Image generation and editing

Use gpt-image-2 through the OpenAI Images API for both generation and editing. Check GET /openai/v1/models?api=images before calling; the native catalog declares openai.images.generations and openai.images.edits separately.

1. Prepare your terminal

Use Python 3.10+ and uv. Put your Praxis cloud API Key in PRAXIS_API_KEY; do not put it in source code or logs.

test -n "$PRAXIS_API_KEY"

Expected: exit code 0. Exit code 1 means the variable is missing. Examples default to https://api.deepraxis.com. To switch environments, set PRAXIS_API_BASE to the API origin without a trailing slash or API path.

2. Generate and edit

Run in a writable directory. This example makes two billable image requests: one generation and one edit. It saves circle.png and circle-edited.png; use a fresh directory to avoid overwriting existing files. Automatic retries are disabled.

Save as images.py and run uv run -q --no-project --with openai --with anthropic --with google-genai python images.py.

import base64
import os
from pathlib import Path
from openai import OpenAI

client = OpenAI(api_key=os.environ['PRAXIS_API_KEY'],
    base_url=os.environ.get('PRAXIS_API_BASE', 'https://api.deepraxis.com') + '/openai/v1',
    timeout=180, max_retries=0)
models = client.models.list(extra_query={'api': 'images'})
print('X-Request-Id:', models._request_id)
assert 'gpt-image-2' in [model.id for model in models]

def save(response, filename):
    print('X-Request-Id:', response._request_id)
    assert len(response.data) == 1 and response.data[0].b64_json
    image = base64.b64decode(response.data[0].b64_json, validate=True)
    assert image.startswith(b'\x89PNG\r\n\x1a\n'), 'Expected PNG output'
    Path(filename).write_bytes(image)
    assert response.usage is not None, 'Usage was not reported'
    print(filename, len(image), 'bytes; usage:', response.usage.model_dump())

save(client.images.generate(model='gpt-image-2',
    prompt='A single blue circle centered on a plain white background.',
    n=1, size='1024x1024', quality='low'), 'circle.png')
with open('circle.png', 'rb') as image:
    save(client.images.edit(model='gpt-image-2', image=image,
        prompt='Change the blue circle to a red circle. Keep the white background.',
        n=1, size='1024x1024', quality='low'), 'circle-edited.png')

Expected: both files are nonempty PNGs; each response contains one image and token usage. Exit code 0. Open the files: the first should show a blue circle, the second a red circle on white. The signature check proves file format, not visual accuracy.

3. Read image costs

gpt-image-2 is billed by tokens, not a fixed price per image. Text input, image input, and image output have separate rates. Editing includes input image tokens. Read the returned usage and the current prices in the model catalog; see usage and billing for account totals.

If the catalog assertion fails, stop before generating and choose a currently available image model. If an output assertion fails, retain the request ID and response error; do not repeatedly regenerate to diagnose a file problem.

Operation details: generate and edit.

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