Seedream v4.5 Sequential API by ByteDance
ByteDance latest image generation model with batch generation support. Generate up to 15 images in a single request.
Invoer
Uitvoer
InactiefElke uitvoering kost $0.036. Voor $10 kunt u ongeveer 277 keer uitvoeren.
U kunt doorgaan met:
Codevoorbeeld
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()Installeren
Installeer het vereiste pakket voor uw programmeertaal.
pip install requestsAuthenticatie
Alle API-verzoeken vereisen authenticatie via een API-sleutel. U kunt uw API-sleutel ophalen via het Atlas Cloud dashboard.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP-headers
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Stel uw API-sleutel nooit bloot in client-side code of openbare repositories. Gebruik in plaats daarvan omgevingsvariabelen of een backend-proxy.
Een verzoek indienen
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())Een verzoek indienen
Dien een asynchroon generatieverzoek in. De API retourneert een voorspellings-ID waarmee u de status kunt controleren en het resultaat kunt ophalen.
/api/v1/model/generateImageVerzoekinhoud
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']}")Antwoord
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Status controleren
Bevraag het voorspellings-eindpunt om de huidige status van uw verzoek te controleren.
/api/v1/model/prediction/{prediction_id}Polling-voorbeeld
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)Statuswaarden
processingHet verzoek wordt nog verwerkt.completedDe generatie is voltooid. Resultaten zijn beschikbaar.succeededDe generatie is geslaagd. Resultaten zijn beschikbaar.failedDe generatie is mislukt. Controleer het foutveld.Voltooid antwoord
{
"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"
}
}Bestanden uploaden
Upload bestanden naar Atlas Cloud opslag en ontvang een URL die u kunt gebruiken in uw API-verzoeken. Gebruik multipart/form-data om te uploaden.
/api/v1/model/uploadMediaUpload-voorbeeld
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}")Antwoord
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Invoer-Schema
De volgende parameters worden geaccepteerd in de verzoekinhoud.
Geen parameters beschikbaar.
Voorbeeld verzoekinhoud
{
"model": "bytedance/seedream-v4.5/sequential"
}Uitvoer-Schema
De API retourneert een voorspellingsantwoord met de gegenereerde uitvoer-URL's.
Voorbeeldantwoord
{
"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 integreert meer dan 300 AI-modellen rechtstreeks in uw AI-codeerassistent. Eén commando om te installeren, gebruik daarna natuurlijke taal om afbeeldingen, video's te genereren en te chatten met LLMs.
Ondersteunde clients
Installeren
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI-sleutel instellen
Haal uw API-sleutel op via het Atlas Cloud dashboard en stel deze in als omgevingsvariabele.
export ATLASCLOUD_API_KEY="your-api-key-here"Mogelijkheden
Eenmaal geïnstalleerd kunt u natuurlijke taal gebruiken in uw AI-assistent om toegang te krijgen tot alle Atlas Cloud modellen.
MCP-server
De Atlas Cloud MCP-server verbindt uw IDE met meer dan 300 AI-modellen via het Model Context Protocol. Werkt met elke MCP-compatibele client.
Ondersteunde clients
Installeren
npx -y atlascloud-mcpConfiguratie
Voeg de volgende configuratie toe aan het MCP-instellingenbestand van uw IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Beschikbare tools
API Schema
Schema niet beschikbaarInloggen om aanvraaggeschiedenis te bekijken
U moet ingelogd zijn om toegang te krijgen tot uw modelaanvraaggeschiedenis.
InloggenSeedreamGeluid en Beeld, Alles in Één Opname
ByteDance's revolutionaire AI-model dat perfect gesynchroniseerde audio en video simultaan genereert vanuit één uniform proces. Ervaar echte native audio-visuele generatie met millisecondennauwkeurige lipsynchronisatie in meer dan 8 talen.
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
Face Quality
Significant improvement when face proportion is small
Text Rendering
Enhanced small character rendering capability
ID Preservation
Stronger identity retention ability
Ervaar Native Audio-Visuele Generatie
Sluit u aan bij filmmakers, adverteerders en creators wereldwijd die videocontent creatie revolutioneren met de baanbrekende technologie van Seedance 1.5 Pro.
Seedream 4.5 : A professional, high-fidelity multimodal image generation model by ByteDance Seed
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Seedream 4.5 |
| Developed By | ByteDance Seed |
| Release Date | December 2025 |
| Model Type | Multimodal Image Generation |
| Related Links | Official 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)
| Rank | Model | Developer | ELO Score | Release Date |
|---|---|---|---|---|
| 1 | GPT Image 1.5 (high) | OpenAI | 1,252 | Dec 2025 |
| 2 | Nano Banana Pro | 1,223 | Nov 2025 | |
| 5 | Seedream 4.0 | ByteDance Seed | 1,193 | Sept 2025 |
| 7 | Seedream 4.5 | ByteDance Seed | 1,169 | Dec 2025 |
