apps/infsh/sdxl

sdxl

Generates and modifies images from text prompts, producing high-resolution, photorealistic results with superior detail and accuracy compared to previous versions.

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run with your agent

# install belt

$curl -fsSL https://cli.inference.sh | sh

# view schema & details

$belt app get infsh/sdxl

# run

$belt app run infsh/sdxl

api reference

about

generates and modifies images from text prompts, producing high-resolution, photorealistic results with superior detail and accuracy compared to previous versions.

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/sdxl",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/sdxl",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/sdxl",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/sdxl",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}

idstring— task id

statusnumber— terminal status (9=completed, 10=failed, 11=cancelled)

outputobject— task output (when completed)

errorstring— error message (when failed)

session_idstring— session id (if using sessions)

created_atstring— iso timestamp

updated_atstring— iso timestamp

5. schema

input

promptstring*

the text prompt to generate the image from

example: "A majestic lion jumping from a big stone at night"

negative_promptstring

negative prompt to avoid certain elements in the generated image

example: "blurry, low quality, distorted"

num_inference_stepsinteger

number of denoising steps

default: 50min:1max:100

guidance_scalenumber

classifier-free guidance scale

default: 7.5min:1max:20

widthinteger

width of the generated image

default: 1024min:256max:2048

heightinteger

height of the generated image

default: 1024min:256max:2048

model_urlstring

url or huggingface path to a custom stable diffusion xl model

default: "stabilityai/stable-diffusion-xl-base-1.0"example: "stabilityai/stable-diffusion-xl-base-1.0"

lorasarray

list of lora configs to apply

output

resultobject*

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

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