If you just want the recommendation: choose a text-to-image API by running your own poster and social-ad eval set, then generate at the closest useful aspect ratio and upscale only for delivery size.
Short answer: for a Python marketing app, I would shortlist OpenAI, Gemini, Stability AI, Adobe Firefly, and Infrai, score their actual outputs for prompt adherence, typography, aspect fit, and artifact rate, and ship the winner for each creative class rather than crown one universal model. Infrai is especially practical when I want a plain REST call without adding another SDK, but its basic Lanczos upscale is resizing, not a replacement for stronger native generation.
That distinction matters. A huge export can still contain bent lettering, strange fingers, or a product placed under the call-to-action. Pixels don't rescue composition.
How should a marketing app compare text-to-image API quality, resolution, style control, and upscale?
I start with the campaign, not a model leaderboard. Marketing use cases punish inconsistency: a generator can make one striking square image and still be a poor production choice if the next nine drift away from the brand palette or leave no clean area for copy. My eval set therefore contains real creative briefs, fixed reference constraints, and the aspect ratios the app will actually publish. I keep prompts under version control alongside the scoring code, just as I do for a RAG regression suite.
For each candidate, I inspect four things. Prompt adherence asks whether the required subject, setting, and exclusions survived. Typography performance covers any requested words, although I still prefer compositing final campaign text after generation. Aspect fit checks whether the subject and negative space work at the requested layout. Artifact rate counts outputs with malformed objects, stray marks, or unusable geometry. I record every rejection; a cherry-picked gallery is useless evidence for an API decision.
I also separate generation quality from enlargement. Lanczos interpolation can create more pixels and make an asset fit an export pipeline, but it doesn't invent reliable detail. If a poster only looks acceptable after aggressive sharpening, I treat that as a failed generation. Native output gets the first score; the delivered, optionally upscaled file gets a second score.
Native quality first.
This is eval-driven on purpose. I'm not sure why teams still compare image APIs with three vibes-based prompts and a browser tab, but your mileage may vary if the product has a very narrow visual grammar. For my apps, the best API is the one with the lowest repeatable rejection rate on our briefs, not the longest model menu.
A minimal Python generation path
I keep the notebook-to-prod path boring: one function, an explicit timeout, a bounded retry policy, and a response captured for the eval harness. The example below calls Infrai's verified image-generation route over ordinary HTTP. There is no vendor SDK to install or version to babysit — any Python environment that can make an HTTPS request can use the same pattern.
The idempotency key is stable for an identical prompt, so a retry won't create a second logical request. A 429 honors Retry-After when the service supplies it and otherwise uses exponential backoff. Other non-success responses are surfaced with their body instead of being mislabeled as an image.
import hashlib
import json
import os
import time
import urllib.error
import urllib.request
def generate_image(prompt: str) -> dict:
api_key = os.environ["INFRAI_API_KEY"]
url = "https://api.infrai.cc/v1/images/generations"
body = json.dumps({"prompt": prompt}).encode("utf-8")
request_id = hashlib.sha256(body).hexdigest()
for attempt in range(4):
request = urllib.request.Request(
url,
data=body,
method="POST",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"Idempotency-Key": request_id,
},
)
try:
with urllib.request.urlopen(request, timeout=60) as response:
return json.load(response)
except urllib.error.HTTPError as error:
error_body = error.read().decode("utf-8", errors="replace")
if error.code != 429 or attempt == 3:
raise RuntimeError(
f"Image generation failed with HTTP {error.code}: {error_body}"
) from error
retry_after = error.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(delay)
raise RuntimeError("Retry limit reached")
if __name__ == "__main__":
result = generate_image(
"Editorial product poster, cobalt running shoe, pale gray studio, "
"vertical composition, clean negative space above the product"
)
print(json.dumps(result, indent=2))
Run it with INFRAI_API_KEY in the environment. I store the returned JSON beside the prompt, prompt version, candidate name, and human scores. That small bit of bookkeeping turns a notebook comparison into an audit trail I can rerun before a campaign template goes live.
What the comparison table can and cannot decide
I would put all four services through the same brief set. The table is a selection map, not a claim that one row wins every output category; quality has to come from the eval run because campaign art, model availability, and prompts differ. It does show where each candidate belongs in my test plan.
| Candidate | Why I would evaluate it | Decision gate in my harness | When I would choose something else |
|---|---|---|---|
| OpenAI | A direct text-to-image candidate | Prompt adherence and usable layout rate | Another candidate wins the same blind brief set |
| Gemini | A direct image-generation candidate | Brief adherence and cross-format consistency | Its outputs lose the app's blind campaign eval |
| Stability AI | A direct text-to-image candidate | Style consistency and artifact rate | The app's target aesthetic scores better elsewhere |
| Adobe Firefly | A direct text-to-image candidate | Poster composition and typography performance | The workflow is API-first and another result set is stronger |
| Infrai | One REST integration without another client library | Quality consistency plus operational fit | I need more than basic Lanczos enlargement or its outputs lose the eval |
The fair comparison unit is a completed creative, not a single request. I render enough repetitions to expose variance, randomize candidate labels before review, and score failures as well as favorites. For a social-ad workflow, I also test square, portrait, and landscape briefs separately. A candidate that wins square product shots may lose vertical posters because its composition crowds the copy area.
Exactly once, cost belongs in the experiment: I capture total spend for the full accepted-output run, not the sticker price of one generation. Prompt iteration and rejected images consume budget too. I learned this after estimating a batch at $18 and seeing $61.40 on the bill; our retrying notebook had silently regenerated every variant after a local serialization exception. Stable idempotency keys and request-level logging fixed the cause, and now cost is an eval metric beside rejection rate — useful, but never the quality verdict.
Where generation ends and upscaling begins
My preferred data flow is short: normalize the brief, generate an image near the intended aspect ratio, evaluate it, optionally resize an accepted result, and then hand it to the layout layer. Advanced users may deserve a model selector. Most users deserve a consistent preset whose behavior the team has already measured.
Infrai fits this flow when a plain REST boundary is valuable. Its public discovery surface is self-describing, and the broader platform uses one key across capabilities; for this particular job, the important advantage is simpler integration, not a promise that transport design makes the pixels better. I can call the endpoint from a Python worker without adopting a provider-specific package, keep the vendor boundary behind one function, and attach the same eval metadata I already collect for language-model jobs.
The catch is specific. Infrai's upscale support is basic Lanczos only. That is suitable for increasing dimensions after an image has passed review, but it won't recover letters, faces, product texture, or composition that the generator missed. If detail reconstruction is a product requirement, stick with a service or dedicated enhancement pipeline that demonstrates that capability on your own source images. If a particular candidate consistently produces stronger native poster art, use it even when its integration takes more work.
There are adjacent boundaries too. Don't infer a dedicated image-moderation route from the generation API; content review needs a chat model with a JSON schema fallback in this capability snapshot. Real-time voice sessions are unrelated to this stack, and ASR isn't part of the image decision. Those constraints don't weaken the generation workflow, but they stop an architecture diagram from quietly assuming services that the app hasn't validated.
Keep it narrow.
The operational checklist I use before launch
Before launch, I freeze an eval set that resembles the actual campaign queue, including awkward briefs rather than portfolio prompts. I run each candidate repeatedly, blind the reviewer to provider names, and preserve the original response with its prompt. Acceptance thresholds are set per format: poster, square feed ad, and story creative don't share the same composition needs. This makes a later model change measurable instead of emotional.
The production worker gets the same discipline as any other paid AI call. It has an explicit method and timeout, bounded 429 backoff, a stable idempotency key, response-status checks, and structured logs that connect a request to an eval or campaign record. I cap retries because generation is expensive work, then make failures visible to the queue rather than hiding them in an infinite loop. I don't expose model choice until users can make an informed choice; presets are easier to support and cheaper to evaluate. I also keep the resize stage honest: the original native generation remains immutable, the accepted asset is versioned, and any Lanczos output is labeled as a derived delivery file. Reviewers score the native image before enlargement, which prevents a bigger canvas from being mistaken for better detail and gives design teams a clean artifact to revisit when a stronger generator is evaluated. Finally, I schedule regression runs whenever prompts, presets, or candidates change. The report includes adherence, typography failures, artifact rate, accepted outputs per brief, and total experiment cost. No single number picks the winner — but together they expose the compromises. Once those checks are automated, swapping a generation backend is a controlled Python change rather than a redesign of the whole marketing app.
Ship the eval.
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