bytedance/seedream-v4.5/sequential

ByteDance latest image generation model with batch generation support. Generate up to 15 images in a single request.

TEXT-TO-IMAGEHOTNEW
Seedream v4.5 Sequential
text-to-image

ByteDance latest image generation model with batch generation support. Generate up to 15 images in a single request.

INPUT

Loading parameter configuration...

OUTPUT

Idle
Your generated images will appear here
Configure your settings and click Run to get started

Your request will cost $0.036 per run. For $10 you can run this model approximately 277 times.

Here's what you can do next:

Parameters

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/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()

Install

Install the required package for your language.

bash
pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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}"
}
Keep your API key secure

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.

POST/api/v1/model/generateImage

Request 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/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']}")

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.

GET/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.

POST/api/v1/model/uploadMedia

Upload 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.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

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

Output Schema

The API returns a prediction response with the generated output URLs.

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

Example Response

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 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

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ supported clients

Install

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

bash
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.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.
Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.
LLM ChatChat with Qwen, DeepSeek, and other large language models.
Media UploadUpload local files for image editing and image-to-video workflows.

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

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ supported clients

Install

bash
npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

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

Available Tools

atlas_generate_imageGenerate images from text prompts.
atlas_generate_videoCreate videos from text or images.
atlas_chatChat with large language models.
atlas_list_modelsBrowse 300+ available AI models.
atlas_quick_generateOne-step content creation with auto model selection.
atlas_upload_mediaUpload local files for API workflows.

API Schema

Schema not available

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4.5NEW RELEASE

SeedreamNext-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

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

Ready to Create?

Experience the power of Seedream 4.5 and transform your creative workflow.

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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