ByteDance next-generation image model with batch generation support. Generate up to 15 related images in a single request.
Entrada
Saída
InativoCada execução custará 0.032. Com $10 você pode executar aproximadamente 312 vezes.
Você pode continuar com:
Exemplo 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/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
Instale o pacote necessário para a sua linguagem de programação.
pip install requestsAutenticação
Todas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Cabeçalhos HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de backend.
Enviar uma solicitação
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 uma solicitação
Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.
/api/v1/model/generateImageCorpo da solicitação
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/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']}")Resposta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Verificar status
Consulte o endpoint de predição para verificar o status atual da sua solicitação.
/api/v1/model/prediction/{prediction_id}Exemplo 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 status
processingA solicitação ainda está sendo processada.completedA geração está completa. As saídas estão disponíveis.succeededA geração foi bem-sucedida. As saídas estão disponíveis.failedA geração falhou. Verifique o campo de erro.Resposta concluída
{
"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"
}
}Enviar arquivos
Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.
/api/v1/model/uploadMediaExemplo de upload
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}")Resposta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema de entrada
Os seguintes parâmetros são aceitos no corpo da solicitação.
Nenhum parâmetro disponível.
Exemplo de corpo da solicitação
{
"model": "bytedance/seedream-v5.0-lite/sequential"
}Schema de saída
A API retorna uma resposta de predição com as URL de saída geradas.
Exemplo de resposta
{
"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
O Atlas Cloud Skills integra mais de 300 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.
Clientes compatíveis
Instalar
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurar chave de API
Obtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Funcionalidades
Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.
MCP Server
O Atlas Cloud MCP Server conecta seu IDE com mais de 300 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.
Clientes compatíveis
Instalar
npx -y atlascloud-mcpConfiguração
Adicione a seguinte configuração ao arquivo de configuração de MCP do seu IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Ferramentas disponíveis
API Schema
Schema não disponívelFaça login para ver o histórico de solicitações
Você precisa fazer login para acessar o histórico de solicitações do modelo.
Fazer Login1. 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
-
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.
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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.
