How to Use Seedgream 4.5 API

Seedream 4.5 is the newest evolution of the Seedream family of text-to-image / image-editing models (developed under Byte/BytePlus research). It’s being rolled out across official BytePlus endpoints and multiple third-party platforms — including integrated access via multi-model gateways such as CometAPI — and brings improved subject consistency, typography/text rendering, and multi-image editing fidelity.

This article is a hands-on, professional guide to using the Seedream 4.5 API. You’ll get practical setup steps, authentication and request patterns, prompt and parameter best practices, editing & multi-image workflows, error handling, deployment patterns, and legal/safety considerations.

What is Seedream 4.5?

Seedream 4.5 is the latest iteration of the Seedream family — a multimodal image generation and editing model designed for high-fidelity text-to-image creation and context-aware image editing (image-to-image, multi-reference editing, inpainting/outpainting, typography and dense-text handling). Compared with earlier Seedream releases, 4.5 focuses on improved subject consistency across multi-image workflows, stricter preservation of reference details, higher typographic fidelity (text in images), and better output quality up to 4K/ultra-HD in “high quality” settings. These improvements come as part of a scaled architecture and updated prompt tuning / engine-side heuristics.

Why this matters: 4.5 is intentionally built to handle professional creative tasks — batch product variations, brand-consistent multi-image edits, and high-res print assets — while enabling finer control with reference images and specialized editing operations.

Core capabilities

• Text-to-image generation (single and batch): generate 1–15 images per API call, with selectable quality modes (Basic vs High) that trade off speed and resolution.
• Image editing (i2i / inpainting / outpainting): use one or more reference images; preserves detail and spatial relationships across multiple references.
• Multi-reference blending & element copy: up to ~10 reference images can be used in a single job to transplant elements while keeping lighting/perspective coherent.
• High typography/dense-text rendering: better handling for images with text or signage (useful for mockups, product labels, UI screenshots).
• Streaming / progressive output: some deployment endpoints support streaming results so clients can receive partial results while generation continues.

How do I Use Seedream 4.5 API through CometAPI?

Below is a practical, copy-pasteable walkthrough for generating images through CometAPI (an aggregator that exposes the Seedream 4.5 model as a model parameter). Use CometAPI when you want one API key to access dozens/hundreds of models and a stable, easy-to-integrate REST surface. The CometAPI documentation shows the doubao-seedream-4-5-251128 model alias and a standard images generation endpoint.

High-level steps

1. Sign up for CometAPI and get an API key.
2. Use the images generation endpoint (POST https://api.cometapi.com/v1/images/generations) with model param set to the Seedream 4.5 identifier (example: doubao-seedream-4-5-251128).
3. Include prompt, optional reference images (URLs or multipart uploads depending on the aggregator), output size/quality, and other parameters.
4. Receive a JSON response containing generated image URLs (or base64) and metadata.

Request types and modes

Seedream 4.5 commonly supports:

• Text → Image (text prompts → novel images)
• Image → Image (reference images + prompts for stylized transforms)
• Image Editing / Inpainting (mask + edit instructions for targeted changes)
  hosted APIs support asynchronous task modes (submit job → poll with taskId) which fits long-running renders and batch workflows. Generated links are often time-limited (e.g., valid for 24 hours on some gateways) so plan for storage/export.

curl example (text-to-image, single prompt)

curl -X POST "https://api.cometapi.com/v1/images/generations" \
  -H "Authorization: Bearer COMETAPI_KEY_GOES_HERE" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "doubao-seedream-4-5-251128",
    "prompt": "A cinematic portrait of a cyberpunk fox in neon rain, 4k, detailed lighting, film grain",
    "n": 3,
    "width": 2048,
    "height": 2048,
    "quality": "high",     # or "basic"
    "seed": 12345,
    "style": "photorealistic"
  }'

Notes

• Replace COMETAPI_KEY_GOES_HERE with your CometAPI key.
• The n parameter generates multiple variations in one call (saves overhead).
• quality: "high" typically maps to a higher resolution / higher compute cost (often 4K-capable).

Python requests example (text-to-image + saving results)

import requests, base64, os

API_URL = "https://api.cometapi.com/v1/images/generations"
API_KEY = os.environ.get("COMETAPI_KEY")  # set env var for safety

payload = {
  "model": "doubao-seedream-4-5-251128",
  "prompt": "Studio shot of a ceramic mug on a wooden table, warm natural light, ultra-detailed, 2k",
  "n": 2,
  "width": 1024,
  "height": 1024,
  "quality": "basic"
}

resp = requests.post(API_URL, json=payload, headers={
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
})
resp.raise_for_status()
data = resp.json()

# Example: each item has 'b64_json' or 'url' depending on provider
for i, item in enumerate(data.get("data", [])):
    img_b64 = item.get("b64_json")
    if img_b64:
        img_bytes = base64.b64decode(img_b64)
        with open(f"seedream_result_{i}.png", "wb") as f:
            f.write(img_bytes)
    else:
        print("Image URL:", item.get("url"))

Why this pattern? Aggregators commonly return either a base64 payload or hosted URLs; code handles both. The endpoint will typically return a task_id. Poll the GET /tasks/{task_id} endpoint until status is succeeded and then download the result. Many providers include SDKs with built-in helpers for this pattern.

How do I optimize image quality and keep text legible?

1. Use reference images for consistent context and color matching.
2. Call out typography explicitly in the prompt (font family, weight, alignment) and consider adding the exact text as an overlay in a secondary step to ensure legibility.
3. Run a two-step process: (a) generate base composition; (b) re-render or edit in a second pass focused on closeups or label areas with higher resolution.

How should you write prompts for Seedream 4.5?

Prompt engineering principles

• Be explicit: list subject, action, style, lens/camera, time of day, and desired color palette.
• Use identity anchors: If you need the same face/prop across images, include persistent descriptors (e.g., “the same woman with short wavy hair, green jacket, scar on left eyebrow”) and supply 1–3 reference images. Seedream 4.5’s multi-reference fusion improves, but anchors help.
• Negative prompts: explicitly state what to avoid (e.g., “no text”, “no watermarks”, “no extra limbs”).
• Short + long hybrid: give a short canonical instruction then extend with a few lines of detail and constraints.

Example prompt templates

Product hero shot (photoreal): "A clean product hero shot of a matte black wireless speaker placed on a white tabletop, softbox lighting, 50mm, shallow depth of field, studio background, photoreal, no text"

Fantasy illustration (stylized): "Epic fantasy landscape, towering glass castle on a cliff, golden hour, volumetric fog, painterly, highly detailed, concept art"

Image edit (remove object) : "Remove the person on the left and extend the background to fill the space, keep lighting consistent, no artifacts"

Typography-heavy mockup: "Mobile app landing screen mockup on an iPhone 14, with the text 'Launch Now' in Gotham Bold, make the button green and keep shadows soft"

Character portrait: "Heroic portrait of a female warrior, cinematic rim lighting, 85mm portrait lens, ultra-detailed skin texture, natural freckles, leather armor, neutral background, photorealistic."

Multi-image and reference prompts

When using multi-image editing, specify which reference image maps to which part of the prompt. Seedream 4.5 improves at identifying the main subject across multiple references — but being explicit (e.g., “use image_1 for face, image_2 for clothing texture”) yields better results.

Output selection & postprocessing

• Generate N variants and run objective filters: face similarity score, color histogram comparison, typography OCR to check text accuracy.
• Automate QC thresholds to route outputs under the threshold for manual retouch.
• Offload final typography to layout tools if you need pixel-exact text — use the model for backgrounds and imagery, then composite precise text in post. This reduces the need to rely on model text fidelity for marketing assets.

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How do you perform image editing, inpainting, and multi-image composition?

Image editing workflow

1. Upload reference image(s) to the provider or send them inline with the request.
2. Provide a mask (binary image) for inpainting or a bounding annotation for targeted edits.
3. Send an edit prompt clarifying which regions to change and which to preserve.

Many APIs support both single-image edit and multi-image composition modes; 4.5 is explicitly tuned to preserve subject identity and improve multi-image consistency.

Example: Inpainting payload (JSON pseudocode)

{
  "model": "seedream-4.5",
  "mode": "image_edit",
  "image_url": "https://.../original.png",
  "mask_url": "https://.../mask.png",
  "prompt": "Replace background with a sunset beach — keep subject untouched, maintain original lighting on subject",
  "guidance": 9,
  "steps": 40
}

Tips for consistent multi-image editing

• Use the same seed for related renders to keep consistency across frames.
• Keep camera descriptors consistent across prompts (e.g., “85mm portrait, softbox, 3/4”) to maintain viewpoint consistency.
• When editing faces, request fine-grained preservation clauses (“preserve facial structure, change hair color only”) to reduce identity drift.

What are the best practices when using Seedream 4.5

How do you troubleshoot common issues?

Here are practical troubleshooting steps when results are off:

Blurry faces / wrong details

Increase prompt specificity for facial details (age, expression, lighting), supply higher-quality reference images, or try an explicit “preserve face” instruction and lower edit strength to keep more of the original. Seedream 4.5 improves facial realism, but inputs still matter.

Text is unreadable or garbled

Provide vector or raster text as a separate overlay if you need pixel-perfect typography; otherwise, use higher resolution settings and explicit “render legible text: yes” style instructions. 4.5 improves dense-text handling compared to prior versions, but typographic perfection may still require compositing in post.

Inconsistent batch lighting or composition

Use a templated prompt with fixed lighting/camera mentions, or generate within a single batch call to increase consistency. BytePlus and CometAPI provide batch inference patterns for that reason.

Final notes and next steps

Seedream 4.5 is a mature, production-oriented image model with explicit improvements aimed at real creative workflows: better consistency, improved text and facial rendering, and multi-reference support.use CometAPI or similar aggregators when you want fast experimentation and multi-model flexibility.

Developers can access [Seedream 4.5](http://Seedream 4.5) API etc through CometAPI, the latest model version is always updated with the official website. To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

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