max 15s. formats: wav/mp3. requires at least one image or video.

default: []
resolutionstring

video resolution. 4k for ultra-high quality (10-bit color, ~2x cost of 1080p), 1080p for high quality, 720p for balanced, 480p for fastest.

default: "720p"
options:"480p""720p""1080p""4k"
ratiostring

aspect ratio. 'adaptive' auto-selects based on input content.

default: "adaptive"
options:"adaptive""21:9""16:9""4:3""1:1""3:4""9:16"
durationinteger

duration in seconds (4-15), or -1 for auto-select.

default: 5
generate_audioboolean

whether to generate synchronized audio with the video.

default: true
seedinteger

seed for reproducibility (-1 for random).

default: -1
watermarkboolean

whether to add watermark to the output video.

default: false
safety_identifierstring

unique identifier of end user for platform safety policy. must be fixed and unique per user, max 64 chars. recommended: hash of username, user id, or email.

output

videostring(file)*

the generated video file.

other video apps

VACE

pruna/vace

$0.40/video

P-Video-Animate

pruna/p-video-animate

$0.03/sec 720p, $0.06/sec 1080p

P-Video-Upscale

pruna/p-video-upscale

Std: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x

P-Video-2-Pro-Avatar

pruna/p-video-2-pro-avatar

$0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k

P-Video-2-Pro-Reference

pruna/p-video-2-pro-reference

Output: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec

more from bytedance

Seedance 2.5

bytedance/seedance-2-5

$6.40–$11.70/M tokens, varies by output resolution and visual input

Seedance 2.5 Studio

bytedance/seedance-2-5-studio

$6.40–$11.70/M tokens, varies by output resolution and visual input

Seedance 2.0 Studio Mini

bytedance/seedance-2-0-studio-mini

from $0.00000277/sec

Seedance 2.0 Mini

bytedance/seedance-2-0-mini

$0.0035/K tokens (text-only), $0.0021/K tokens (visual input)

new models

  1. Tripo Auto Rig

    tripo/tripo-rig

    3d$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

  2. Hunyuan 3D Auto Rigging

    tencent/hunyuan-3d-auto-rigging

    3d$0.02/credit, 10 credits/call ($0.20/call)

  3. TokenRig

    infsh/tokenrig

    3dGPU time, billed per millisecond

  4. Hunyuan 3D 3.1

    tencent/hunyuan-3d-3-1

    3d$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

  5. Rodin Gen-2.5

    hyper3d/rodin-gen-2-5

    3d$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

ready to run Seedance 2.0?

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