ByteDance next-generation image editing model that preserves facial features, lighting, and color tones while enabling professional-quality modifications.
Entrée
Sortie
InactifVotre requête coûtera 0.032 par exécution. Avec $10, vous pouvez exécuter ce modèle environ 312 fois.
Vous pouvez continuer avec :
Exemple de code
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()Installer
Installez le package requis pour votre langage.
pip install requestsAuthentification
Toutes les requêtes API nécessitent une authentification via une clé API. Vous pouvez obtenir votre clé API depuis le tableau de bord Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"En-têtes HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}N'exposez jamais votre clé API dans du code côté client ou dans des dépôts publics. Utilisez plutôt des variables d'environnement ou un proxy backend.
Soumettre une requête
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())Soumettre une requête
Soumettez une requête de génération asynchrone. L'API renvoie un identifiant de prédiction que vous pouvez utiliser pour vérifier le statut et récupérer le résultat.
/api/v1/model/generateImageCorps de la requête
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']}")Réponse
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Vérifier le statut
Interrogez le point de terminaison de prédiction pour vérifier le statut actuel de votre requête.
/api/v1/model/prediction/{prediction_id}Exemple d'interrogation
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)Valeurs de statut
processingLa requête est encore en cours de traitement.completedLa génération est terminée. Les résultats sont disponibles.succeededLa génération a réussi. Les résultats sont disponibles.failedLa génération a échoué. Vérifiez le champ d'erreur.Réponse terminée
{
"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"
}
}Télécharger des fichiers
Téléchargez des fichiers vers le stockage Atlas Cloud et obtenez une URL utilisable dans vos requêtes API. Utilisez multipart/form-data pour le téléchargement.
/api/v1/model/uploadMediaExemple de téléchargement
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}")Réponse
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema d'entrée
Les paramètres suivants sont acceptés dans le corps de la requête.
Aucun paramètre disponible.
Exemple de corps de requête
{
"model": "bytedance/seedream-v5.0-lite/edit"
}Schema de sortie
L'API renvoie une réponse de prédiction avec les URL des résultats générés.
Exemple de réponse
{
"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 intègre plus de 300 modèles d'IA directement dans votre assistant de codage IA. Une seule commande pour installer, puis utilisez le langage naturel pour générer des images, des vidéos et discuter avec des LLM.
Clients pris en charge
Installer
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurer la clé API
Obtenez votre clé API depuis le tableau de bord Atlas Cloud et définissez-la comme variable d'environnement.
export ATLASCLOUD_API_KEY="your-api-key-here"Fonctionnalités
Une fois installé, vous pouvez utiliser le langage naturel dans votre assistant IA pour accéder à tous les modèles Atlas Cloud.
Serveur MCP
Le serveur MCP Atlas Cloud connecte votre IDE avec plus de 300 modèles d'IA via le Model Context Protocol. Compatible avec tout client compatible MCP.
Clients pris en charge
Installer
npx -y atlascloud-mcpConfiguration
Ajoutez la configuration suivante au fichier de paramètres MCP de votre IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Outils disponibles
Schéma API
Schéma non disponibleVeuillez vous connecter pour voir l'historique des requêtes
Vous devez vous connecter pour accéder à l'historique de vos requêtes de modèle.
Se Connecter1. 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.
-
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.
-
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.
-
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.
-
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.
-
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.
