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

text-to-image

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.

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GPT Image 2.5 Sunburst Text-to-Image is developed by OpenAI. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Quality

Size

Aspect ratio locked: changing one side scales the other proportionally. Click the lock to edit freely.

Background

Output format

Number

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost ≈$0.003 per run. For $10 you can run this model approximately 3333 times.This model is billed by tokens: text and reference-image inputs plus the generated image are all metered per token. The amount above is an estimate for your current settings — larger sizes, higher quality levels, and more reference images use more tokens and cost more.

Here's what you can do next:

Image-to-Video Image-to-Image

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": "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()

Install

Install the required package for your language.

pip install requests

Authentication

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}"
}

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": "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']}")

Response

{
  "code": 200,
  "data": {
    "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: 8Required: 2Optional: 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

Example Request Body

{
  "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

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

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.

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 400+ 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

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.

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.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ 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

npx -y atlascloud-mcp

Configuration

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

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

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-Friendly Prompt Template

# 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.