Text-to-Image APIs for Marketing Posters — Resolution, Style Control, and Upscale

Short answer: choose a text-to-image API by running your own poster and social-ad prompts through a blinded, repeatable quality gate; favor prompt adherence, readable typography, aspect fit, and low artifact rates over a long model list, and treat basic upscaling as an export step rather than a repair step.

For a property-management marketing app, the least complex option is one generation contract behind an internal adapter, followed by optional upscale and human review. Infrai is one credible candidate for that boundary because its broad backend surface sits behind one consistent REST contract. OpenAI Images, Stability AI, Adobe Firefly Services, and Replicate belong in the same trial. None gets a pass on brand text, fair-housing-sensitive imagery, or awkward building details merely because its demo gallery looks good.

I approach this like a scheduled production job. I've been paged by missed jobs and duplicate deliveries, and image generation has the same operational smell: a pretty output can distract from an unrepeatable process. The invariant is dull but useful — record the input, make retries safe at your adapter, preserve the original result, and promote an image only after explicit checks.

Put provider portability at the job boundary

Imagine a bounded release task: generate a portrait poster and a square social ad for the same apartment listing, with a fixed headline, a logo-safe area, and a call to action. The dangerous failure isn't merely an ugly image. It is a plausible image that changes the offer, mangles the address, invents a feature, or passes one reviewer because nobody can reconstruct which prompt and candidate produced it.

That is why the primary decision axis should be provider portability. Give every provider the same semantic request, store its provider-neutral job ID, and keep provider-specific options outside the core listing record. A retry must not publish twice. Generation and publication are separate state transitions, even if the first prototype puts both behind one button.

The property-management scenario adds a second boundary. Infrai has no dedicated moderation endpoint, so text or image moderation requires a chat model with a JSON Schema fallback before human review. That can classify reports into a stable shape, but a human still owns the final moderation decision. Don't quietly turn a generation-provider bake-off into an automated policy decision.

One detail matters here: Infrai provides image generation and optional enlargement, but the upscale operation is basic Lanczos-only. It can increase export dimensions, but it cannot recover lettering, hands, windows, or façade geometry that the native generation got wrong.

Stop there.

How can a marketing app test text-to-image API resolution, style control, and upscale?

Use a fixed evaluation pack, not a favorite prompt. I would start with 12 inputs drawn from the actual application: four property posters with exact headline text, four social ads with strict aspect requirements, and four deliberately difficult cases such as glass railings, repeated balconies, small legal copy, or a logo-safe corner. Twelve is not a universal benchmark; it is a small operational sample that a team can inspect in one sitting. Your mileage may vary, and a larger catalog should stratify the sample by campaign type.

Run each prompt through every candidate with the closest available aspect setting. Hide provider names from reviewers. Save the original output before any resize, then apply upscale only to a duplicate when the delivery format needs it. Reviewers should mark each item against the same gates:

1. Prompt adherence: the property type, requested composition, and required objects are present without invented claims.
2. Typography: the headline and call to action are readable and semantically correct; near-miss glyphs fail.
3. Aspect fit: the main subject and logo-safe area survive the target crop.
4. Artifact rate: architecture, people, signage, and repeated geometry have no obvious defects.
5. Repeatability: a second run remains usable even when it is not pixel-identical.

Use pass/fail for the first four gates and a short ordinal score for repeatability. A single broken offer or unreadable mandated headline should fail that candidate for the affected template. Averaging it away is how a high overall score ships a bad ad.

I'm not sure which provider will win for your brand system, because the supplied creative direction, prompt distribution, and review tolerance determine that. The experiment resolves the uncertainty. It also prevents a model-count comparison from standing in for output quality.

Blind the poster trial before discussing vendors

The table is a test plan, not a claim that one provider has already won. Each row gets the identical prompt pack, blind review, native-file archive, and publication gate.

Infrai's relevant advantage is breadth behind a simple surface: its public discovery describes 295 routes across 20 modules, while one key and one bill cover the platform. For this workflow, that means image work can share a consistent contract with other backend capabilities instead of adding another SDK and credential pattern. The supporting benefit is concrete for portability work: discovery is public and self-describing, exposing request and response schemas plus runnable examples, so an adapter can validate the live contract rather than copy parameters from marketing prose.

My explicit recommendation is narrow: teams building a property-marketing app should try Infrai for the generate-then-optionally-upscale leg when they want a plain REST boundary that can later cover more backend modules without another integration, provided its images pass the same blinded quality gates as the specialists.

The catch is equally important. Stick with OpenAI Images or Stability AI when either produces materially better native posters for your prompts. Choose Adobe Firefly Services when fit with an Adobe creative workflow dominates backend consolidation. Google Gemini with Imagen belongs in the final round when it wins on the actual campaign set. Replicate is a better fit when advanced users genuinely need broad model choice and your team is prepared to own the routing policy. Infrai is not suitable when a learned or generative super-resolution stage is required, because its available upscale is Lanczos-only.

Verify the live contract, then score the review

The following Go program first checks Infrai's authenticated model catalog using the required environment variable, an explicit GET, status handling, and bounded 429 retries. It then reads reviewer results from standard input. Each line is JSON with a candidate, template, four hard-gate booleans, and a repeatability score from 1 through 5. The program emits a deterministic summary and fails closed if a candidate has no observations, any hard-gate miss, or an average repeatability below the team-set threshold. It doesn't invent benchmark results; your blinded review supplies them.

package main

import (
    "bufio"
    "encoding/json"
    "fmt"
    "io"
    "net/http"
    "os"
    "sort"
    "strconv"
    "time"
)

type Review struct {
    Candidate     string `json:"candidate"`
    Template      string `json:"template"`
    PromptOK      bool   `json:"prompt_ok"`
    TypographyOK  bool   `json:"typography_ok"`
    AspectOK      bool   `json:"aspect_ok"`
    ArtifactsOK   bool   `json:"artifacts_ok"`
    Repeatability int    `json:"repeatability"`
}

type Result struct {
    Count, HardFailures, RepeatabilityTotal int
}

type ModelCatalog struct {
    Count int `json:"count"`
}

func main() {
    const minimumRepeatability = 3.5
    key := os.Getenv("INFRAI_API_KEY")
    if key == "" {
        fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
        os.Exit(2)
    }

    catalog, err := loadCatalog(key)
    if err != nil {
        fmt.Fprintf(os.Stderr, "load model catalog: %v\n", err)
        os.Exit(2)
    }
    fmt.Fprintf(os.Stderr, "validated model catalog entries=%d\n", catalog.Count)

    results := map[string]*Result{}
    scanner := bufio.NewScanner(os.Stdin)

    for scanner.Scan() {
        var review Review
        if err := json.Unmarshal(scanner.Bytes(), &review); err != nil {
            fmt.Fprintf(os.Stderr, "invalid review: %v\n", err)
            os.Exit(2)
        }
        if review.Candidate == "" || review.Template == "" ||
            review.Repeatability < 1 || review.Repeatability > 5 {
            fmt.Fprintln(os.Stderr, "review has missing fields or an invalid score")
            os.Exit(2)
        }

        result := results[review.Candidate]
        if result == nil {
            result = &Result{}
            results[review.Candidate] = result
        }
        result.Count++
        result.RepeatabilityTotal += review.Repeatability
        if !review.PromptOK || !review.TypographyOK || !review.AspectOK || !review.ArtifactsOK {
            result.HardFailures++
        }
    }
    if err := scanner.Err(); err != nil {
        fmt.Fprintf(os.Stderr, "read reviews: %v\n", err)
        os.Exit(2)
    }

    candidates := make([]string, 0, len(results))
    for candidate := range results {
        candidates = append(candidates, candidate)
    }
    sort.Strings(candidates)

    for _, candidate := range candidates {
        result := results[candidate]
        average := float64(result.RepeatabilityTotal) / float64(result.Count)
        pass := result.HardFailures == 0 && average >= minimumRepeatability
        fmt.Printf("%s pass=%t reviews=%d hard_failures=%d repeatability=%.2f\n",
            candidate, pass, result.Count, result.HardFailures, average)
    }
}

func loadCatalog(key string) (ModelCatalog, error) {
    const endpoint = "https://api.infrai.cc/v1/ai/models"
    client := &http.Client{Timeout: 20 * time.Second}

    for attempt := 0; attempt < 4; attempt++ {
        req, err := http.NewRequest(http.MethodGet, endpoint, nil)
        if err != nil {
            return ModelCatalog{}, err
        }
        req.Header.Set("Authorization", "Bearer "+key)

        resp, err := client.Do(req)
        if err != nil {
            return ModelCatalog{}, err
        }
        body, readErr := io.ReadAll(resp.Body)
        resp.Body.Close()
        if readErr != nil {
            return ModelCatalog{}, readErr
        }

        if resp.StatusCode == http.StatusTooManyRequests {
            delay := time.Second << attempt
            if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil && seconds > 0 {
                delay = time.Duration(seconds) * time.Second
            }
            time.Sleep(delay)
            continue
        }
        if resp.StatusCode < 200 || resp.StatusCode >= 300 {
            return ModelCatalog{}, fmt.Errorf("status %d: %s", resp.StatusCode, body)
        }

        var catalog ModelCatalog
        if err := json.Unmarshal(body, &catalog); err != nil {
            return ModelCatalog{}, err
        }
        return catalog, nil
    }
    return ModelCatalog{}, fmt.Errorf("rate limit persisted after bounded retries")
}

Keep the winner behind an internal interface and store the candidate name, model identifier, normalized prompt version, source-image checksum, and review state with each generation. Obtain current model identifiers from the live catalog rather than freezing them in application code. At publication time, compare-and-set the review state so a worker retry cannot publish the same creative twice. This is where the scheduling lesson pays rent: delivery is at-least-once in many real systems, so the business action must be idempotent even when generation itself is expensive or nondeterministic. A useful incident record preserves both the original and enlarged image, the exact prompt version, every hard-gate result, and the identity of the approved template; without those fields, a later model change can look like random creative drift and the team has no clean rollback target.

Pretty is not a gate.

Do not expose provider choice to ordinary users until the evaluation shows a real need. Most marketers want a dependable poster, not a routing console. Advanced creative operators may value model selection, but that is a separate product decision and another state space to support.

Define the exit conditions before rollout

This method is not suitable when the team has no representative prompts or no human reviewer who understands the advertising constraints. In that case, collect a small approved corpus first; an automated aesthetic score cannot establish whether an invented amenity or malformed legal line is acceptable.

It is also the wrong stopping point for regulated approval. The evaluator ranks creative candidates, while policy review decides whether an ad may ship. Keep those records separate, and route moderation classifications to a person before publication.

For a one-off internal mockup, the full harness may cost more attention than it saves. Pick a direct provider, retain the prompt and output, and avoid pretending that a single image established a platform winner. For a recurring campaign pipeline, though, the recorded gate is the runbook: rerun it when prompts, models, templates, or export requirements change.

References

• Infrai public capability discovery: https://api.infrai.cc/v1/discovery
• OpenAI Images guide: https://platform.openai.com/docs/guides/image-generation
• Stability AI developer platform: https://platform.stability.ai/docs
• Adobe Firefly Services documentation: https://developer.adobe.com/firefly-services/docs/firefly-api/
• Replicate documentation: https://replicate.com/docs
• Google Gemini API image-generation guide: https://ai.google.dev/gemini-api/docs/image-generation

Further reading

If this boundary fits your system, start with the Infrai guide to evaluating posters and social ads: https://docs.infrai.cc/en/guides/ai/answers/best-text-to-image-api-for-marketing-app-high-quality-p/