Open and Advanced Large-Scale Image Generative Models.
코드 예시
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()설치
사용하는 언어에 필요한 패키지를 설치하세요.
pip install requests인증
모든 API 요청에는 API 키를 통한 인증이 필요합니다. Atlas Cloud 대시보드에서 API 키를 받을 수 있습니다.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP 헤더
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}클라이언트 측 코드나 공개 저장소에 API 키를 노출하지 마세요. 대신 환경 변수 또는 백엔드 프록시를 사용하세요.
요청 제출
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())요청 제출
비동기 생성 요청을 제출합니다. API는 상태 확인 및 결과 조회에 사용할 수 있는 예측 ID를 반환합니다.
/api/v1/model/generateImage요청 본문
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']}")응답
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}상태 확인
예측 엔드포인트를 폴링하여 요청의 현재 상태를 확인합니다.
/api/v1/model/prediction/{prediction_id}폴링 예시
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)상태 값
processing요청이 아직 처리 중입니다.completed생성이 완료되었습니다. 출력을 사용할 수 있습니다.succeeded생성이 성공했습니다. 출력을 사용할 수 있습니다.failed생성에 실패했습니다. 오류 필드를 확인하세요.완료 응답
{
"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"
}
}파일 업로드
Atlas Cloud 스토리지에 파일을 업로드하고 API 요청에 사용할 수 있는 URL을 받습니다. multipart/form-data를 사용하여 업로드합니다.
/api/v1/model/uploadMedia업로드 예시
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}")응답
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}입력 Schema
다음 매개변수가 요청 본문에서 사용 가능합니다.
사용 가능한 매개변수가 없습니다.
요청 본문 예시
{
"model": "bytedance/seedream-v4/sequential"
}출력 Schema
API는 생성된 출력 URL이 포함된 예측 응답을 반환합니다.
응답 예시
{
"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 Skills는 300개 이상의 AI 모델을 AI 코딩 어시스턴트에 직접 통합합니다. 한 번의 명령으로 설치하고 자연어로 이미지, 동영상 생성 및 LLM과 대화할 수 있습니다.
지원 클라이언트
설치
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI 키 설정
Atlas Cloud 대시보드에서 API 키를 받아 환경 변수로 설정하세요.
export ATLASCLOUD_API_KEY="your-api-key-here"기능
설치 후 AI 어시스턴트에서 자연어를 사용하여 모든 Atlas Cloud 모델에 접근할 수 있습니다.
MCP Server
Atlas Cloud MCP Server는 Model Context Protocol을 통해 IDE와 300개 이상의 AI 모델을 연결합니다. MCP 호환 클라이언트에서 사용할 수 있습니다.
지원 클라이언트
설치
npx -y atlascloud-mcp설정
다음 설정을 IDE의 MCP 설정 파일에 추가하세요.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}사용 가능한 도구
API 스키마
스키마를 사용할 수 없음Seedance 1.5 Pro
네이티브 오디오-비주얼 동기화 생성사운드와 비전, 원테이크로 완벽 동기화
ByteDance의 혁신적인 AI 모델로 단일 통합 프로세스에서 완벽하게 동기화된 오디오와 비디오를 동시에 생성합니다. 8개 이상의 언어에서 밀리초 단위 정밀도의 립싱크를 제공하는 진정한 네이티브 오디오-비주얼 생성을 경험하세요.
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
완벽한 활용
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.
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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 edits기술 사양
네이티브 오디오-비주얼 생성 경험
Seedance 1.5 Pro의 획기적인 기술로 비디오 콘텐츠 제작을 혁신하고 있는 전 세계 영화 제작자, 광고주, 크리에이터들과 함께하세요.
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. |
