Inference Logoinference shell

OmniZero

Creates stylized portraits instantly without needing specific training data.

text to imageimage to image
run in browser →run via API
run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# sign in (or set INFERENCE_API_KEY on a headless machine)
$belt login
# view schema & details
$belt app get infsh/omni-zero
# run
$belt app run infsh/omni-zero --input '{"prompt":"prompt","base_image":"https://example.com/file.png","composition_image":"https://example.com/file.png","style_image":"https://example.com/file.png","identity_image":"https://example.com/file.png"}'
maker
infsh
category
image
input → output
text → image, image → image
price
GPU time, billed per millisecond
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-07-29

api reference

about

creates stylized portraits instantly without needing specific training data.

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
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8        "app": "infsh/omni-zero",9        "input": {10            "prompt": "prompt",11            "base_image": "https://example.com/file.png",12            "composition_image": "https://example.com/file.png",13            "style_image": "https://example.com/file.png",14            "identity_image": "https://example.com/file.png"15        }16    })1718print(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
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9        "app": "infsh/omni-zero",10        "input": {11            "prompt": "prompt",12            "base_image": "https://example.com/file.png",13            "composition_image": "https://example.com/file.png",14            "style_image": "https://example.com/file.png",15            "identity_image": "https://example.com/file.png"16        }17    }, stream=True):18    if update.get("progress"):19        print(f"progress: {update['progress']}%")20    if update.get("output"):21        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/omni-zero",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/omni-zero",3    "input": {4        "prompt": "prompt",5        "base_image": "https://example.com/file.png",6        "composition_image": "https://example.com/file.png",7        "style_image": "https://example.com/file.png",8        "identity_image": "https://example.com/file.png"9    },10    "webhook": "https://your-server.com/webhook"11}, wait=False)

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)
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

seedinteger

seed

default: 42
promptstring*

prompt

negative_promptstring

negative prompt

default: "blurry, out of focus"
guidance_scalenumber

guidance scale

default: 3
number_of_stepsinteger

number of steps

default: 10
base_imagestring(file)*

base image

base_image_strengthnumber

base image strength

default: 0.15
composition_imagestring(file)*

composition image

composition_image_strengthnumber

composition image strength

default: 1
style_imagestring(file)*

style image

style_image_strengthnumber

style image strength

default: 1
identity_imagestring(file)*

identity image

identity_image_strengthnumber

identity image strength

default: 1
depth_imagestring(file)

depth image

depth_image_strengthnumber

depth image strength

default: 0.5

output

resultstring(file)*

result

other image apps

Gemini Nano Banana 2.1

google/gemini-nano-banana-2-1

$1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search

grok-imagine-image-2-0

xai/grok-imagine-image-2-0

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GPT Image 2.5 Sunburst

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$0.006-$0.21 per 1024x1024 image: low $0.006, medium $0.013, high $0.053, xhigh $0.094, max $0.21. Larger sizes cost more; edits add ~$0.008 per reference image.

GPT Image 2.5 Flare

openai/gpt-image-2-5-flare

$0.006-$0.21/image at 1024x1024: low $0.0059, medium $0.0132, high $0.0527, xhigh $0.0937, max $0.2107. Larger sizes cost more; edits add ~$0.0083 per reference image.

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from $0.00000277/sec

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