Seedream v4.5 Edit API by ByteDance
ByteDance advanced image editing model that preserves facial features, lighting, and color tones while enabling professional-quality modifications.
INPUT
OUTPUT
IdleYour request will cost $0.036 per run. For $10 you can run this model approximately 277 times.
Here's what you can do next:
Code Example
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()Install
Install the required package for your language.
pip install requestsAuthentication
All API requests require authentication via an API key. You can get your API key from the 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}"
}Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.
Submit a request
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())Submit a Request
Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.
/api/v1/model/generateImageRequest Body
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']}")Response
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Check Status
Poll the prediction endpoint to check the current status of your request.
/api/v1/model/prediction/{prediction_id}Polling Example
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)Status Values
processingThe request is still being processed.completedGeneration is complete. Outputs are available.succeededGeneration succeeded. Outputs are available.failedGeneration failed. Check the error field.Completed Response
{
"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"
}
}Upload Files
Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.
/api/v1/model/uploadMediaUpload Example
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}")Response
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
The following parameters are accepted in the request body.
No parameters available.
Example Request Body
{
"model": "bytedance/seedream-v4.5/edit"
}Output Schema
The API returns a prediction response with the generated output URLs.
Example Response
{
"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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.
Supported Clients
Install
npx skills add AtlasCloudAI/atlas-cloud-skillsSetup API Key
Get your API key from the Atlas Cloud dashboard and set it as an environment variable.
export ATLASCLOUD_API_KEY="your-api-key-here"Capabilities
Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.
MCP Server
Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.
Supported Clients
Install
npx -y atlascloud-mcpConfiguration
Add the following configuration to your IDE's MCP settings file.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
API Schema
Schema not availableNo examples available
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Log InSeedreamNext-Gen Visual Creation
ByteDance's most advanced image generation model with superior aesthetics, higher consistency, and smarter instruction following capabilities.
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
Ready to Create?
Experience the power of Seedream 4.5 and transform your creative workflow.
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 |
