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
gambar-ke-gambar

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

INPUT

Memuat konfigurasi parameter...

OUTPUT

Menunggu
Gambar yang dihasilkan akan muncul di sini
Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Permintaan Anda akan dikenakan biaya $0.036 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 277 kali.

Berikut yang dapat Anda lakukan selanjutnya:

Parameter

Contoh kode

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()

Instalasi

Instal paket yang diperlukan untuk bahasa pemrograman Anda.

bash
pip install requests

Autentikasi

Semua permintaan API memerlukan autentikasi melalui API key. Anda bisa mendapatkan API key dari dasbor Atlas Cloud.

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}"
}
Jaga keamanan API key Anda

Jangan pernah mengekspos API key Anda di kode sisi klien atau repositori publik. Gunakan variabel lingkungan atau proxy backend sebagai gantinya.

Kirim permintaan

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())

Kirim Permintaan

Kirim permintaan pembuatan asinkron. API mengembalikan prediction ID yang dapat Anda gunakan untuk memeriksa status dan mengambil hasil.

POST/api/v1/model/generateImage

Isi Permintaan

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']}")

Respons

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

Periksa Status

Polling prediction endpoint untuk memeriksa status permintaan Anda saat ini.

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

Contoh 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)

Nilai Status

processingPermintaan masih diproses.
completedPembuatan selesai. Output tersedia.
succeededPembuatan berhasil. Output tersedia.
failedPembuatan gagal. Periksa field error.

Respons Selesai

{
  "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"
  }
}

Unggah File

Unggah file ke penyimpanan Atlas Cloud dan dapatkan URL yang dapat Anda gunakan dalam permintaan API Anda. Gunakan multipart/form-data untuk mengunggah.

POST/api/v1/model/uploadMedia

Contoh Unggah

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}")

Respons

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

Input Schema

Parameter berikut diterima di isi permintaan.

Total: 0Wajib: 0Opsional: 0

Tidak ada parameter yang tersedia.

Contoh Isi Permintaan

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

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

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

Contoh Respons

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 mengintegrasikan 300+ model AI langsung ke asisten pengkodean AI Anda. Satu perintah untuk menginstal, lalu gunakan bahasa alami untuk menghasilkan gambar, video, dan mengobrol dengan LLM.

Klien yang Didukung

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ klien yang didukung

Instalasi

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

Dapatkan API key dari dasbor Atlas Cloud dan atur sebagai variabel lingkungan.

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

Kemampuan

Setelah diinstal, Anda dapat menggunakan bahasa alami di asisten AI Anda untuk mengakses semua model Atlas Cloud.

Pembuatan GambarBuat gambar dengan model seperti Nano Banana 2, Z-Image, dan lainnya.
Pembuatan VideoBuat video dari teks atau gambar dengan Kling, Vidu, Veo, dll.
Obrolan LLMMengobrol dengan Qwen, DeepSeek, dan model bahasa besar lainnya.
Unggah MediaUnggah file lokal untuk pengeditan gambar dan alur kerja gambar-ke-video.

MCP Server

Atlas Cloud MCP Server menghubungkan IDE Anda dengan 300+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ klien yang didukung

Instalasi

bash
npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

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

Alat yang Tersedia

atlas_generate_imageBuat gambar dari prompt teks.
atlas_generate_videoBuat video dari teks atau gambar.
atlas_chatMengobrol dengan model bahasa besar.
atlas_list_modelsJelajahi 300+ model AI yang tersedia.
atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model otomatis.
atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Schema API

Schema tidak tersedia

Silakan masuk untuk melihat riwayat permintaan

Anda perlu masuk untuk mengakses riwayat permintaan model Anda.

Masuk
4.5NEW RELEASE

SeedreamSuara dan Gambar, Semua dalam Satu Pengambilan

Model AI revolusioner ByteDance yang menghasilkan audio dan video tersinkronisasi sempurna secara bersamaan dari satu proses terpadu. Rasakan generasi audio-visual asli sejati dengan sinkronisasi bibir presisi milidetik di lebih dari 8 bahasa.

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

Rasakan Generasi Audio-Visual Asli

Bergabunglah dengan pembuat film, pengiklan, dan kreator di seluruh dunia yang merevolusi pembuatan konten video dengan teknologi inovatif 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

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