Inference Logoinference.sh
apps/infsh/bytedance-uso

bytedance-uso

A unified image editor that allows users to generate images by combining any subject with any style efficiently, preserving identity and consistency.

run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# view schema & details
$belt app get infsh/bytedance-uso
# run
$belt app run infsh/bytedance-uso

api reference

about

a unified image editor that allows users to generate images by combining any subject with any style efficiently, preserving identity and consistency.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash
1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash
1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python
1from inferencesh import inference23client = inference()456result = client.run({7        "app": "infsh/bytedance-uso",8        "input": {}9    })1011print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python
1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8        "app": "infsh/bytedance-uso",9        "input": {}10    }, stream=True):11    if update.get("progress"):12        print(f"progress: {update['progress']}%")13    if update.get("output"):14        print(f"output: {update['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

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python
1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/bytedance-uso",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

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

python
1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

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.

python
1result = client.run({2    "app": "infsh/bytedance-uso",3    "input": {},4    "webhook": "https://your-server.com/webhook"5}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "id": "task_abc123",3  "status": 9,4  "output": { ... },5  "error": "",6  "session_id": null,7  "created_at": "2024-01-15T10:30:00Z",8  "updated_at": "2024-01-15T10:30:05Z"9}
idstringtask id
statusnumberterminal status (9=completed, 10=failed, 11=cancelled)
outputobjecttask output (when completed)
errorstringerror message (when failed)
session_idstringsession id (if using sessions)
created_atstringiso timestamp
updated_atstringiso timestamp

5. schema

input

promptstring*

text prompt for image generation

content_imageobject

content reference image for subject/identity-driven generation

style_imageobject

style reference image for style transfer

extra_style_imageobject

extra style reference image (experimental feature)

widthinteger

generation width (512-1536, step 16). if not specified, inferred from input image

min:512max:1536
heightinteger

generation height (512-1536, step 16). if not specified, inferred from input image

min:512max:1536
num_stepsinteger

number of inference steps (1-50)

default: 25min:1max:50
guidancenumber

guidance scale (1.0-5.0)

default: 4min:1max:5
seedinteger

random seed (-1 for random)

default: -1
keep_sizeboolean

keep input image size (for style editing)

default: false
content_long_sizeinteger

content reference image size (0-1024)

default: 512min:0max:1024

output

generated_imageobject*

generated stylized image

ready to run bytedance-uso?

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