bytedance/seedream-v4/sequential

文生圖

Open and Advanced Large-Scale Image Generative Models.

輸入

正在載入參數設定...

輸出

閒置

生成的圖片將在這裡顯示

設定參數後點擊執行開始生成

每次執行將花費 0.024。$10 可執行約 416 次。

你可以繼續：

圖生影片圖生圖

參數

程式碼範例
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()

安裝

為您的程式語言安裝所需的套件。

bash

pip install requests

驗證

所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP 標頭

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

請妥善保管您的 API 金鑰

切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改用環境變數或後端代理。

提交請求

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())

提交請求

提交非同步生成請求。API 會傳回一個預測 ID，您可以用它來檢查狀態並取得結果。

POST/api/v1/model/generateImage

請求主體

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']}")

回應

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

檢查狀態

輪詢預測端點以檢查請求的當前狀態。

GET/api/v1/model/prediction/{prediction_id}

輪詢範例

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)

狀態值

processing請求仍在處理中。

completed生成完成。輸出已可取得。

succeeded生成成功。輸出已可取得。

failed生成失敗。請檢查錯誤欄位。

完成回應

{
  "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"
  }
}

上傳檔案

上傳檔案至 Atlas Cloud 儲存空間並取得 URL，可用於您的 API 請求。使用 multipart/form-data 上傳。

POST/api/v1/model/uploadMedia

上傳範例

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}")

回應

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

輸入 Schema

以下參數可在請求主體中使用。

總計: 0必填: 0選填: 0

無可用參數。

範例請求主體

json

{
  "model": "bytedance/seedream-v4/sequential"
}

輸出 Schema

API 傳回包含生成輸出 URL 的預測回應。

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

範例回應

json

{
  "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 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝，即可使用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API 金鑰

從 Atlas Cloud 儀表板取得 API 金鑰，並設為環境變數。

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案，用於圖片編輯和圖片轉影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

bash

npx -y atlascloud-mcp

設定

將以下設定新增至您 IDE 的 MCP 設定檔中。

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 300 多個可用的 AI 模型。

atlas_quick_generate一步完成內容創建，自動選擇模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

Schema 不可用

請登入以檢視請求歷史

您需要登入才能存取模型請求歷史記錄。

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Seedance 1.5 Pro

原生音視頻同步生成

聲畫合一，一鏡到底

ByteDance 開創性的 AI 模型，通過單一統一流程同步生成完美同步的音頻和視頻。支持 8 種以上語言，實現毫秒級精準唇音同步的真正原生音視頻生成體驗。

模型亮點

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

Character Consistency:Highly maintains character features across different creation styles (illustration/3D/photography), keeping creation always controllable

Scene Preservation:Maximizes original image details, no worry about "AI oily" feel after editing, achieving lossless editing

Deep Intent Understanding

Knowledge Upgrade:Expert-level knowledge base, taking text understanding to the next level

Inspiration Materialization:From abstract to concrete, turning "wild" inspirations into reality

Predictive Reasoning:Stronger reasoning capabilities, simulating predictions across time and space, making the unseen visible

Adaptive Ratio:When enabled, automatically matches the best aspect ratio for your image

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

完美適用於

🎨

Commercial Design

🖼️

Artistic Creation

📸

Photo Editing

🎮

Game Assets

👤

Character Design

🏗️

Architecture Visualization

📱

Social Media

🎬

Film & Animation

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.

Precision Editing

Perspective & Composition Control

Transform camera angles, adjust scene distance, and modify aspect ratios with precision

Prompt Template

Change 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.

Text & Formula Generation

Mathematical Whiteboard Creation

Generate clean whiteboard with precise mathematical formulas and equations

Prompt Template

Create 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.

Deep Intent Understanding

Sketch to Reality Transformation

Transform rough sketches into detailed realistic objects - bringing wild imagination to life

Prompt Template

Based 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.

High Feature Preservation

Lossless Detail Enhancement

Maximize original image detail retention, avoiding AI-generated artifacts for truly lossless editing

Prompt Template

Enhance 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.

Text Transformation

Creative Font Styling

Transform plain text into artistic, creative typography while maintaining readability

Prompt Template

Transform 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.

Professional templates designed for Seedream 4.0 • Unlock your creative potential with AI-powered image generation

核心功能

Generation

Text-to-Image Creation

Advanced text understanding and image generation capabilities, supporting various artistic styles and professional requirements, from concept to final artwork in one step.

Editing

Intelligent Image Editing

Natural language-based editing commands, supporting object addition/removal, style transfer, background replacement, and more complex editing operations.

Synthesis

Multi-Image Composition

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 tools

🎯

Professional Quality

Commercial-grade output quality with precise control over every detail

🔄

Consistent Style

Maintains character and style consistency across multiple generations and edits

技術規格

Model Architecture:ByteDance Doubao AI Powered

Core Features:Generation + Editing Integration

Resolution Support:Ultra HD Output

Input Support:Text, Single/Multi-Image

Output Formats:PNG, JPEG, WebP

API Integration:RESTful API with SDK Support

體驗原生音視頻生成

加入全球電影製作人、廣告商和創作者的行列，使用 Seedance 1.5 Pro 的突破性技術革新視頻內容創作。

✨Professional Tools

⚡Lightning Fast

🌐All-in-One Platform

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.

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