Inference Logoinference.sh
apps/infsh/real-esrgan

real-esrgan

Enhances low-quality, degraded images and videos by upscaling resolution, reducing noise, and restoring fine details.

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

api reference

about

enhances low-quality, degraded images and videos by upscaling resolution, reducing noise, and restoring fine details.

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/real-esrgan",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/real-esrgan",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/real-esrgan",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/real-esrgan",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

input_fileobject*

a class representing a file in the inference.sh ecosystem.

model_namestring*

model name

options:"realesr-general-wdn-x4v3""realesr-general-x4v3""realesr-animevideov3""RealESRGAN_x2plus""RealESRGAN_x4plus_anime_6B""RealESRGAN_x4plus"
face_enhanceboolean

face enhance

default: false
outscalenumber

outscale

default: 4
tileinteger

tile

default: 0
tile_padinteger

tile pad

default: 10
pre_padinteger

pre pad

default: 0
fp32boolean

fp32

default: false

output

resultobject*

a class representing a file in the inference.sh ecosystem.

ready to run real-esrgan?

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