Seedream v4.5 Edit Sequential API by ByteDance
ByteDance advanced image editing model with batch generation support. Edit multiple images while preserving facial features and details.
इनपुट
आउटपुट
निष्क्रियआपके अनुरोध की लागत $0.036 प्रति रन होगी। $10 के साथ आप इस मॉडल को लगभग 277 बार चला सकते हैं।
आगे आप यह कर सकते हैं:
कोड उदाहरण
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-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()इंस्टॉल करें
अपनी प्रोग्रामिंग भाषा के लिए आवश्यक पैकेज इंस्टॉल करें।
pip install requestsप्रमाणीकरण
सभी API अनुरोधों के लिए API कुंजी के माध्यम से प्रमाणीकरण आवश्यक है। आप अपनी API कुंजी Atlas Cloud डैशबोर्ड से प्राप्त कर सकते हैं।
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP हेडर
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}क्लाइंट-साइड कोड या सार्वजनिक रिपॉज़िटरी में अपनी API कुंजी कभी उजागर न करें। इसके बजाय एनवायरनमेंट वेरिएबल या बैकएंड प्रॉक्सी का उपयोग करें।
अनुरोध सबमिट करें
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())अनुरोध सबमिट करें
एक असिंक्रोनस जनरेशन अनुरोध सबमिट करें। API एक प्रेडिक्शन ID लौटाता है जिसका उपयोग आप स्थिति जाँचने और परिणाम प्राप्त करने के लिए कर सकते हैं।
/api/v1/model/generateImageअनुरोध बॉडी
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-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']}")प्रतिक्रिया
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}स्थिति जाँचें
अपने अनुरोध की वर्तमान स्थिति जाँचने के लिए प्रेडिक्शन एंडपॉइंट को पोल करें।
/api/v1/model/prediction/{prediction_id}पोलिंग उदाहरण
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)स्थिति मान
processingअनुरोध अभी भी प्रोसेस हो रहा है।completedजनरेशन पूर्ण हो गया है। आउटपुट उपलब्ध हैं।succeededजनरेशन सफल रहा। आउटपुट उपलब्ध हैं।failedजनरेशन विफल हो गया। एरर फ़ील्ड जाँचें।पूर्ण प्रतिक्रिया
{
"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"
}
}फ़ाइलें अपलोड करें
Atlas Cloud स्टोरेज पर फ़ाइलें अपलोड करें और एक URL प्राप्त करें जिसका उपयोग आप अपने API अनुरोधों में कर सकते हैं। अपलोड के लिए multipart/form-data का उपयोग करें।
/api/v1/model/uploadMediaअपलोड उदाहरण
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}")प्रतिक्रिया
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
अनुरोध बॉडी में निम्नलिखित पैरामीटर स्वीकार किए जाते हैं।
कोई पैरामीटर उपलब्ध नहीं है।
अनुरोध बॉडी का उदाहरण
{
"model": "bytedance/seedream-v4.5/edit-sequential"
}Output Schema
API जनरेट किए गए आउटपुट URL के साथ एक प्रेडिक्शन प्रतिक्रिया लौटाता है।
प्रतिक्रिया का उदाहरण
{
"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+ AI मॉडल को सीधे आपके AI कोडिंग असिस्टेंट में इंटीग्रेट करता है। इंस्टॉल करने के लिए एक कमांड, फिर इमेज, वीडियो जनरेट करने और LLM के साथ चैट करने के लिए प्राकृतिक भाषा का उपयोग करें।
समर्थित क्लाइंट
इंस्टॉल करें
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI कुंजी सेटअप करें
Atlas Cloud डैशबोर्ड से अपनी API कुंजी प्राप्त करें और इसे एनवायरनमेंट वेरिएबल के रूप में सेट करें।
export ATLASCLOUD_API_KEY="your-api-key-here"क्षमताएँ
एक बार इंस्टॉल होने के बाद, आप सभी Atlas Cloud मॉडल तक पहुँचने के लिए अपने AI असिस्टेंट में प्राकृतिक भाषा का उपयोग कर सकते हैं।
MCP Server
Atlas Cloud MCP Server आपके IDE को Model Context Protocol के माध्यम से 300+ AI मॉडल से जोड़ता है। किसी भी MCP-संगत क्लाइंट के साथ काम करता है।
समर्थित क्लाइंट
इंस्टॉल करें
npx -y atlascloud-mcpकॉन्फ़िगरेशन
अपने IDE की MCP सेटिंग्स फ़ाइल में निम्नलिखित कॉन्फ़िगरेशन जोड़ें।
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}उपलब्ध टूल
API स्कीमा
स्कीमा उपलब्ध नहींअनुरोध इतिहास देखने के लिए कृपया साइन इन करें
अपने मॉडल अनुरोध इतिहास तक पहुंचने के लिए आपको साइन इन करना होगा।
साइन इन करेंSeedreamसाउंड और विज़न, सब कुछ एक ही टेक में
ByteDance का क्रांतिकारी AI मॉडल जो एक ही एकीकृत प्रक्रिया से पूरी तरह से सिंक्रनाइज़ ऑडियो और वीडियो को एक साथ जेनरेट करता है। 8+ भाषाओं में मिलीसेकंड-प्रिसिजन लिप-सिंक के साथ सच्चे नेटिव ऑडियो-विजुअल जेनरेशन का अनुभव करें।
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
नेटिव ऑडियो-विजुअल जेनरेशन का अनुभव करें
दुनिया भर के फिल्म निर्माताओं, विज्ञापनदाताओं और क्रिएटर्स के साथ शामिल हों जो Seedance 1.5 Pro की अभूतपूर्व तकनीक के साथ वीडियो कंटेंट क्रिएशन में क्रांति ला रहे हैं।
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 |
