ByteDance next-generation image editing model with batch generation support. Edit multiple images while preserving facial features and details.
INPUT
OUTPUT
IdleYour request will cost 0.032 per run. For $10 you can run this model approximately 312 times.
Here's what you can do next:
Esempio di codice
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-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()Installa
Installa il pacchetto richiesto per il tuo linguaggio.
pip install requestsAutenticazione
Tutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Header HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Non esporre mai la tua chiave API nel codice lato client o nei repository pubblici. Utilizza invece variabili d'ambiente o un proxy backend.
Invia una richiesta
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())Invia una richiesta
Invia una richiesta di generazione asincrona. L'API restituisce un ID di previsione che puoi usare per controllare lo stato e recuperare il risultato.
/api/v1/model/generateImageCorpo della richiesta
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-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']}")Risposta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Controlla lo stato
Interroga l'endpoint di previsione per verificare lo stato attuale della tua richiesta.
/api/v1/model/prediction/{prediction_id}Esempio di 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)Valori di stato
processingLa richiesta è ancora in fase di elaborazione.completedLa generazione è completata. I risultati sono disponibili.succeededLa generazione è riuscita. I risultati sono disponibili.failedLa generazione è fallita. Controlla il campo errore.Risposta completata
{
"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"
}
}Carica file
Carica file nello storage Atlas Cloud e ottieni un URL utilizzabile nelle tue richieste API. Usa multipart/form-data per il caricamento.
/api/v1/model/uploadMediaEsempio di caricamento
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}")Risposta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema di input
I seguenti parametri sono accettati nel corpo della richiesta.
Nessun parametro disponibile.
Esempio di corpo della richiesta
{
"model": "bytedance/seedream-v5.0-lite/edit-sequential"
}Schema di output
L'API restituisce una risposta di previsione con gli URL degli output generati.
Esempio di risposta
{
"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 oltre 300 modelli di IA direttamente nel tuo assistente di codifica IA. Un comando per installare, poi usa il linguaggio naturale per generare immagini, video e chattare con LLM.
Client supportati
Installa
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigura chiave API
Ottieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Funzionalità
Una volta installato, puoi usare il linguaggio naturale nel tuo assistente IA per accedere a tutti i modelli Atlas Cloud.
Server MCP
Il server MCP di Atlas Cloud collega il tuo IDE con oltre 300 modelli di IA tramite il Model Context Protocol. Funziona con qualsiasi client compatibile MCP.
Client supportati
Installa
npx -y atlascloud-mcpConfigurazione
Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Strumenti disponibili
API Schema
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Log In1. 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.
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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
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E-Commerce Product Imaging: Generates detailed, high-resolution images for product packaging and promotional content, ensuring clarity and realism suited for online retail platforms.
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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.
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Office and Educational Materials: Creates clear diagrams, layouts, and infographics for training, presentations, and instructional design requiring logical structure and accuracy.
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Creative Design and UI Prototyping: Assists in generating UI components, infographics, and conceptual visuals for design prototyping and ideation processes with iterative conversational refinement.
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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.
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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.
