Seedream v4.5 Sequential API by ByteDance
ByteDance latest image generation model with batch generation support. Generate up to 15 images in a single request.
الإدخال
الإخراج
في انتظار التنفيذكل مرة ستكلف $0.036 مع $10 يمكنك التشغيل حوالي 277 مرة
يمكنك المتابعة بـ:
مثال الكود
import requests
import time
# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seedream-v4.5/sequential",
"prompt": "A beautiful landscape with mountains and lake",
"width": 512,
"height": 512,
"steps": 20,
"guidance_scale": 7.5,
}
generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]
# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
def check_status():
while True:
response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
result = response.json()
if result["data"]["status"] == "completed":
print("Generated image:", result["data"]["outputs"][0])
return result["data"]["outputs"][0]
elif result["data"]["status"] == "failed":
raise Exception(result["data"]["error"] or "Generation failed")
else:
# Still processing, wait 2 seconds
time.sleep(2)
image_url = check_status()التثبيت
قم بتثبيت الحزمة المطلوبة للغة البرمجة الخاصة بك.
pip install requestsالمصادقة
تتطلب جميع طلبات API المصادقة عبر مفتاح API. يمكنك الحصول على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"ترويسات HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}لا تكشف أبدًا مفتاح API الخاص بك في الكود من جانب العميل أو المستودعات العامة. استخدم متغيرات البيئة أو وكيل الخادم الخلفي بدلاً من ذلك.
إرسال طلب
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"prompt": "A beautiful landscape"
}
response = requests.post(url, headers=headers, json=data)
print(response.json())إرسال طلب
أرسل طلب توليد غير متزامن. تُرجع API معرّف التنبؤ الذي يمكنك استخدامه للتحقق من الحالة واسترداد النتيجة.
/api/v1/model/generateImageنص الطلب
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "bytedance/seedream-v4.5/sequential",
"input": {
"prompt": "A beautiful landscape with mountains and lake"
}
}
response = requests.post(url, headers=headers, json=data)
result = response.json()
print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")الاستجابة
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}التحقق من الحالة
استعلم عن نقطة نهاية التنبؤ للتحقق من الحالة الحالية لطلبك.
/api/v1/model/prediction/{prediction_id}مثال الاستعلام
import requests
import time
prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
while True:
response = requests.get(url, headers=headers)
result = response.json()
status = result["data"]["status"]
print(f"Status: {status}")
if status in ["completed", "succeeded"]:
output_url = result["data"]["outputs"][0]
print(f"Output URL: {output_url}")
break
elif status == "failed":
print(f"Error: {result['data'].get('error', 'Unknown')}")
break
time.sleep(3)قيم الحالة
processingلا يزال الطلب قيد المعالجة.completedاكتمل التوليد. المخرجات متاحة.succeededنجح التوليد. المخرجات متاحة.failedفشل التوليد. تحقق من حقل الخطأ.استجابة مكتملة
{
"data": {
"id": "pred_abc123",
"status": "completed",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}
}رفع الملفات
ارفع الملفات إلى تخزين Atlas Cloud واحصل على URL يمكنك استخدامه في طلبات API الخاصة بك. استخدم multipart/form-data للرفع.
/api/v1/model/uploadMediaمثال الرفع
import requests
url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
with open("image.png", "rb") as f:
files = {"file": ("image.png", f, "image/png")}
response = requests.post(url, headers=headers, files=files)
result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")الاستجابة
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
المعاملات التالية مقبولة في نص الطلب.
لا توجد معاملات متاحة.
مثال على نص الطلب
{
"model": "bytedance/seedream-v4.5/sequential"
}Output Schema
تُرجع API استجابة تنبؤ تحتوي على عناوين URL للمخرجات المولّدة.
مثال على الاستجابة
{
"id": "pred_abc123",
"status": "completed",
"model": "model-name",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}Atlas Cloud Skills
يدمج Atlas Cloud Skills أكثر من 300 نموذج ذكاء اصطناعي مباشرة في مساعد البرمجة بالذكاء الاصطناعي الخاص بك. أمر واحد للتثبيت، ثم استخدم اللغة الطبيعية لتوليد الصور ومقاطع الفيديو والدردشة مع LLM.
العملاء المدعومون
التثبيت
npx skills add AtlasCloudAI/atlas-cloud-skillsإعداد مفتاح API
احصل على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud وعيّنه كمتغير بيئة.
export ATLASCLOUD_API_KEY="your-api-key-here"الإمكانيات
بمجرد التثبيت، يمكنك استخدام اللغة الطبيعية في مساعد الذكاء الاصطناعي الخاص بك للوصول إلى جميع نماذج Atlas Cloud.
MCP Server
يربط Atlas Cloud MCP Server بيئة التطوير الخاصة بك بأكثر من 300 نموذج ذكاء اصطناعي عبر Model Context Protocol. يعمل مع أي عميل متوافق مع MCP.
العملاء المدعومون
التثبيت
npx -y atlascloud-mcpالتكوين
أضف التكوين التالي إلى ملف إعدادات MCP في بيئة التطوير الخاصة بك.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}الأدوات المتاحة
مخطط API
المخطط غير متاحSeedreamالصوت والصورة، كل شيء في لقطة واحدة
نموذج الذكاء الاصطناعي الثوري من ByteDance الذي ينشئ صوتًا وفيديو متزامنين تمامًا في وقت واحد من عملية موحدة واحدة. اختبر التوليد الصوتي المرئي الأصلي الحقيقي مع مزامنة الشفاه بدقة ميلي ثانية عبر أكثر من 8 لغات.
Key Updates
Experience the next level of AI-powered visual creation
Superior Aesthetics
Produces cinematic visuals with refined lighting and rendering for professional-grade output.
Higher Consistency
Maintains stable subjects, clear details, and coherent scenes across multiple images.
Smarter Instruction Following
Accurately responds to complex prompts with precise visual control and interactive editing.
Stronger Spatial Understanding
Generates realistic proportions, object placement, and scene layout with accuracy.
Richer World Knowledge
Creates knowledge-based visuals with accurate scientific and technical reasoning.
Deeper Industry Application
Supports professional workflows for e-commerce, film, advertising, gaming, and more.
Industry Applications
E-commerce
Product photography & marketing
Film & TV
Concept art & storyboarding
Advertising
Campaign visuals & creatives
Gaming
Character & environment design
Education
Instructional illustrations
Interior Design
Space visualization
Architecture
Architectural rendering
Fashion
Virtual try-on & styling
Improvements from 4.0
See how Seedream 4.5 outperforms the previous version
Face Quality
Significant improvement when face proportion is small
Text Rendering
Enhanced small character rendering capability
ID Preservation
Stronger identity retention ability
اختبر التوليد الصوتي المرئي الأصلي
انضم إلى صانعي الأفلام والمعلنين والمبدعين في جميع أنحاء العالم الذين يحدثون ثورة في إنشاء محتوى الفيديو بتقنية Seedance 1.5 Pro الرائدة.
Seedream 4.5 : A professional, high-fidelity multimodal image generation model by ByteDance Seed
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Seedream 4.5 |
| Developed By | ByteDance Seed |
| Release Date | December 2025 |
| Model Type | Multimodal Image Generation |
| Related Links | Official Website,Technical Paper (arXiv), GitHub Repository |
Introduction
Seedream 4.5 is a state-of-the-art, multimodal generative model engineered for scalability, efficiency, and professional-grade output. As an advanced version of Seedream 4.0, it is built upon a unified framework that seamlessly integrates text-to-image synthesis, sophisticated image editing, and complex multi-image composition. The model's primary design goal is to deliver professional visual creatives with exceptional consistency and fidelity. This is achieved through a significant scaling of the model architecture and training data, which enhances its ability to preserve reference details, render dense text and typography accurately, and understand nuanced user instructions.
Key Features & Innovations
- Unified Multimodal Framework: Integrates text-to-image (T2I), single-image editing, and multi-image composition into a single, cohesive model, allowing for diverse and flexible creative workflows.
- High-Fidelity & High-Resolution Generation: Capable of generating native high-resolution images (up to 4K), capturing fine details, realistic textures, and accurate lighting for professional use cases.
- Advanced Image Editing: Excels at preserving the core structure, lighting, and color tone of reference images while applying precise edits based on natural language instructions.
- Enhanced Multi-Image Composition: Accurately identifies and blends main subjects from multiple reference images, enabling complex creative compositions and style fusions.
- Superior Typography and Text Rendering: Features significantly improved capabilities for rendering clear, legible, and contextually integrated text within images.
- Efficient and Scalable Architecture: Built on a highly efficient Diffusion Transformer (DiT) and a powerful Variational Autoencoder (VAE), enabling fast inference and effective scalability.
- Optimized for Professional Use: Demonstrates strong performance in generating structured, knowledge-based content such as design materials, posters, and product visualizations, bridging the gap between creative generation and practical industry applications.
Model Architecture & Technical Details
Seedream 4.5's architecture is an extension of the foundation laid by Seedream 4.0. The core of the model is a highly efficient and scalable Diffusion Transformer (DiT), which significantly increases model capacity while reducing computational requirements for training and inference. This is paired with a powerful Variational Autoencoder (VAE) with a high compression ratio, which minimizes the number of image tokens processed in the latent space, further boosting efficiency.
Training and Data: The model was pre-trained on billions of text-image pairs, covering a vast range of taxonomies and knowledge-centric concepts. Training was conducted in multiple stages, starting at a 512x512 resolution and fine-tuning at progressively higher resolutions up to 4K. The post-training phase is extensive, incorporating Continuing Training (CT) for foundational knowledge, Supervised Fine-Tuning (SFT) for artistic quality, and Reinforcement Learning from Human Feedback (RLHF) to align outputs with human preferences. A sophisticated Prompt Engineering (PE) module, built upon the Seed1.5-VL vision-language model, is used to process user inputs and enhance instruction following.
Intended Use & Applications
Seedream 4.5 is designed for professional creators and applications demanding high-quality, consistent, and controllable image generation. Its intended uses include:
- Professional Content Creation: Generating cinematic-quality visuals for digital advertising, social media, and print.
- Advanced Photo Editing: Performing complex edits, such as changing clothing materials, modifying backgrounds, or adjusting lighting, while maintaining subject integrity.
- E-commerce and Product Visualization: Creating high-quality product showcases and marketing materials.
- Graphic Design: Designing posters, key visuals, and other materials that require the integration of stylized text and typography.
- Creative Storytelling: Producing sequential, thematically related images for storyboards or visual narratives.
Performance
Seedream 4.5 and its predecessor, Seedream 4.0, have demonstrated top-tier performance on public benchmarks. The models are evaluated on the Artificial Analysis Arena, a real-time competitive leaderboard that ranks models based on blind user votes.
Text-to-Image Leaderboard (December 2025)
| Rank | Model | Developer | ELO Score | Release Date |
|---|---|---|---|---|
| 1 | GPT Image 1.5 (high) | OpenAI | 1,252 | Dec 2025 |
| 2 | Nano Banana Pro | 1,223 | Nov 2025 | |
| 5 | Seedream 4.0 | ByteDance Seed | 1,193 | Sept 2025 |
| 7 | Seedream 4.5 | ByteDance Seed | 1,169 | Dec 2025 |
