Multishot Master | Text to Video

fal-ai/multishot-master

MultiShotMaster is a controllable multi-shot narrative video generation framework that supports text-driven inter-shot consistency, variable shot counts and shot durations, customized subject with motion control, and background-driven customized scene.

Inference

Commercial use

Schema

LLMs

Playground API

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Generate

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/multishot-master", {
  input: {
    prompt: "Subject 1: A man wearing a grey t-shirt, dark vest. Subject 2: A woman with long brown hair, wearing a soft pink floral sundress. The whole scene takes place indoors in a spacious, well-decorated room. The visual style is modern television production, featuring clear imagery and naturalistic representation.",
    shots: [{
      prompt: "Subject 1 is smiling and gesturing with his right hand while facing the camera. Subject 2 is seen from behind, facing Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera is static, providing a medium close-up shot of Subject 1.",
      num_frames: 65
    }, {
      prompt: "Subject 2 is standing, looking at Subject 1 with a smile. Subject 1 is seen from the back, facing Subject 2. The scene is an indoor, spacious, and well-decorated room with a patterned rug and elegant furniture. The camera is static, providing a medium close-up shot of Subject 2.",
      num_frames: 65
    }, {
      prompt: "Subject 1 is speaking animatedly, gesturing with his right hand, facing the camera. Subject 2 stands with her back to the camera, listening to Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera maintains a close medium shot, capturing the subjects.",
      num_frames: 65
    }]
  },
  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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/multishot-master", {
  input: {
    prompt: "Subject 1: A man wearing a grey t-shirt, dark vest. Subject 2: A woman with long brown hair, wearing a soft pink floral sundress. The whole scene takes place indoors in a spacious, well-decorated room. The visual style is modern television production, featuring clear imagery and naturalistic representation.",
    shots: [{
      prompt: "Subject 1 is smiling and gesturing with his right hand while facing the camera. Subject 2 is seen from behind, facing Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera is static, providing a medium close-up shot of Subject 1.",
      num_frames: 65
    }, {
      prompt: "Subject 2 is standing, looking at Subject 1 with a smile. Subject 1 is seen from the back, facing Subject 2. The scene is an indoor, spacious, and well-decorated room with a patterned rug and elegant furniture. The camera is static, providing a medium close-up shot of Subject 2.",
      num_frames: 65
    }, {
      prompt: "Subject 1 is speaking animatedly, gesturing with his right hand, facing the camera. Subject 2 stands with her back to the camera, listening to Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera maintains a close medium shot, capturing the subjects.",
      num_frames: 65
    }]
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/multishot-master", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/multishot-master", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

Global story caption describing the overall scene, subjects, setting, and visual style. This provides inter-shot consistency.

shots list<Shot>* required

List of shots to generate. Each shot has its own caption and frame count. Maximum 5 shots with a combined maximum of 308 frames.

negative_prompt string

Negative prompt describing undesired content in the generated video. Default value: "bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards"

resolution ResolutionEnum

Resolution of the generated video. Default value: "480p"

Possible enum values: 480p, 720p

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video. Default value: "16:9"

Possible enum values: 16:9, 9:16

num_inference_steps integer

Number of denoising steps. Higher values produce better quality but take longer. Default value: 50

guidance_scale float

Classifier-free guidance scale. Default value: 5

seed integer

Random seed for reproducibility.

frames_per_second integer

Frames per second of the output video. Default value: 16

enable_safety_checker boolean

Enable safety checker for input/output content. Default value: true

{
  "prompt": "Subject 1: A man wearing a grey t-shirt, dark vest. Subject 2: A woman with long brown hair, wearing a soft pink floral sundress. The whole scene takes place indoors in a spacious, well-decorated room. The visual style is modern television production, featuring clear imagery and naturalistic representation.",
  "shots": [
    {
      "prompt": "Subject 1 is smiling and gesturing with his right hand while facing the camera. Subject 2 is seen from behind, facing Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera is static, providing a medium close-up shot of Subject 1.",
      "num_frames": 65
    },
    {
      "prompt": "Subject 2 is standing, looking at Subject 1 with a smile. Subject 1 is seen from the back, facing Subject 2. The scene is an indoor, spacious, and well-decorated room with a patterned rug and elegant furniture. The camera is static, providing a medium close-up shot of Subject 2.",
      "num_frames": 65
    },
    {
      "prompt": "Subject 1 is speaking animatedly, gesturing with his right hand, facing the camera. Subject 2 stands with her back to the camera, listening to Subject 1. The scene is indoors in a well-decorated room, with a dark door and ornate glass visible in the background. The camera maintains a close medium shot, capturing the subjects.",
      "num_frames": 65
    }
  ],
  "negative_prompt": "bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards",
  "resolution": "480p",
  "aspect_ratio": "16:9",
  "num_inference_steps": 50,
  "guidance_scale": 5,
  "frames_per_second": 16,
  "enable_safety_checker": true
}

Output#

video File* required

The generated multi-shot narrative video.

seed integer* required

The seed used for generation.

timings Timings* required

Performance timing information.

{
  "video": {
    "file_size": 837114,
    "file_name": "output.mp4",
    "content_type": "video/mp4",
    "url": "https://v3b.fal.media/files/b/0a8fb858/x6xEQNBP9m61inmR6p_WH_output.mp4"
  },
  "seed": 680491931
}

Other types#

Shot#

prompt string* required

Caption describing what happens in this shot, including subject actions, camera angles, and scene details.

num_frames integer

Number of frames for this shot. Each shot must have between 17 and 100 frames. Default value: 65

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

Multishot Master Text to Video

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Shot#

File#