Inference Logoinference shell
apps/openai/gpt-image-2-5-flare

gpt-image-2-5-flare

GPT Image 2.5 Flare — OpenAI's fast, high-quality everyday image model. Higher quality than GPT Image 2 at 50% lower latency. Text-to-image, reference-image editing, mask inpainting, transparent backgrounds, quality up to max.

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 openai/gpt-image-2-5-flare
# run
$belt app run openai/gpt-image-2-5-flare

api reference

about

gpt image 2.5 flare — openai's fast, high-quality everyday image model. higher quality than gpt image 2 at 50% lower latency. text-to-image, reference-image editing, mask inpainting, transparent backgrounds, quality up to max.

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": "openai/gpt-image-2-5-flare",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": "openai/gpt-image-2-5-flare",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": "openai/gpt-image-2-5-flare",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": "openai/gpt-image-2-5-flare",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*

text prompt describing the desired image.

example: "A cat wearing a tiny top hat, oil painting style"
imagesarray

optional reference image(s) for editing. when a mask is provided, it applies to the first image.

maskstring(file)

optional mask image indicating areas to edit (requires input images). transparent areas in the mask indicate where the image should be edited. applied to the first image.

widthinteger

output image width in pixels. must be a multiple of 16.

default: 1024min:256max:3840
heightinteger

output image height in pixels. must be a multiple of 16.

default: 1024min:256max:3840
qualitystring

rendering quality. 'low' for fast drafts, 'high' for final assets, 'xhigh' and 'max' for maximum detail at higher cost.

default: "auto"
options:"auto""low""medium""high""xhigh""max"
ninteger

number of images to generate (1-10).

default: 1min:1max:10
output_formatstring

output file format.

default: "png"
options:"png""jpeg""webp"
output_compressioninteger

compression level for jpeg/webp (0-100). ignored for png.

min:0max:100
moderationstring

content moderation strictness. 'auto' applies standard filtering; 'low' is less restrictive.

default: "auto"
options:"auto""low"
backgroundstring

background transparency. 'transparent' produces an alpha-channel image (requires png or webp output; prompt for an isolated subject, not a scene). 'opaque' forces a solid background. 'auto' lets the model decide.

default: "auto"
options:"auto""transparent""opaque"

output

imagesarray*

the generated image files.

ready to run gpt-image-2-5-flare?

try in browserbrowse all tools

we use cookies

we use cookies to ensure you get the best experience on our website. for more information on how we use cookies, please see our cookie policy.

by clicking "accept", you agree to our use of cookies.
learn more.