# MiniMax H3 Reference to Video LoRA > References into video with synchronized audio using MiniMax H3 ## Overview - **Endpoint**: `https://fal.run/minimax/h3/reference-to-video/lora` - **Model ID**: `minimax/h3/reference-to-video/lora` - **Category**: video-to-video - **Kind**: inference **Tags**: utility, editing ## Pricing Video costs **$0.10** per second at **768p**, **$0.1625** per second at **2K** and **$0.20** per second at **4K**. For more details, see [fal.ai pricing](https://fal.ai/pricing). ## 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: - **`prompt`** (`string`, _required_): Text prompt for video generation. Refer to reference assets by their modality and order in the reference lists: Image 1, Image 2, Video 1, Audio 1, and so on. - Examples: "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden." - **`duration`** (`integer`, _optional_): The duration of the video in seconds. Default value: `5` - Default: `5` - Range: `5` to `15` - **`resolution`** (`ResolutionEnum`, _optional_): The resolution of the generated video. Default value: `"2K"` - Default: `"2K"` - Options: `"768P"`, `"2K"`, `"4K"` - **`enable_prompt_expansion`** (`boolean`, _optional_): Whether to expand the prompt with a vision language model before generation. Default value: `true` - Default: `true` - **`enable_safety_checker`** (`boolean`, _optional_): If set to true, the safety checker will be enabled. Default value: `true` - Default: `true` - **`aspect_ratio`** (`AspectRatioEnum`, _optional_): The aspect ratio of the generated video. Default value: `"adaptive"` - Default: `"adaptive"` - Options: `"adaptive"`, `"21:9"`, `"16:9"`, `"4:3"`, `"1:1"`, `"3:4"`, `"9:16"` - **`reference_image_urls`** (`list`, _optional_): URLs of subject/style reference images, referenced in the prompt as Image 1, Image 2, and so on. Reference images, videos, and audio clips must add up to at most 12 files. - Array of string - Examples: ["https://storage.googleapis.com/falserverless/example_inputs/hailuo23/pro_i2v_in.jpg"] - **`reference_video_urls`** (`list`, _optional_): URLs of motion/reference video clips (2-15 seconds each, combined duration at most 15 seconds), referenced in the prompt as Video 1, Video 2, and so on. Reference images, videos, and audio clips must add up to at most 12 files. - Array of string - **`reference_audio_urls`** (`list`, _optional_): URLs of reference audio clips (2-15 seconds each, combined duration at most 15 seconds), referenced in the prompt as Audio 1, Audio 2, and so on. Audio cannot be the only reference input; provide at least one reference image or video with it. Reference images, videos, and audio clips must add up to at most 12 files. - Array of string - **`loras`** (`list`, _required_): List of LoRA adapters to apply to the transformer. - Array of LoRAInput **Required Parameters Example**: ```json { "prompt": "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden.", "loras": [ { "path": "https://huggingface.co/owner/repo/resolve/main/lora.safetensors", "scale": 1 } ] } ``` **Full Example**: ```json { "prompt": "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden.", "duration": 5, "resolution": "2K", "enable_prompt_expansion": true, "enable_safety_checker": true, "aspect_ratio": "adaptive", "reference_image_urls": [ "https://storage.googleapis.com/falserverless/example_inputs/hailuo23/pro_i2v_in.jpg" ], "loras": [ { "path": "https://huggingface.co/owner/repo/resolve/main/lora.safetensors", "scale": 1 } ] } ``` ### Output Schema The API returns the following output format: - **`video`** (`File`, _required_): The generated video **Example Response**: ```json { "video": { "url": "", "content_type": "image/png", "file_name": "z9RV14K95DvU.png", "file_size": 4404019 } } ``` ## Usage Examples ### cURL ```bash curl --request POST \ --url https://fal.run/minimax/h3/reference-to-video/lora \ --header "Authorization: Key $FAL_KEY" \ --header "Content-Type: application/json" \ --data '{ "prompt": "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden.", "loras": [ { "path": "https://huggingface.co/owner/repo/resolve/main/lora.safetensors", "scale": 1 } ] }' ``` ### Python Ensure you have the Python client installed: ```bash pip install fal-client ``` Then use the API client to make requests: ```python import fal_client def on_queue_update(update): if isinstance(update, fal_client.InProgress): for log in update.logs: print(log["message"]) result = fal_client.subscribe( "minimax/h3/reference-to-video/lora", arguments={ "prompt": "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden.", "loras": [{ "path": "https://huggingface.co/owner/repo/resolve/main/lora.safetensors", "scale": 1 }] }, with_logs=True, on_queue_update=on_queue_update, ) print(result) ``` ### JavaScript Ensure you have the JavaScript client installed: ```bash npm install --save @fal-ai/client ``` Then use the API client to make requests: ```javascript import { fal } from "@fal-ai/client"; const result = await fal.subscribe("minimax/h3/reference-to-video/lora", { input: { prompt: "Image 1 is the female protagonist. Image 2 is her small dog. Keep the woman and dog consistent with their respective reference images while they walk together through a sunlit garden.", loras: [{ path: "https://huggingface.co/owner/repo/resolve/main/lora.safetensors", scale: 1 }] }, logs: true, onQueueUpdate: (update) => { if (update.status === "IN_PROGRESS") { update.logs.map((log) => log.message).forEach(console.log); } }, }); console.log(result.data); console.log(result.requestId); ``` ## Additional Resources ### Documentation - [Model Playground](https://fal.ai/models/minimax/h3/reference-to-video/lora) - [API Documentation](https://fal.ai/models/minimax/h3/reference-to-video/lora/api) - [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=minimax/h3/reference-to-video/lora) ### fal.ai Platform - [Platform Documentation](https://fal.ai/docs/documentation) - [Python Client](https://fal.ai/docs/api-reference/client-libraries/python) - [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript) ### Other agent-readable surfaces This file covers one model. To find anything else: - [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs - [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page - [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined - Any other model: `https://fal.ai/models//llms.txt`