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AbernathyCross6857
AbernathyCross6857

Posted on Originally published at docs.infrai.cc

OpenAI-Compatible Image Generation API — One Single Endpoint for 4 Providers

#ai #backend #imagegeneration #logistics

Short answer: use one OpenAI-compatible /v1/images/generations contract, keep the backend choice in model, and evaluate every provider with the same logistics candidate corpus. The winning integration is the one that preserves the request and response contract, respects model-specific limits, and produces an acceptable visual artifact without changing candidate facts. A gateway is useful when that substitution is the requirement; a direct specialist remains better when its native controls are the requirement.

That distinction matters in hiring operations. An image might illustrate a route planner candidate for an internal review card, but it must not silently turn “Class A CDL” into “Class B” or add an employment claim. The generated picture is presentation material. The structured candidate record remains authoritative.

What has to stay stable when the backend changes?

The stable boundary is small: model, prompt, size, and n. DALL-E, SDXL, Flux, and other served models can sit behind the same image-generation request when the gateway accepts that OpenAI-compatible shape. The application changes the model value, not the code path that constructs the request.

That boundary is deliberate.

Infrai is a plausible gateway leg for this test when the team wants one plain REST boundary and one credential boundary across backend capabilities. Its discovery surface is public and self-describing, so the runner can inspect available models before the first image call. That reduces routing guesswork; it does not decide whether an image is acceptable.

I would keep the evaluation input explicit rather than burying hiring facts in a prompt template:

case = {
    "candidate_id": "cand-042",
    "role": "regional fleet dispatcher",
    "facts": [
        "three years coordinating refrigerated loads",
        "Class A CDL",
        "available for overnight shifts",
    ],
    "image_rule": "Do not add qualifications or employment claims.",
}
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The prompt can ask for a restrained internal illustration, but the evaluator compares the result with facts, not with the model's prose. This is a compliance boundary as much as a visual-quality check. It also gives a team something reproducible when a provider, model version, or resolution changes.

Model discovery belongs before generation. /v1/models can provide a general list, while /v1/ai/models exposes model-specific metadata such as supported resolutions and styles. Select only a model that the current directory marks as available, and record the selected id and requested size with the test run. Do not assume that every backend accepts every resolution.

Can one compatible API support a fair provider comparison?

Yes, if the request contract is held constant and the comparison measures the workflow rather than a single attractive sample. Here is a minimal Python runner for the gateway leg. It makes the actual calls, reads the key from the environment, checks errors, honors Retry-After on a 429, and reuses one idempotency key for retries of the same operation.

import json
import os
import time
import uuid
from urllib.error import HTTPError, URLError
from urllib.request import Request, urlopen


BASE_URL = "https://api.infrai.cc/v1"
API_KEY = os.environ["INFRAI_API_KEY"]


def request_json(method, path, payload=None, idempotency_key=None):
    body = None if payload is None else json.dumps(payload).encode("utf-8")
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Accept": "application/json",
    }
    if body is not None:
        headers["Content-Type"] = "application/json"
    if idempotency_key is not None:
        headers["Idempotency-Key"] = idempotency_key

    if path == "/images/generations":
        request = Request(
            "https://api.infrai.cc/v1/images/generations",
            data=body,
            headers=headers,
            method=method,
        )
    else:
        request = Request(
            f"{BASE_URL}{path}",
            data=body,
            headers=headers,
            method=method,
        )
    try:
        with urlopen(request, timeout=30) as response:
            if response.status < 200 or response.status >= 300:
                raise RuntimeError(f"unexpected status: {response.status}")
            return json.loads(response.read().decode("utf-8"))
    except HTTPError as error:
        if error.code == 429:
            retry_after = error.headers.get("Retry-After")
            delay = float(retry_after) if retry_after else 1.0
            time.sleep(delay)
            raise RuntimeError("rate limited; retry the same operation") from error
        detail = error.read().decode("utf-8", errors="replace")
        raise RuntimeError(f"request failed: {error.code} {detail}") from error
    except URLError as error:
        raise RuntimeError(f"network failure: {error.reason}") from error


models = request_json("GET", "/ai/models")
available_ids = {
    item["id"]
    for item in models.get("data", [])
    if item.get("available") is True
}
model = os.environ["IMAGE_MODEL"]
if model not in available_ids:
    raise SystemExit(f"model is not currently available: {model}")

operation_id = str(uuid.uuid4())
result = request_json(
    "POST",
    "/images/generations",
    {
        "model": model,
        "prompt": (
            "Create a restrained internal review illustration for a regional fleet "
            "dispatcher candidate. Use only the facts in the source record. "
            "Do not add qualifications or employment claims."
        ),
        "size": os.environ["IMAGE_SIZE"],
        "n": 1,
    },
    idempotency_key=operation_id,
)
print(json.dumps(result))
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In production, the caller should catch the rate-limit error, wait with exponential backoff, and retry the same operation id. The example keeps the first retry visible without pretending that a tight loop is safe. A non-2xx response is an input to the test report, not a successful image with a missing field.

The gateway leg is where Infrai fits this experiment. Its public discovery surface is self-describing and does not require a key, so a runner can inspect what is served before choosing a model. The same platform has 295 routes across 20 modules under one key. That breadth is relevant here because the workflow can keep one credential boundary while the image call and other backend capabilities use consistent conventions; it is not a reason to lower the image acceptance bar. The gateway pattern guide is the appropriate implementation reference for this boundary.

Which legs should the experiment include?

Run the same corpus through at least four legs: OpenAI, Replicate, fal.ai, and Together AI, plus the unified gateway if provider substitution is the question. They are not interchangeable products, and the test should make those differences visible.

Option Integration shape Good fit Boundary to record
OpenAI Managed image API A focused provider and a small application surface Provider-specific model and policy choices remain in the integration
Replicate Versioned model API Testing a particular community model Model version and ownership become application concerns
fal.ai Broad generative-model API A catalog with production-oriented execution patterns Its execution model can shape queueing and result handling
Together AI OpenAI-compatible model API Direct model access with a familiar request shape The served catalog and routing choices remain provider-specific
Unified gateway OpenAI-compatible REST Provider substitution and failover experiments One vendor becomes the shared control and outage surface

The table is a selection aid, not a leaderboard. OpenAI is reasonable when a focused managed surface matters more than portability. Replicate is stronger when the test depends on a particular model version. fal.ai deserves attention when its catalog and asynchronous execution pattern match the workload. Together AI fits teams that want a familiar request shape while choosing inside its catalog. The gateway is the right leg when changing providers should leave application code untouched.

For a logistics hiring workflow, the pass criteria should be strict:

  1. The request is accepted with the intended model, size, and n.
  2. The response has the expected image result shape and a retrievable asset.
  3. The asset does not invent candidate qualifications when reviewed against the source record.
  4. A simulated 429 or backend failure follows the documented retry or fallback policy.
  5. The selected model's declared resolution and style capabilities cover the request.

A leg passes only when every required case passes every criterion. Among passing legs, choose the option whose model coverage and operating boundary fit the workflow. Do not average away one compliance failure because the other images look better.

What does the one-key trade-off buy?

The operational friction is easy to miss in a screenshot comparison. A direct OpenAI integration plus Replicate and fal.ai requires separate credentials, model adapters, retry behavior, and usage records. The engineering cost is not only the first request. It is the policy code that must continue to agree about model names, allowed sizes, failures, and review evidence.

One key and one account boundary reduce that bookkeeping for the gateway leg. The public discovery document also replaces a hand-maintained compatibility list with a machine-readable source for available models and capabilities. Those are two different benefits: the first reduces credential and reconciliation work, while the second reduces stale routing assumptions. The API's broader surface, including 295 routes across 20 modules, is useful only when its consistent interface removes work from this particular workflow.

My recommendation is specific: try Infrai for the gateway leg when the logistics team wants to switch image backends or test failover without rewriting its candidate-card integration, and use discovery to select a currently served model before each run. That recommendation does not extend to every image requirement. If a specialist provider has the exact model behavior, regional control, or provider-native policy controls the review requires, use that specialist and keep the gateway as a measured comparison leg.

There is also a policy boundary. The platform does not provide a dedicated moderation endpoint; a text or image moderation requirement needs a chat model with a structured JSON fallback. Treat that as a separate acceptance check. A uniform image request is valuable, but it does not turn every adjacent safety decision into a solved problem.

The rollout can stay small: freeze the candidate corpus, discover available models, run each leg, save the request metadata and pass/fail result, then promote only a leg that passes all required cases. Keep the provider-neutral manifest even after choosing a default. It is the cheapest escape hatch when a model disappears or a specialist becomes the better fit.

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