bytedance/seedream-v4.5

ByteDance latest image generation model achieving all-round improvements. Excels at typography, poster design, and brand visual creation with superior prompt adherence.

TEXT-TO-IMAGEHOTNEW
Seedream v4.5
texte-vers-image

ByteDance latest image generation model achieving all-round improvements. Excels at typography, poster design, and brand visual creation with superior prompt adherence.

Entrée

Chargement de la configuration des paramètres...

Sortie

Inactif
Les images générées apparaîtront ici
Configurez vos paramètres et cliquez sur exécuter pour commencer

Votre requête coûtera $0.036 par exécution. Avec $10, vous pouvez exécuter ce modèle environ 277 fois.

Vous pouvez continuer avec :

Paramètres

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-v4.5",
    "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.

bash
pip install requests

Authentification

Toutes les requêtes API nécessitent une authentification via une clé API. Vous pouvez obtenir votre clé API depuis le tableau de bord Atlas Cloud.

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

En-têtes HTTP

python
import os

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

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.

POST/api/v1/model/generateImage

Corps 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-v4.5",
    "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.

GET/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.

POST/api/v1/model/uploadMedia

Exemple 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.

Total: 0Requis: 0Optionnel: 0

Aucun paramètre disponible.

Exemple de corps de requête

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

Schema de sortie

L'API renvoie une réponse de prédiction avec les URL des résultats générés.

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

Exemple de réponse

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 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

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ clients pris en charge

Installer

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Configurer la clé API

Obtenez votre clé API depuis le tableau de bord Atlas Cloud et définissez-la comme variable d'environnement.

bash
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.

Génération d'imagesGénérez des images avec des modèles comme Nano Banana 2, Z-Image, et plus encore.
Création de vidéosCréez des vidéos à partir de texte ou d'images avec Kling, Vidu, Veo, etc.
Chat LLMDiscutez avec Qwen, DeepSeek et d'autres grands modèles de langage.
Téléchargement de médiasTéléchargez des fichiers locaux pour l'édition d'images et les workflows image-vers-vidéo.

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

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ clients pris en charge

Installer

bash
npx -y atlascloud-mcp

Configuration

Ajoutez la configuration suivante au fichier de paramètres MCP de votre IDE.

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

Outils disponibles

atlas_generate_imageGénérez des images à partir de prompts textuels.
atlas_generate_videoCréez des vidéos à partir de texte ou d'images.
atlas_chatDiscutez avec de grands modèles de langage.
atlas_list_modelsParcourez plus de 300 modèles d'IA disponibles.
atlas_quick_generateCréation de contenu en une étape avec sélection automatique du modèle.
atlas_upload_mediaTéléchargez des fichiers locaux pour les workflows API.

Schéma API

Schéma non disponible

Veuillez vous connecter pour voir l'historique des requêtes

Vous devez vous connecter pour accéder à l'historique de vos requêtes de modèle.

Se Connecter
4.5NEW RELEASE

SeedreamSon et Image, Tout en Une Seule Prise

Le modèle d'IA révolutionnaire de ByteDance qui génère simultanément de l'audio et de la vidéo parfaitement synchronisés à partir d'un processus unifié unique. Découvrez la véritable génération audio-visuelle native avec une synchronisation labiale d'une précision milliseconde dans plus de 8 langues.

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

Découvrez la Génération Audio-Visuelle Native

Rejoignez les cinéastes, annonceurs et créateurs du monde entier qui révolutionnent la création de contenu vidéo avec la technologie révolutionnaire de Seedance 1.5 Pro.

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

Commencez avec Plus de 300 Modèles,

Explorer tous les modèles