Open and Advanced Large-Scale Image Generative Models.
Entrada
Salida
InactivoCada ejecución costará 0.024. Con $10 puedes ejecutar aproximadamente 416 veces.
Puedes continuar con:
Ejemplo de código
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()Instalar
Instala el paquete necesario para tu lenguaje de programación.
pip install requestsAutenticación
Todas las solicitudes de API requieren autenticación mediante una clave de API. Puedes obtener tu clave de API desde el panel de Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Encabezados HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Nunca expongas tu clave de API en código del lado del cliente ni en repositorios públicos. Usa variables de entorno o un proxy de backend en su lugar.
Enviar una solicitud
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())Enviar una solicitud
Envía una solicitud de generación asíncrona. La API devuelve un ID de predicción que puedes usar para verificar el estado y obtener el resultado.
/api/v1/model/generateImageCuerpo de la solicitud
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']}")Respuesta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Verificar estado
Consulta el endpoint de predicción para verificar el estado actual de tu solicitud.
/api/v1/model/prediction/{prediction_id}Ejemplo de polling
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)Valores de estado
processingLa solicitud aún se está procesando.completedLa generación está completa. Las salidas están disponibles.succeededLa generación fue exitosa. Las salidas están disponibles.failedLa generación falló. Verifica el campo de error.Respuesta completada
{
"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"
}
}Subir archivos
Sube archivos al almacenamiento de Atlas Cloud y obtén una URL que puedes usar en tus solicitudes de API. Usa multipart/form-data para subir.
/api/v1/model/uploadMediaEjemplo de carga
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}")Respuesta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema de entrada
Los siguientes parámetros se aceptan en el cuerpo de la solicitud.
No hay parámetros disponibles.
Ejemplo de cuerpo de solicitud
{
"model": "bytedance/seedream-v4/sequential"
}Schema de salida
La API devuelve una respuesta de predicción con las URL de salida generadas.
Ejemplo de respuesta
{
"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 integra más de 300 modelos de IA directamente en tu asistente de codificación con IA. Un solo comando para instalar y luego usa lenguaje natural para generar imágenes, videos y chatear con LLM.
Clientes compatibles
Instalar
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurar clave de API
Obtén tu clave de API desde el panel de Atlas Cloud y configúrala como variable de entorno.
export ATLASCLOUD_API_KEY="your-api-key-here"Funcionalidades
Una vez instalado, puedes usar lenguaje natural en tu asistente de IA para acceder a todos los modelos de Atlas Cloud.
MCP Server
Atlas Cloud MCP Server conecta tu IDE con más de 300 modelos de IA a través del Model Context Protocol. Funciona con cualquier cliente compatible con MCP.
Clientes compatibles
Instalar
npx -y atlascloud-mcpConfiguración
Agrega la siguiente configuración al archivo de configuración de MCP de tu IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Herramientas disponibles
API Schema
Schema no disponiblePor favor inicia sesión para ver el historial de solicitudes
Necesitas iniciar sesión para acceder al historial de solicitudes del modelo.
Iniciar SesiónSeedance 1.5 Pro
GENERACIÓN NATIVA AUDIO-VISUALSonido y Visión, Todo en Una Sola Toma
El revolucionario modelo de IA de ByteDance que genera audio y video perfectamente sincronizados simultáneamente desde un único proceso unificado. Experimenta la verdadera generación nativa audio-visual con sincronización labial de precisión milimétrica en más de 8 idiomas.
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
Perfecto Para
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.
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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.
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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 editsEspecificaciones Técnicas
Experimenta la Generación Nativa Audio-Visual
Únete a cineastas, anunciantes y creadores de todo el mundo que están revolucionando la creación de contenido de video con la tecnología innovadora de Seedance 1.5 Pro.
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. |
