bytedance/seedream-v4.5/sequential

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

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Seedream v4.5 Sequential
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ByteDance latest image generation model with batch generation support. Generate up to 15 images in a single request.

Inmatning

Laddar parameterkonfiguration...

Utmatning

Vilande
Dina genererade bilder visas här
Konfigurera parametrar och klicka på Kör för att börja generera

Varje körning kostar 0.036. För $10 kan du köra cirka 277 gånger.

Du kan fortsätta med:

Parametrar

Kodexempel

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-v4.5/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()

Installera

Installera det nödvändiga paketet för ditt programmeringsspråk.

bash
pip install requests

Autentisering

Alla API-förfrågningar kräver autentisering via en API key. Du kan hämta din API key från Atlas Cloud-instrumentpanelen.

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

HTTP Headers

python
import os

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

Exponera aldrig din API key i klientkod eller publika arkiv. Använd miljövariabler eller en backend-proxy istället.

Skicka en förfrågan

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())

Skicka en förfrågan

Skicka en asynkron genereringsförfrågan. API:et returnerar ett prediction ID som du kan använda för att kontrollera statusen och hämta resultatet.

POST/api/v1/model/generateImage

Förfrågningsinnehåll

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-v4.5/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']}")

Svar

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Kontrollera status

Polla prediction-endpointen för att kontrollera den aktuella statusen för din förfrågan.

GET/api/v1/model/prediction/{prediction_id}

Polling-exempel

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)

Statusvärden

processingFörfrågan bearbetas fortfarande.
completedGenereringen är klar. Utdata är tillgängliga.
succeededGenereringen lyckades. Utdata är tillgängliga.
failedGenereringen misslyckades. Kontrollera error-fältet.

Slutfört svar

{
  "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"
  }
}

Ladda upp filer

Ladda upp filer till Atlas Cloud-lagring och få en URL som du kan använda i dina API-förfrågningar. Använd multipart/form-data för uppladdning.

POST/api/v1/model/uploadMedia

Uppladdningsexempel

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}")

Svar

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Följande parametrar accepteras i förfrågningsinnehållet.

Totalt: 0Obligatorisk: 0Valfri: 0

Inga parametrar tillgängliga.

Exempel på förfrågningsinnehåll

json
{
  "model": "bytedance/seedream-v4.5/sequential"
}

Output Schema

API:et returnerar ett prediction-svar med de genererade utdata-URL:erna.

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

Exempelsvar

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 integrerar 300+ AI-modeller direkt i din AI-kodassistent. Ett kommando för att installera, sedan använd naturligt språk för att generera bilder, videor och chatta med LLM.

Stödda klienter

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ stödda klienter

Installera

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Konfigurera API Key

Hämta din API key från Atlas Cloud-instrumentpanelen och ställ in den som en miljövariabel.

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

Funktioner

När det är installerat kan du använda naturligt språk i din AI-assistent för att komma åt alla Atlas Cloud-modeller.

BildgenereringGenerera bilder med modeller som Nano Banana 2, Z-Image och fler.
VideoskapandeSkapa videor från text eller bilder med Kling, Vidu, Veo m.fl.
LLM-chattChatta med Qwen, DeepSeek och andra stora språkmodeller.
MediauppladdningLadda upp lokala filer för bildredigering och bild-till-video-arbetsflöden.

MCP Server

Atlas Cloud MCP Server ansluter din IDE med 300+ AI-modeller via Model Context Protocol. Fungerar med alla MCP-kompatibla klienter.

Stödda klienter

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ stödda klienter

Installera

bash
npx -y atlascloud-mcp

Konfiguration

Lägg till följande konfiguration i din IDE:s MCP-inställningsfil.

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

Tillgängliga verktyg

atlas_generate_imageGenerera bilder från textpromptar.
atlas_generate_videoSkapa videor från text eller bilder.
atlas_chatChatta med stora språkmodeller.
atlas_list_modelsBläddra bland 300+ tillgängliga AI-modeller.
atlas_quick_generateInnehållsskapande i ett steg med automatiskt modellval.
atlas_upload_mediaLadda upp lokala filer för API-arbetsflöden.

API Schema

Schema ej tillgängligt

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4.5NEW RELEASE

SeedreamLjud och Bild, Allt i Ett Tag

ByteDances revolutionerande AI-modell som genererar perfekt synkroniserat ljud och video samtidigt från en enda enhetlig process. Upplev äkta nativ ljud-visuell generering med millisekundprecis läppsynk över 8+ språk.

Key Updates

Experience the next level of AI-powered visual creation

Superior Aesthetics

Produces cinematic visuals with refined lighting and rendering for professional-grade output.

Higher Consistency

Maintains stable subjects, clear details, and coherent scenes across multiple images.

Smarter Instruction Following

Accurately responds to complex prompts with precise visual control and interactive editing.

Stronger Spatial Understanding

Generates realistic proportions, object placement, and scene layout with accuracy.

Richer World Knowledge

Creates knowledge-based visuals with accurate scientific and technical reasoning.

Deeper Industry Application

Supports professional workflows for e-commerce, film, advertising, gaming, and more.

Industry Applications

🛒

E-commerce

Product photography & marketing

🎬

Film & TV

Concept art & storyboarding

📺

Advertising

Campaign visuals & creatives

🎮

Gaming

Character & environment design

📚

Education

Instructional illustrations

🏠

Interior Design

Space visualization

🏗️

Architecture

Architectural rendering

👗

Fashion

Virtual try-on & styling

Improvements from 4.0

See how Seedream 4.5 outperforms the previous version

1

Face Quality

Significant improvement when face proportion is small

Before (4.0)Distorted facial features in distant shots
After (4.5)Clear, natural facial details preserved
2

Text Rendering

Enhanced small character rendering capability

Before (4.0)Blurry or incorrect text generation
After (4.5)Sharp, accurate text placement
3

ID Preservation

Stronger identity retention ability

Before (4.0)Character features drift across generations
After (4.5)Consistent identity across all outputs

Upplev Nativ Ljud-Visuell Generering

Gå med filmskapare, annonsörer och kreatörer över hela världen som revolutionerar videoskapande med Seedance 1.5 Pro:s banbrytande teknologi.

Cinematic Quality
Fast Generation
🎯Precise Control

Seedream 4.5 : A professional, high-fidelity multimodal image generation model by ByteDance Seed

Model Card Overview

FieldDescription
Model NameSeedream 4.5
Developed ByByteDance Seed
Release DateDecember 2025
Model TypeMultimodal Image Generation
Related LinksOfficial Website,Technical Paper (arXiv), GitHub Repository

Introduction

Seedream 4.5 is a state-of-the-art, multimodal generative model engineered for scalability, efficiency, and professional-grade output. As an advanced version of Seedream 4.0, it is built upon a unified framework that seamlessly integrates text-to-image synthesis, sophisticated image editing, and complex multi-image composition. The model's primary design goal is to deliver professional visual creatives with exceptional consistency and fidelity. This is achieved through a significant scaling of the model architecture and training data, which enhances its ability to preserve reference details, render dense text and typography accurately, and understand nuanced user instructions.

Key Features & Innovations

  • Unified Multimodal Framework: Integrates text-to-image (T2I), single-image editing, and multi-image composition into a single, cohesive model, allowing for diverse and flexible creative workflows.
  • High-Fidelity & High-Resolution Generation: Capable of generating native high-resolution images (up to 4K), capturing fine details, realistic textures, and accurate lighting for professional use cases.
  • Advanced Image Editing: Excels at preserving the core structure, lighting, and color tone of reference images while applying precise edits based on natural language instructions.
  • Enhanced Multi-Image Composition: Accurately identifies and blends main subjects from multiple reference images, enabling complex creative compositions and style fusions.
  • Superior Typography and Text Rendering: Features significantly improved capabilities for rendering clear, legible, and contextually integrated text within images.
  • Efficient and Scalable Architecture: Built on a highly efficient Diffusion Transformer (DiT) and a powerful Variational Autoencoder (VAE), enabling fast inference and effective scalability.
  • Optimized for Professional Use: Demonstrates strong performance in generating structured, knowledge-based content such as design materials, posters, and product visualizations, bridging the gap between creative generation and practical industry applications.

Model Architecture & Technical Details

Seedream 4.5's architecture is an extension of the foundation laid by Seedream 4.0. The core of the model is a highly efficient and scalable Diffusion Transformer (DiT), which significantly increases model capacity while reducing computational requirements for training and inference. This is paired with a powerful Variational Autoencoder (VAE) with a high compression ratio, which minimizes the number of image tokens processed in the latent space, further boosting efficiency.

Training and Data: The model was pre-trained on billions of text-image pairs, covering a vast range of taxonomies and knowledge-centric concepts. Training was conducted in multiple stages, starting at a 512x512 resolution and fine-tuning at progressively higher resolutions up to 4K. The post-training phase is extensive, incorporating Continuing Training (CT) for foundational knowledge, Supervised Fine-Tuning (SFT) for artistic quality, and Reinforcement Learning from Human Feedback (RLHF) to align outputs with human preferences. A sophisticated Prompt Engineering (PE) module, built upon the Seed1.5-VL vision-language model, is used to process user inputs and enhance instruction following.

Intended Use & Applications

Seedream 4.5 is designed for professional creators and applications demanding high-quality, consistent, and controllable image generation. Its intended uses include:

  • Professional Content Creation: Generating cinematic-quality visuals for digital advertising, social media, and print.
  • Advanced Photo Editing: Performing complex edits, such as changing clothing materials, modifying backgrounds, or adjusting lighting, while maintaining subject integrity.
  • E-commerce and Product Visualization: Creating high-quality product showcases and marketing materials.
  • Graphic Design: Designing posters, key visuals, and other materials that require the integration of stylized text and typography.
  • Creative Storytelling: Producing sequential, thematically related images for storyboards or visual narratives.

Performance

Seedream 4.5 and its predecessor, Seedream 4.0, have demonstrated top-tier performance on public benchmarks. The models are evaluated on the Artificial Analysis Arena, a real-time competitive leaderboard that ranks models based on blind user votes.

Text-to-Image Leaderboard (December 2025)

RankModelDeveloperELO ScoreRelease Date
1GPT Image 1.5 (high)OpenAI1,252Dec 2025
2Nano Banana ProGoogle1,223Nov 2025
5Seedream 4.0ByteDance Seed1,193Sept 2025
7Seedream 4.5ByteDance Seed1,169Dec 2025

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