bytedance/seedream-v4.5/edit

ByteDance advanced image editing model that preserves facial features, lighting, and color tones while enabling professional-quality modifications.

IMAGE-TO-IMAGEHOTNEW
Seedream v4.5 Edit
Görüntü-Görüntü

ByteDance advanced image editing model that preserves facial features, lighting, and color tones while enabling professional-quality modifications.

Girdi

Parametre yapılandırması yükleniyor...

Çıktı

Boşta
Oluşturulan görüntüleriniz burada görünecek
Parametreleri yapılandırın ve oluşturmaya başlamak için Çalıştır'a tıklayın

Her çalıştırma $0.036 maliyete sahip. 10$ ile yaklaşık 277 kez çalıştırabilirsiniz.

Parametreler

Kod örneği

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/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()

Kurulum

Programlama diliniz için gerekli paketi kurun.

bash
pip install requests

Kimlik Doğrulama

Tüm API istekleri, API anahtarı ile kimlik doğrulama gerektirir. API anahtarınızı Atlas Cloud kontrol panelinden alabilirsiniz.

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

HTTP Başlıkları

python
import os

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

API anahtarınızı asla istemci tarafı kodunda veya herkese açık depolarda ifşa etmeyin. Bunun yerine ortam değişkenleri veya arka uç proxy kullanın.

İstek gönder

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())

İstek Gönder

Asenkron bir oluşturma isteği gönderin. API, durumu kontrol etmek ve sonucu almak için kullanabileceğiniz bir tahmin ID'si döndürür.

POST/api/v1/model/generateImage

İstek Gövdesi

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/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']}")

Yanıt

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

Durumu Kontrol Et

İsteğinizin mevcut durumunu kontrol etmek için tahmin uç noktasını sorgulayın.

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

Sorgulama Örneği

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)

Durum Değerleri

processingİstek hâlâ işleniyor.
completedOluşturma tamamlandı. Çıktılar kullanılabilir.
succeededOluşturma başarılı oldu. Çıktılar kullanılabilir.
failedOluşturma başarısız oldu. Hata alanını kontrol edin.

Tamamlanmış Yanıt

{
  "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"
  }
}

Dosya Yükle

Dosyaları Atlas Cloud depolama alanına yükleyin ve API isteklerinizde kullanabileceğiniz bir URL alın. Yüklemek için multipart/form-data kullanın.

POST/api/v1/model/uploadMedia

Yükleme Örneği

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}")

Yanıt

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

Input Schema

İstek gövdesinde aşağıdaki parametreler kabul edilir.

Toplam: 0Zorunlu: 0İsteğe Bağlı: 0

Kullanılabilir parametre yok.

Örnek İstek Gövdesi

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

Output Schema

API, oluşturulan çıktı URL'lerini içeren bir tahmin yanıtı döndürür.

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

Örnek Yanıt

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, 300'den fazla AI modelini doğrudan AI kodlama asistanınıza entegre eder. Kurmak için tek bir komut, ardından görüntü, video oluşturmak ve LLM ile sohbet etmek için doğal dil kullanın.

Desteklenen İstemciler

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Gemini CLI
Cursor
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VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ desteklenen i̇stemciler

Kurulum

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

API Anahtarını Ayarla

API anahtarınızı Atlas Cloud kontrol panelinden alın ve ortam değişkeni olarak ayarlayın.

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

Yetenekler

Kurulduktan sonra, tüm Atlas Cloud modellerine erişmek için AI asistanınızda doğal dil kullanabilirsiniz.

Görüntü OluşturmaNano Banana 2, Z-Image ve daha fazla model ile görüntüler oluşturun.
Video OluşturmaKling, Vidu, Veo vb. ile metin veya görüntülerden videolar oluşturun.
LLM SohbetQwen, DeepSeek ve diğer büyük dil modelleri ile sohbet edin.
Medya YüklemeGörüntü düzenleme ve görüntüden videoya iş akışları için yerel dosyaları yükleyin.

MCP Server

Atlas Cloud MCP Server, IDE'nizi Model Context Protocol aracılığıyla 300'den fazla AI modeline bağlar. Herhangi bir MCP uyumlu istemci ile çalışır.

Desteklenen İstemciler

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ desteklenen i̇stemciler

Kurulum

bash
npx -y atlascloud-mcp

Yapılandırma

Aşağıdaki yapılandırmayı IDE'nizin MCP ayarları dosyasına ekleyin.

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

Mevcut Araçlar

atlas_generate_imageMetin istemlerinden görüntüler oluşturun.
atlas_generate_videoMetin veya görüntülerden videolar oluşturun.
atlas_chatBüyük dil modelleri ile sohbet edin.
atlas_list_models300'den fazla mevcut AI modelini keşfedin.
atlas_quick_generateOtomatik model seçimi ile tek adımda içerik oluşturma.
atlas_upload_mediaAPI iş akışları için yerel dosyaları yükleyin.

API Şeması

Şema mevcut değil

İstek geçmişini görüntülemek için oturum açın

Model istek geçmişinize erişmek için oturum açmanız gerekir.

Oturum Aç
4.5NEW RELEASE

SeedreamSes ve Görüntü, Tek Çekimde Hepsi

ByteDance'in tek birleşik süreçten eş zamanlı olarak mükemmel senkronize ses ve video üreten devrim niteliğindeki yapay zeka modeli. 8'den fazla dilde milisaniye hassasiyetinde dudak senkronizasyonu ile gerçek yerli ses-görsel üretimi deneyimleyin.

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

Yerli Ses-Görsel Üretimi Deneyimleyin

Seedance 1.5 Pro'nun çığır açan teknolojisi ile video içerik oluşturmayı devrimleştiren dünya çapındaki film yapımcılarına, reklamverenlere ve içerik üreticilerine katılın.

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

300+ Model ile Başlayın,

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