# AI Storyboard & Scene Image Generator (`dami_studio/ai-storyboard-generator`) Actor Turn a story or script into a sequence of consistent AI images — one per scene. Give it a story (it splits into scenes) or your own scene list, a shared art style, and optional character notes. For faceless videos, comics, ads, pitches, and animatics. - **URL**: https://apify.com/dami\_studio/ai-storyboard-generator.md - **Developed by:** [Dami's Studio](https://apify.com/dami_studio) (community) - **Categories:** AI, For creators, Marketing - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 scene image generateds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Storyboard & Scene Image Generator Turn a story or script into a sequence of AI scene images, one per scene, that share a consistent art style and character look. Hand it a block of text and it works out the scenes for you, or pass your own scene list if you'd rather control the breakdown. Built for faceless videos, comics, ad concepts, and pitch decks where you need a run of related shots instead of a single one-off picture. ### How it works If you give it a `story`, a chat model (gpt-4o-mini) splits it into N short visual scene descriptions, keeping the same characters and setting across them. Each scene is then rendered with gpt-image-1, with your `style` and `characters` notes prepended to every prompt so the look stays consistent shot to shot. ### Input You can run with just a `story` (everything else has sensible defaults) or skip splitting entirely by passing your own `scenes`. Nothing is strictly required, but you do need an OpenAI key, either as `openaiApiKey` or the `OPENAI_API_KEY` environment variable. | Field | Required | Notes | | --- | --- | --- | | `story` | no | A story or script. Gets split into scenes automatically. Leave empty if you supply `scenes`. | | `scenes` | no | Your own array of scene descriptions, one image each. Overrides automatic splitting. | | `numScenes` | no | How many scenes to split a story into. Default 6, range 1-20. Ignored when you pass `scenes`. | | `style` | no | Shared art style applied to every image. Default is a cinematic look. | | `characters` | no | Recurring character notes for continuity, e.g. "an old bearded keeper in a yellow raincoat". | | `aspectRatio` | no | `9:16`, `16:9`, or `1:1`. Default `9:16`. | | `quality` | no | gpt-image-1 quality: `low`, `medium`, or `high`. Default `high`. | | `openaiApiKey` | no* | Your OpenAI key, used for both splitting and image generation. Required unless `OPENAI_API_KEY` is set. | | `baseUrl` | no | OpenAI-compatible base URL if you're routing through a proxy. Defaults to the OpenAI API. | ### Output The dataset gets one record per scene. Each record has `index`, the `scene` text, the full `prompt` that was sent, and `imageKey` / `imageUrl` pointing at the PNG in the run's key-value store, plus `aspectRatio` and `size`. Records that fail come back with `ok: false` and an `error` field, so a single bad scene won't kill the run. ### Example ```json { "story": "A lonely lighthouse keeper finds a mysterious glowing bottle washed ashore. Inside is a map to an island that shouldn't exist. He sets sail at dawn.", "numScenes": 6, "style": "cinematic, highly detailed, dramatic lighting, vibrant color grade", "characters": "an old bearded keeper in a yellow raincoat", "aspectRatio": "9:16", "quality": "high", "openaiApiKey": "sk-..." } ```` ### Pricing $0.05 per image, pay per result, no subscription. You bring your own OpenAI key, so the gpt-4o-mini and gpt-image-1 calls are billed to your own OpenAI account on top. ### Notes Continuity is prompt-based, not a locked reference image, so characters stay close but won't be pixel-identical across every scene. Tighter `characters` notes and a specific `style` help a lot. Higher `quality` costs more on your OpenAI bill and runs slower. # Actor input Schema ## `story` (type: `string`): A story or script. It will be split into visual scenes automatically. (Leave empty if you provide your own scenes.) ## `scenes` (type: `array`): Your own list of scene descriptions (one image each). Overrides automatic splitting. ## `numScenes` (type: `integer`): How many scenes/images to generate when splitting a story. ## `style` (type: `string`): Shared visual style applied to every image (for consistency). ## `characters` (type: `string`): Describe recurring characters so they stay consistent across scenes (e.g. 'an old bearded keeper in a yellow raincoat'). ## `aspectRatio` (type: `string`): 9:16 (vertical), 16:9 (wide), or 1:1 (square). ## `quality` (type: `string`): gpt-image-1 quality. ## `openaiApiKey` (type: `string`): Your OpenAI key — used for scene splitting and image generation (gpt-image-1). Kept private. ## `baseUrl` (type: `string`): OpenAI-compatible base URL. Default https://api.openai.com/v1. ## Actor input object example ```json { "story": "A lonely lighthouse keeper finds a mysterious glowing bottle washed ashore. Inside is a map to an island that shouldn't exist. He sets sail at dawn.", "numScenes": 6, "style": "cinematic, highly detailed, dramatic lighting, vibrant color grade", "aspectRatio": "9:16", "quality": "high" } ``` # Actor output Schema ## `results` (type: `string`): Result rows / metadata are stored in the default dataset (one row per item). ## `files` (type: `string`): Generated media/files (video, audio, images, captions) are stored in the default key-value store. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "story": "A lonely lighthouse keeper finds a mysterious glowing bottle washed ashore. Inside is a map to an island that shouldn't exist. He sets sail at dawn." }; // Run the Actor and wait for it to finish const run = await client.actor("dami_studio/ai-storyboard-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "story": "A lonely lighthouse keeper finds a mysterious glowing bottle washed ashore. Inside is a map to an island that shouldn't exist. He sets sail at dawn." } # Run the Actor and wait for it to finish run = client.actor("dami_studio/ai-storyboard-generator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "story": "A lonely lighthouse keeper finds a mysterious glowing bottle washed ashore. Inside is a map to an island that shouldn'\''t exist. He sets sail at dawn." }' | apify call dami_studio/ai-storyboard-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dami_studio/ai-storyboard-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Storyboard & Scene Image Generator", "description": "Turn a story or script into a sequence of consistent AI images — one per scene. Give it a story (it splits into scenes) or your own scene list, a shared art style, and optional character notes. For faceless videos, comics, ads, pitches, and animatics.", "version": "0.1", "x-build-id": "8e5xqoRm0ad8fMFrg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dami_studio~ai-storyboard-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dami_studio-ai-storyboard-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dami_studio~ai-storyboard-generator/runs": { "post": { "operationId": "runs-sync-dami_studio-ai-storyboard-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dami_studio~ai-storyboard-generator/run-sync": { "post": { "operationId": "run-sync-dami_studio-ai-storyboard-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "story": { "title": "Story / script", "type": "string", "description": "A story or script. It will be split into visual scenes automatically. (Leave empty if you provide your own scenes.)" }, "scenes": { "title": "Scenes (optional)", "type": "array", "description": "Your own list of scene descriptions (one image each). Overrides automatic splitting.", "items": { "type": "string" } }, "numScenes": { "title": "Number of scenes", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many scenes/images to generate when splitting a story.", "default": 6 }, "style": { "title": "Art style", "type": "string", "description": "Shared visual style applied to every image (for consistency).", "default": "cinematic, highly detailed, dramatic lighting, vibrant color grade" }, "characters": { "title": "Character notes (optional)", "type": "string", "description": "Describe recurring characters so they stay consistent across scenes (e.g. 'an old bearded keeper in a yellow raincoat')." }, "aspectRatio": { "title": "Aspect ratio", "enum": [ "9:16", "16:9", "1:1" ], "type": "string", "description": "9:16 (vertical), 16:9 (wide), or 1:1 (square).", "default": "9:16" }, "quality": { "title": "Image quality", "enum": [ "low", "medium", "high" ], "type": "string", "description": "gpt-image-1 quality.", "default": "high" }, "openaiApiKey": { "title": "OpenAI API key (BYO)", "type": "string", "description": "Your OpenAI key — used for scene splitting and image generation (gpt-image-1). Kept private." }, "baseUrl": { "title": "API base URL (advanced)", "type": "string", "description": "OpenAI-compatible base URL. Default https://api.openai.com/v1." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```