apps/infsh/hidream-e1-full

hidream-e1-full

Edit images by simply telling it what changes you want, such as altering colors, backgrounds, or accessories with precision.

run in browser run via API

run with your agent

# install belt

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

# view schema & details

$belt app get infsh/hidream-e1-full

# run

$belt app run infsh/hidream-e1-full

api reference

about

edit images by simply telling it what changes you want, such as altering colors, backgrounds, or accessories with precision.

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/hidream-e1-full",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/hidream-e1-full",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/hidream-e1-full",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/hidream-e1-full",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*

editing instruction followed by target description. format: 'editing instruction: {instruction}. target image description: {description}'

imageobject*

input image to edit

negative_promptstring

negative prompt to avoid unwanted features

default: "low resolution, blur"

seedinteger

the seed for the random number generator (-1 for random)

default: -1

num_inference_stepsinteger

number of denoising steps

default: 28min:1max:100

guidance_scalenumber

text guidance scale - how closely to follow the prompt

default: 5min:0max:20

image_guidance_scalenumber

image guidance scale - how closely to follow the input image

default: 4min:0max:20

refine_strengthnumber

refinement strength (0.0 disables, 1.0 full refinement). works with all variants - uses i1 base + e1 lora when > 0.0, standalone e1 when = 0.0.

default: 0.3min:0max:1

output

resultobject*

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

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