bytedance/seedream-v5.0-lite/sequential

text-to-image

ByteDance next-generation image model with batch generation support. Generate up to 15 related images in a single request.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost 0.032 per run. For $10 you can run this model approximately 312 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parametri

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/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.

bash

pip install requests

Autenticazione

Tutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Header HTTP

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Proteggi la tua chiave API

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.

POST/api/v1/model/generateImage

Corpo 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/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.

GET/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.

POST/api/v1/model/uploadMedia

Esempio 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.

Totale: 0Obbligatorio: 0Opzionale: 0

Nessun parametro disponibile.

Esempio di corpo della richiesta

json

{
  "model": "bytedance/seedream-v5.0-lite/sequential"
}

Schema di output

L'API restituisce una risposta di previsione con gli URL degli output generati.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Esempio di risposta

json

{
  "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

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ client supportati

Installa

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Configura chiave API

Ottieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.

bash

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.

Generazione di immaginiGenera immagini con modelli come Nano Banana 2, Z-Image e altri.

Creazione di videoCrea video da testo o immagini con Kling, Vidu, Veo, ecc.

Chat LLMChatta con Qwen, DeepSeek e altri grandi modelli linguistici.

Caricamento mediaCarica file locali per la modifica di immagini e flussi di lavoro da immagine a video.

Scopri di più

github.com/AtlasCloudAI/atlas-cloud-skills

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

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ client supportati

Installa

bash

npx -y atlascloud-mcp

Configurazione

Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Strumenti disponibili

atlas_generate_imageGenera immagini da prompt testuali.

atlas_generate_videoCrea video da testo o immagini.

atlas_chatChatta con grandi modelli linguistici.

atlas_list_modelsEsplora oltre 300 modelli di IA disponibili.

atlas_quick_generateCreazione di contenuti in un solo passaggio con selezione automatica del modello.

atlas_upload_mediaCarica file locali per i flussi di lavoro API.

Scopri di più

github.com/AtlasCloudAI/mcp-server

API Schema

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1. 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	Google	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.

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