openai/gpt-image-2.5-sunburst/text-to-image

文生圖

GPT Image 2.5 Sunburst Text-to-Image API by OpenAI

openai/gpt-image-2.5-sunburst/text-to-image

Text-to-image

GPT Image 2.5 Sunburst generates images from natural-language prompts with arbitrary resolutions up to 3840x2160, five quality tiers including xhigh and max, and first-class transparent backgrounds. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

模型對比

GPT Image 2.5 Sunburst Text-to-Image 由 OpenAI 開發。Atlas Cloud（由 Atlas Cloud AI LLC 營運）僅提供接取服務，並不擁有該模型。所有商標均歸其各自所有者所有。

輸入

提示詞 *

品質

尺寸

長寬比已鎖定：修改一邊時另一邊會等比縮放。點擊鎖頭圖示可自由編輯。

Background

輸出格式

數量

輸出

閒置

生成的圖片將在這裡顯示

設定參數後點擊執行開始生成

每次執行將花費 ≈$0.003。$10 可執行約 3333 次。此模型按 token 計費：輸入文字、參考圖以及生成的圖片都按 token 計量。上方金額是依目前參數的預估值——尺寸越大、品質等級越高、參考圖越多，消耗的 token 越多，實際扣費也越高。

你可以繼續：

圖生影片圖生圖

參數

程式碼範例
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": "openai/gpt-image-2.5-sunburst/text-to-image",  # Required. Model name. options: openai/gpt-image-2.5-sunburst/text-to-image
    "prompt": "A cute baby sea otter floating on its back in golden evening light.",  # Required. Text description of the desired image
    "quality": "medium",  # Rendering quality tier. options: auto | low | medium | high | xhigh | max
    "size": "1024x1024",  # optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images
    "background": "auto",  # Background of the generated image. options: auto | opaque | transparent
    "output_format": "png",  # Output image format. options: png | jpeg | webp
    "moderation": "auto",  # Content-moderation strictness. options: auto | low
    "n": 1,  # Number of images to generate, 1 to 10. (min: 1, max: 10)
}

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()

安裝

安裝所需的相依套件。

pip install requests

驗證

所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。

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

HTTP 標頭

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

保護好您的 API Key

切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使用環境變數或後端代理。

提交請求

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())

提交請求

提交一個非同步生成請求。API 會傳回一個 prediction ID，您可以用它來檢查狀態與取得結果。

POST/api/v1/model/generateImage

請求主體

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "openai/gpt-image-2.5-sunburst/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

回應

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

檢查狀態

輪詢 prediction 端點以檢查請求的當前狀態。

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

輪詢範例

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)

狀態值

processing請求仍在處理中。

completed生成完成，輸出可用。

succeeded生成成功，輸出可用。

failed生成失敗，請檢查 error 欄位。

完成回應

{
  "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"
  }
}

上傳檔案

將檔案上傳到 Atlas Cloud 儲存空間，取得可在 API 請求中使用的 URL。使用 multipart/form-data 上傳。

POST/api/v1/model/uploadMedia

上傳範例

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}")

回應

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

Input Schema

以下參數在請求主體中被接受。

總計: 8必填: 2選填: 6

modelstringrequired

Model name.

Default: "openai/gpt-image-2.5-sunburst/text-to-image"

openai/gpt-image-2.5-sunburst/text-to-image

promptstringrequired

Text description of the desired image. Up to 32000 characters.

Default: "A cute baby sea otter floating on its back in golden evening light."

qualitystring

Rendering quality tier. gpt-image-2.5 adds xhigh and max on top of low/medium/high. Higher tiers cost more per image.

Default: "medium"

autolowmediumhighxhighmax

sizestring

optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.

Default: "1024x1024"

auto1024x10241024x768768x10241024x15361536x10242048x20482048x11521152x20482560x10881088x25602880x21602160x28803840x21602160x3840

backgroundstring

Background of the generated image. When using transparent, set the output format to png or webp.

Default: "auto"

autoopaquetransparent

output_formatstring

Output image format.

Default: "png"

pngjpegwebp

moderationstring

Content-moderation strictness. low applies less restrictive filtering.

Default: "auto"

autolow

ninteger

Number of images to generate, 1 to 10. Each image is billed separately.

Default: 1Min: 1Max: 10

範例請求主體

{
  "model": "openai/gpt-image-2.5-sunburst/text-to-image",
  "prompt": "A cute baby sea otter floating on its back in golden evening light.",
  "quality": "medium",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "moderation": "auto",
  "n": 1
}

Output Schema

API 傳回包含生成輸出 URL 的 prediction 回應。

idstring

Unique identifier for the prediction.

modelstring

Model ID used for the prediction.

statusstring

Status of the task: created, processing, completed, or failed.

outputsarray

Array of URLs to the generated images. Empty when status is not completed.

created_atstring

ISO timestamp of when the request was created.

範例回應

{
  "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 將 400+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API Key

從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。

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

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案用於圖片編輯和圖生影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 400+ AI 模型連接。支援任何相容 MCP 的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

npx -y atlascloud-mcp

設定

將以下設定新增到您的 IDE 的 MCP 設定檔中。

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

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 400+ 可用 AI 模型。

atlas_quick_generate一步式內容創建，自動選擇最佳模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "GPT Image 2.5 Sunburst text-to-image (OpenAI Images API)."
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "openai/gpt-image-2.5-sunburst/text-to-image",
            "enum": [
              "openai/gpt-image-2.5-sunburst/text-to-image"
            ]
          },
          "prompt": {
            "type": "string",
            "maxLength": 32000,
            "default": "A cute baby sea otter floating on its back in golden evening light.",
            "description": "Text description of the desired image. Up to 32000 characters."
          },
          "quality": {
            "type": "string",
            "default": "medium",
            "enum": [
              "auto",
              "low",
              "medium",
              "high",
              "xhigh",
              "max"
            ],
            "description": "Rendering quality tier. gpt-image-2.5 adds xhigh and max on top of low/medium/high. Higher tiers cost more per image.",
            "x-ui-component": "select"
          },
          "size": {
            "default": "1024x1024",
            "description": "optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.",
            "enum": [
              "auto",
              "1024x1024",
              "1024x768",
              "768x1024",
              "1024x1536",
              "1536x1024",
              "2048x2048",
              "2048x1152",
              "1152x2048",
              "2560x1088",
              "1088x2560",
              "2880x2160",
              "2160x2880",
              "3840x2160",
              "2160x3840"
            ],
            "type": "string"
          },
          "background": {
            "type": "string",
            "default": "auto",
            "enum": [
              "auto",
              "opaque",
              "transparent"
            ],
            "description": "Background of the generated image. When using transparent, set the output format to png or webp.",
            "x-ui-component": "select"
          },
          "output_format": {
            "type": "string",
            "default": "png",
            "enum": [
              "png",
              "jpeg",
              "webp"
            ],
            "description": "Output image format.",
            "x-ui-component": "select"
          },
          "moderation": {
            "type": "string",
            "default": "auto",
            "enum": [
              "auto",
              "low"
            ],
            "description": "Content-moderation strictness. low applies less restrictive filtering.",
            "x-ui-component": "select"
          },
          "n": {
            "type": "integer",
            "minimum": 1,
            "maximum": 10,
            "default": 1,
            "description": "Number of images to generate, 1 to 10. Each image is billed separately."
          }
        },
        "x-order-properties": [
          "model",
          "prompt",
          "quality",
          "size",
          "background",
          "output_format",
          "n"
        ],
        "allOf": [
          {
            "if": {
              "properties": {
                "background": {
                  "const": "transparent"
                }
              },
              "required": [
                "background"
              ]
            },
            "then": {
              "properties": {
                "output_format": {
                  "enum": [
                    "png",
                    "webp"
                  ]
                }
              }
            }
          }
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique identifier for the prediction."
          },
          "urls": {
            "type": "object",
            "description": "Object containing related API endpoints."
          },
          "model": {
            "type": "string",
            "description": "Model ID used for the prediction."
          },
          "status": {
            "type": "string",
            "description": "Status of the task: created, processing, completed, or failed."
          },
          "outputs": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Array of URLs to the generated images. Empty when status is not completed."
          },
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp of when the request was created."
          }
        }
      }
    }
  }
}

LLM 友善的提示詞範本

# openai/gpt-image-2.5-sunburst/text-to-image

> GPT Image 2.5 Sunburst generates images from natural-language prompts with arbitrary resolutions up to 3840x2160, five quality tiers including xhigh and max, and first-class transparent backgrounds. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `openai/gpt-image-2.5-sunburst/text-to-image`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"openai/gpt-image-2.5-sunburst/text-to-image"`
  - Options: "openai/gpt-image-2.5-sunburst/text-to-image"

- **`prompt`** (`string`, _required_):
  Text description of the desired image. Up to 32000 characters.
  - Default: `"A cute baby sea otter floating on its back in golden evening light."`

- **`quality`** (`string`, _optional_):
  Rendering quality tier. gpt-image-2.5 adds xhigh and max on top of low/medium/high. Higher tiers cost more per image.
  - Default: `"medium"`
  - Options: "auto", "low", "medium", "high", "xhigh", "max"

- **`size`** (`string`, _optional_):
  optional string or auto or 1024x1024 or 1536x1024 or 5 moreThe size ofthe generated images.For gpt-image-2.5 is supported as WIDTHxHEIGHT strings,for example 1536x864 . Width and height mustboth be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1.Resolutions above2560x1440 are experimental, and the maximum supported resolution is 3840x2160 .The requesteosize must also satisfy the model's current pixel and edge limits.The standard sizes 1024x1024.1536x1024 ,and 1024x1536 are supported by the GPT image models; auto is supported for modelsthat allow automatic sizing.For dall-e-2,use oneof 256x256,512x512,or 1024x1024.Fordall-e-3,use oneof 1024x1024,1792x1024,or 1024x1792.
  - Default: `"1024x1024"`
  - Options: "auto", "1024x1024", "1024x768", "768x1024", "1024x1536", "1536x1024", "2048x2048", "2048x1152", "1152x2048", "2560x1088", "1088x2560", "2880x2160", "2160x2880", "3840x2160", "2160x3840"

- **`background`** (`string`, _optional_):
  Background of the generated image. When using transparent, set the output format to png or webp.
  - Default: `"auto"`
  - Options: "auto", "opaque", "transparent"

- **`output_format`** (`string`, _optional_):
  Output image format.
  - Default: `"png"`
  - Options: "png", "jpeg", "webp"

- **`n`** (`integer`, _optional_):
  Number of images to generate, 1 to 10. Each image is billed separately.
  - Default: `1`
  - Min: 1
  - Max: 10



**Required Parameters Example**:

```json
{
  "model": "openai/gpt-image-2.5-sunburst/text-to-image",
  "prompt": "A cute baby sea otter floating on its back in golden evening light."
}
```


**Full Example**:

```json
{
  "model": "openai/gpt-image-2.5-sunburst/text-to-image",
  "prompt": "A cute baby sea otter floating on its back in golden evening light.",
  "quality": "medium",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "n": 1
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated images. Empty when status is not completed.

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "openai/gpt-image-2.5-sunburst/text-to-image",
  "prompt": "A cute baby sea otter floating on its back in golden evening light.",
  "quality": "medium",
  "size": "1024x1024",
  "background": "auto",
  "output_format": "png",
  "n": 1
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/openai/gpt-image-2.5-sunburst/text-to-image)

Create a cinematic 24-shot storyboard sequence for a Japanese cyberpunk anime feature film. The story follows a young motorcycle rider and his companions through a vast futuristic megacity, escalating from an ordinary night ride into a mysterious supernatural crisis and an explosive confrontation. Maintain strict visual continuity across all 24 shots: same characters, same costumes, same motorcycles, same futuristic city, consistent architecture, lighting, proportions, and color palette. 1980s–1990s Japanese sci-fi animation aesthetic, hand-drawn cel animation feeling, detailed mechanical design, dense futuristic urban environments, expressive character acting, dramatic perspective, cinematic composition, strong shadows, atmospheric lighting, subtle film grain, dynamic motion, dystopian atmosphere, highly detailed background art, widescreen cinematic framing. SHOT 01 — Establishing Shot A gigantic futuristic metropolis at night, endless skyscrapers, elevated highways, glowing advertisements, dense traffic, distant industrial towers, rain-soaked streets, tiny humans beneath overwhelming architecture, establishing the immense scale of the city. SHOT 02 — Empty Highway A deserted elevated highway cutting through the city at night, streetlights disappearing into the distance, several futuristic motorcycles approaching from far away, reflections shimmering across wet asphalt. SHOT 03 — Motorcycle Riders Three young riders speeding through the neon-lit city, aggressive motorcycle silhouettes, headlights cutting through the darkness, dramatic low-angle tracking shot, intense sense of speed. SHOT 04 — Close-Up Extreme close-up of the lead rider's face beneath a motorcycle helmet, reflected city lights moving rapidly across his visor, tense expression, wind and rain striking his face. SHOT 05 — High-Speed Turn The motorcycles make a sharp turn beneath a massive elevated highway, tires sliding across wet pavement, sparks and water spraying into the air, dynamic Dutch angle. SHOT 06 — Strange Encounter A mysterious child-like figure appears unexpectedly in the middle of the road, surrounded by an unnatural glow, the riders suddenly braking, headlights illuminating the figure. SHOT 07 — Collision Avoidance The lead motorcycle swerves violently to avoid the figure, almost crashing into a concrete barrier, extreme perspective, motion blur, chaotic composition. SHOT 08 — Aftermath The riders stop in an abandoned industrial district, engines steaming in the cold night air, the mysterious figure standing silently in the distance, ominous atmosphere. SHOT 09 — Government Facility A colossal futuristic government research facility surrounded by armed security vehicles, helicopters hovering overhead, massive concrete architecture, cold fluorescent lighting. SHOT 10 — Underground Laboratory A vast underground laboratory filled with mysterious machinery, cables, glowing monitors and enormous scientific equipment, scientists moving through the darkness. SHOT 11 — The Experiment A mysterious energy chamber containing a small human figure, powerful electromagnetic distortion surrounding the chamber, scientists observing from behind reinforced glass. SHOT 12 — Psychic Phenomenon The laboratory suddenly experiences a supernatural energy surge, objects floating in midair, shattered glass suspended around the room, scientists reacting in fear, intense visual distortion. SHOT 13 — Citywide Blackout Aerial view of the futuristic city suddenly losing power, entire districts falling into darkness while isolated neon signs flicker, massive storm clouds gathering overhead. SHOT 14 — Military Convoy An armored military convoy racing through the city streets, tanks and helicopters moving between enormous skyscrapers, civilians fleeing through the rain. SHOT 15 — Rooftop The lead character stands alone on the roof of a skyscraper overlooking the city, motorcycle beside him, enormous skyline behind him, distant explosions illuminating the clouds. SHOT 16 — Psychological Vision Surreal sequence inside the protagonist's mind, distorted city architecture bending into impossible shapes, fragmented memories, floating objects, intense psychological tension. SHOT 17 — Destruction Begins A massive invisible force tears through the city, buildings collapsing in sequence, vehicles thrown into the air, enormous clouds of dust and debris filling the streets. SHOT 18 — Human Scale A tiny group of characters running through a gigantic avenue while skyscrapers collapse around them, extreme scale contrast, overwhelming sense of danger. SHOT 19 — Neon Rain A quiet emotional moment after the destruction, one character standing alone beneath heavy rain and flickering neon lights, abandoned vehicles scattered across the street. SHOT 20 — The Colossal Presence A gigantic supernatural humanoid silhouette emerging through clouds of dust above the ruined city, barely visible against the skyline, characters looking upward in awe and terror. SHOT 21 — Final Confrontation The protagonist faces the enormous supernatural force in the middle of a devastated city, motorcycle headlights illuminating the foreground, monumental scale contrast, intense cinematic composition. SHOT 22 — Energy Explosion A gigantic sphere of energy erupts across the city, concentric shockwaves expanding through skyscrapers, debris and light filling the frame, spectacular hand-drawn animation effects. SHOT 23 — Silence After Chaos The destruction suddenly falls silent, an enormous ruined city beneath a pale dawn sky, smoke drifting between buildings, the protagonist standing motionless among the ruins. SHOT 24 — Final Wide Shot A vast futuristic metropolis at sunrise, damaged but still alive, distant figures walking through the ruins, the protagonist's motorcycle resting in the foreground, soft morning light breaking through the clouds, ambiguous but hopeful ending. Overall visual direction: classic Japanese cyberpunk anime cinema, hand-drawn cel animation aesthetic, intricate mechanical details, futuristic motorcycles, brutalist megastructures, dense urban environments, neon reflections, dramatic perspective, expressive faces, atmospheric depth, cinematic lighting, dynamic action choreography, psychological science fiction, dystopian urban atmosphere, epic scale, coherent character design, consistent visual continuity, widescreen 16:9 storyboard composition.

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