Open and Advanced Large-Scale Image Generative Models.
コード例
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/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()インストール
お使いの言語に必要なパッケージをインストールしてください。
pip install requests認証
すべての API リクエストには API キーによる認証が必要です。API キーは Atlas Cloud ダッシュボードから取得できます。
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP ヘッダー
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}API キーをクライアントサイドのコードや公開リポジトリに公開しないでください。代わりに環境変数またはバックエンドプロキシを使用してください。
リクエストを送信
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())リクエストを送信
非同期生成リクエストを送信します。API は予測 ID を返し、それを使用してステータスの確認や結果の取得ができます。
/api/v1/model/generateImageリクエストボディ
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/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']}")レスポンス
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}ステータスを確認
予測エンドポイントをポーリングして、リクエストの現在のステータスを確認します。
/api/v1/model/prediction/{prediction_id}ポーリング例
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)ステータス値
processingリクエストはまだ処理中です。completed生成が完了しました。出力が利用可能です。succeeded生成が成功しました。出力が利用可能です。failed生成に失敗しました。エラーフィールドを確認してください。完了レスポンス
{
"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"
}
}ファイルをアップロード
Atlas Cloud ストレージにファイルをアップロードし、API リクエストで使用できる URL を取得します。multipart/form-data を使用してアップロードします。
/api/v1/model/uploadMediaアップロード例
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}")レスポンス
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}入力 Schema
以下のパラメータがリクエストボディで使用できます。
利用可能なパラメータはありません。
リクエストボディの例
{
"model": "bytedance/seedream-v4/sequential"
}出力 Schema
API は生成された出力 URL を含む予測レスポンスを返します。
レスポンス例
{
"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
Atlas Cloud Skills は 300 以上の AI モデルを AI コーディングアシスタントに直接統合します。ワンコマンドでインストールし、自然言語で画像・動画生成や LLM との対話が可能です。
対応クライアント
インストール
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI キーの設定
Atlas Cloud ダッシュボードから API キーを取得し、環境変数として設定してください。
export ATLASCLOUD_API_KEY="your-api-key-here"機能
インストール後、AI アシスタントで自然言語を使用してすべての Atlas Cloud モデルにアクセスできます。
MCP Server
Atlas Cloud MCP Server は Model Context Protocol を通じて IDE と 300 以上の AI モデルを接続します。MCP 対応のあらゆるクライアントで動作します。
対応クライアント
インストール
npx -y atlascloud-mcp設定
以下の設定を IDE の MCP 設定ファイルに追加してください。
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}利用可能なツール
APIスキーマ
スキーマが利用できませんSeedance 1.5 Pro
ネイティブ音声・映像同期生成音と映像を、ワンテイクで完全同期
ByteDanceの革新的なAIモデル。単一の統合プロセスから完璧に同期した音声と映像を同時生成。8言語以上でミリ秒精度のリップシンクを実現する、真のネイティブ音声・映像生成を体験してください。
Model Highlights
Featuring five core capabilities: Precision Instruction Editing, High Feature Preservation, Deep Intent Understanding, Multi-Image I/O, and Ultra HD Resolution. Covering diverse creative scenarios, bringing every inspiration to life instantly with high quality.
Precision Instruction Editing
Simply describe your needs in plain language to accurately perform add, delete, modify, and replace operations. Enable applications across commercial design, artistic creation, and entertainment.
High Feature Preservation
Deep Intent Understanding
Multi-Image Input/Output
Input multiple images at once, supporting complex editing operations like combination, migration, replacement, and derivation, achieving high-difficulty synthesis
Ultra HD Resolution
Resolution upgraded again, supporting ultra-high-definition output for professional-grade image quality
最適な用途
Prompt Examples & Creative Templates
Discover the power of Seedream 4.0 with these carefully crafted prompt examples. Each template showcases specific capabilities and helps you achieve professional results.
Perspective & Composition Control
Transform camera angles, adjust scene distance, and modify aspect ratios with precisionChange the camera angle from eye-level to bird's-eye view, adjust the scene from close-up to medium shot, and convert the image aspect ratio to 16:9. Maintain all original elements and lighting while adapting the composition for the new perspective and format.
.png&w=3840&q=75)
Mathematical Whiteboard Creation
Generate clean whiteboard with precise mathematical formulas and equationsCreate a clean white whiteboard with the following mathematical equations written in clear, professional handwriting: E=mc², √(9)=3, and the quadratic formula (-b±√(b²-4ac))/2a. Use black or dark blue marker style, with proper spacing and mathematical notation.
.png&w=3840&q=75)
Sketch to Reality Transformation
Transform rough sketches into detailed realistic objects - bringing wild imagination to lifeBased on this rough sketch, generate a vintage television set from the 1950s-60s era. Transform the abstract lines and shapes into a realistic, detailed old-style TV with wooden cabinet, rounded screen, control knobs, and period-appropriate design elements. Make the vague concept concrete and lifelike.
.png&w=3840&q=75)
Lossless Detail Enhancement
Maximize original image detail retention, avoiding AI-generated artifacts for truly lossless editingEnhance this image while maximizing the preservation of original details. Avoid any AI-generated 'plastic' or 'oily' artifacts. Maintain authentic textures, natural lighting, and original image characteristics. Focus on clean, lossless enhancement that respects the source material's integrity.
.png&w=3840&q=75)
Creative Font Styling
Transform plain text into artistic, creative typography while maintaining readabilityTransform all the text in this image into creative, artistic fonts. Replace the standard typography with stylized lettering that matches the image's aesthetic - use decorative fonts, calligraphy styles, or artistic text treatments. Maintain the same text content and layout while making the typography more visually appealing and creative.
Core Capabilities
Advanced text understanding and image generation capabilities, supporting various artistic styles and professional requirements, from concept to final artwork in one step.
Natural language-based editing commands, supporting object addition/removal, style transfer, background replacement, and more complex editing operations.
Revolutionary multi-image input capability, enabling complex image synthesis, style migration, and creative combinations with unprecedented control.
Why Choose Seedream 4.0?
All-in-One Solution
Single model handles generation, editing, and composition - no need to switch between different toolsProfessional Quality
Commercial-grade output quality with precise control over every detailConsistent Style
Maintains character and style consistency across multiple generations and edits技術仕様
ネイティブ音声・映像生成を体験
Seedance 1.5 Proの画期的なテクノロジーで動画コンテンツ制作を革新している世界中の映画制作者、広告主、クリエイターの仲間入りをしてください。
Seedream 4: A next-generation multimodal image generation system developed by ByteDance Seed
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Seedream 4 |
| Developed by | ByteDance Seed Team |
| Release Date | September 9, 2025 |
| Model Type | Multimodal Image Generation |
| Related Links | Official Website, Technical Report (arXiv), GitHub Organization (ByteDance-Seed) |
Introduction
Seedream 4 is a powerful, efficient, and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single, integrated framework. Engineered for scalability and efficiency, the model introduces a novel diffusion transformer (DiT) architecture combined with a powerful Variational Autoencoder (VAE). This design enables the fast generation of native high-resolution images up to 4K, while significantly reducing computational requirements compared to its predecessors.
The primary goal of Seedream 4 is to extend traditional T2I systems into a more interactive and multidimensional creative tool. It is designed to handle complex tasks involving precise image editing, in-context reasoning, and multi-image referencing, pushing the boundaries of generative AI for both creative and professional applications.
Key Features & Innovations
Seedream 4 introduces several key advancements in image generation technology:
- Unified Multimodal Architecture: It integrates T2I generation, image editing, and multi-image composition into a single model, allowing for seamless transitions between different creative workflows.
- Efficient and Scalable Design: The model features a highly efficient DiT backbone and a high-compression VAE, achieving over 10x inference acceleration compared to Seedream 3.0 while delivering superior performance. This architecture is hardware-friendly and easily scalable.
- Ultra-Fast, High-Resolution Output: Seedream 4 can generate native high-resolution images (from 1K to 4K) in as little as 1.4 to 1.8 seconds for a 2K image, greatly enhancing user interaction and production efficiency.
- Advanced Multimodal Capabilities: The model excels at complex tasks such as precise, instruction-based image editing, in-context reasoning, and generating new images by blending elements from multiple reference images.
- Professional and Knowledge-Based Content Generation: Beyond artistic imagery, Seedream 4 can generate structured and knowledge-based content, including charts, mathematical formulas, and professional design materials, bridging the gap between creative expression and practical application.
- Advanced Training and Acceleration: The model is pre-trained on billions of text-image pairs and utilizes a multi-stage post-training process (CT, SFT, RLHF) to enhance its capabilities. Inference is accelerated through a combination of adversarial distillation, quantization, and speculative decoding.
Model Architecture & Technical Details
Seedream 4's architecture is a significant leap forward, focusing on efficiency and power. The core components are a diffusion transformer (DiT) and a Variational Autoencoder (VAE).
- Pre-training Data: Billions of text-image pairs, including a specialized pipeline for knowledge-related data like instructional images and formulas.
- Training Strategy: A multi-stage approach, starting at a 512x512 resolution and fine-tuning at higher resolutions up to 4K.
- Post-training: A joint multi-task process involving Continuing Training (CT), Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) to enhance instruction following and alignment.
- Inference Acceleration: A holistic system combining an adversarial learning framework, hardware-aware quantization (adaptive 4/8-bit), and speculative decoding.
Intended Use & Applications
Seedream 4 is designed for a wide range of creative and professional applications, moving beyond simple image generation to become a comprehensive visual content creation tool.
- Creative Content Generation: Creating high-quality, artistic images, illustrations, and concept art from text prompts.
- Advanced Image Editing: Performing complex edits on existing images using natural language instructions, such as adding or removing objects, changing styles, and modifying backgrounds.
- Design and Marketing: Generating professional design materials, product mockups, and marketing visuals with precise control over text and branding elements.
- Educational and Technical Content: Creating structured, knowledge-based visuals like diagrams, charts, and mathematical formulas for educational or technical documentation.
- Multi-Image Composition: Blending elements from multiple source images to create new compositions, such as virtual try-ons for fashion or combining characters with new scenes.
Performance
Seedream 4 has demonstrated state-of-the-art performance on both internal and public benchmarks as of September 18, often outperforming other leading models in text-to-image and image editing tasks.
MagicBench (Internal Benchmark)
| Task | Performance Summary |
|---|---|
| Text-to-Image | Achieved high scores in prompt following, aesthetics, and text-rendering. |
| Single-Image Editing | Showed a good balance between prompt following and alignment with the source image. |
