ByteDance next-generation image editing model that preserves facial features, lighting, and color tones while enabling professional-quality modifications.
Entrada
Salida
InactivoCada ejecución costará 0.032. Con $10 puedes ejecutar aproximadamente 312 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-v5.0-lite/edit",
"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-v5.0-lite/edit",
"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-v5.0-lite/edit"
}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 Sesión1. Introduction
Seedream 5.0 Lite is an advanced multimodal image generation model developed by ByteDance, released in February 2026. Designed to enable intelligent visual content creation, it integrates deep reasoning and up-to-date contextual awareness to produce high-resolution, semantically accurate images optimized for diverse practical workflows. Seedream 5.0 Lite represents a significant progression in AI-powered image generation through its incorporation of Chain of Thought (CoT) mechanisms and real-time web search capabilities.
This model’s significance lies in its capacity to perform complex multi-step visual reasoning and spatial logic, enhancing adherence to detailed prompts beyond typical static-image generation models. By coupling real-time external knowledge retrieval with sophisticated reasoning pipelines, Seedream 5.0 Lite delivers contextually relevant and conceptually rich images. These innovations position the model at the forefront of AI visual content frameworks targeting both creative and commercial use cases (ByteDance Seed; AIBase News).
2. Key Features & Innovations
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Chain of Thought Visual Reasoning: Implements multi-step inference processes to interpret and synthesize visual elements, enabling complex spatial relationships and logical consistency across generated images. This CoT mechanism improves prompt fidelity and nuanced image understanding.
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Real-time Web Search Integration: Incorporates live data retrieval from web sources at generation time, allowing images to reflect current trends, events, and up-to-date factual information. This dynamic context infusion distinguishes Seedream 5.0 Lite from models relying exclusively on static training corpora.
-
High-Resolution Rapid Generation: Supports native 2K and 4K image outputs with a generation speed of approximately 2 to 3 seconds per image, facilitating large-scale, high-quality imaging tasks with minimal latency.
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Multi-Round Conversational Editing: Enables iterative refinement of images through dialogue-based interactions, supporting up to 14 reference images for complex compositional adjustments in a conversational workflow.
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Competitive Performance and Cost Efficiency: Demonstrates superior logical accuracy and infographic generation capabilities relative to Google’s Nano Banana Pro, while maintaining lower operational costs and faster execution. This balance of quality and efficiency makes it well-suited for professional deployment.
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Extensive Multilingual and Text Rendering Support: Excels in generating marketing and promotional materials with clear, multilingual text embedding and precise typography, enhancing usability across global markets.
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Integration with Major Creative Platforms: Embedded within ByteDance’s CapCut and Jianying applications, allowing seamless API access and facilitating commercial and creative pipeline scalability across diverse industries.
3. Model Architecture & Technical Details
Seedream 5.0 Lite builds upon a multimodal transformer-based architecture optimized for image synthesis and visual reasoning. Its core architecture combines advanced vision encoders and autoregressive or diffusion-based decoders tailored for high-fidelity image generation at multiple resolutions.
Training leveraged extensive, diverse datasets inclusive of annotated images, diagrams, infographics, and textual metadata to support visual reasoning capabilities. The training pipeline underwent staged resolution scaling—from lower to higher (2K and 4K)—improving detail and accuracy progressively. Specialized training techniques, including Chain of Thought supervision, promoted multi-step reasoning within generated outputs.
Real-time web search functionality is integrated through a dedicated retrieval pipeline linking external data queries to the generation process, enabling dynamic conditioning beyond fixed datasets.
Post-training fine-tuning likely involved supervised fine-tuning (SFT) with carefully curated pairs and reinforcement learning from human feedback (RLHF) to enhance prompt adherence, compositional logic, and user interaction responsiveness, though exact methodologies remain proprietary.
4. Performance Highlights
Seedream 5.0 Lite exhibits substantial improvements over its predecessor (v4.5) and strong positioning among contemporary models:
| Rank | Model | Developer | Score/Metric | Release Date |
|---|---|---|---|---|
| 1 | Seedream 5.0 Lite | ByteDance | High Elo scores in MagicBench (office learning, knowledge reasoning, portrait tasks); 2–3s per 4K image | Feb 2026 |
| 2 | Nano Banana Pro | Slight edge in cinematic image polish; strong logical accuracy | 2025 | |
| 3 | Midjourney | Independent | Superior artistic aesthetics; slower generation speeds | Ongoing |
| 4 | Stable Diffusion | Stability AI | Highly customizable and open source flexibility | Ongoing |
Evaluations on MagicBench and MagicArena platforms reveal Seedream 5.0 Lite’s dominance in office and educational image clarity, reasoning complexity, and prompt fidelity. Its operational throughput is at least 25–40% faster than comparable high-resolution competitors, with lower compute costs.
Qualitatively, it balances the strengths of specialized infographics and logical content generation seen in Nano Banana Pro with faster real-world workflow integration, surpassing many artistic-oriented models in practical commercial settings (SourceForge; Storyboard18).
5. Intended Use & Applications
-
E-Commerce Product Imaging: Generates detailed, high-resolution images for product packaging and promotional content, ensuring clarity and realism suited for online retail platforms.
-
Marketing and Advertising Content: Produces complex marketing visuals with multilingual text elements and perfectly rendered typography, supporting dynamic campaign creation with up-to-date topical relevance.
-
Office and Educational Materials: Creates clear diagrams, layouts, and infographics for training, presentations, and instructional design requiring logical structure and accuracy.
-
Creative Design and UI Prototyping: Assists in generating UI components, infographics, and conceptual visuals for design prototyping and ideation processes with iterative conversational refinement.
-
Large-Scale Commercial Workflows: Integrated APIs and platform embeddings within CapCut and Jianying enable scalable image generation pipelines for media, entertainment, and content creation enterprises.
-
Real-Time Trend-Responsive Content: Leverages web search-enabled dynamic data to produce visuals that reflect current events and trending topics, valuable for news media and social content platforms.
