"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const taskData = {
    datasets: [
        {
            description: "Microsoft Research Video to Text is a large-scale dataset for open domain video captioning",
            id: "iejMac/CLIP-MSR-VTT",
        },
        {
            description: "UCF101 Human Actions dataset consists of 13,320 video clips from YouTube, with 101 classes.",
            id: "quchenyuan/UCF101-ZIP",
        },
        {
            description: "A high-quality dataset for human action recognition in YouTube videos.",
            id: "nateraw/kinetics",
        },
        {
            description: "A dataset of video clips of humans performing pre-defined basic actions with everyday objects.",
            id: "HuggingFaceM4/something_something_v2",
        },
        {
            description: "This dataset consists of text-video pairs and contains noisy samples with irrelevant video descriptions",
            id: "HuggingFaceM4/webvid",
        },
        {
            description: "A dataset of short Flickr videos for the temporal localization of events with descriptions.",
            id: "iejMac/CLIP-DiDeMo",
        },
    ],
    demo: {
        inputs: [
            {
                label: "Input",
                content: "Darth Vader is surfing on the waves.",
                type: "text",
            },
        ],
        outputs: [
            {
                filename: "text-to-video-output.gif",
                type: "img",
            },
        ],
    },
    metrics: [
        {
            description: "Inception Score uses an image classification model that predicts class labels and evaluates how distinct and diverse the images are. A higher score indicates better video generation.",
            id: "is",
        },
        {
            description: "Frechet Inception Distance uses an image classification model to obtain image embeddings. The metric compares mean and standard deviation of the embeddings of real and generated images. A smaller score indicates better video generation.",
            id: "fid",
        },
        {
            description: "Frechet Video Distance uses a model that captures coherence for changes in frames and the quality of each frame. A smaller score indicates better video generation.",
            id: "fvd",
        },
        {
            description: "CLIPSIM measures similarity between video frames and text using an image-text similarity model. A higher score indicates better video generation.",
            id: "clipsim",
        },
    ],
    models: [
        {
            description: "A strong model for consistent video generation.",
            id: "tencent/HunyuanVideo",
        },
        {
            description: "A text-to-video model with high fidelity motion and strong prompt adherence.",
            id: "Lightricks/LTX-Video",
        },
        {
            description: "A text-to-video model focusing on physics-aware applications like robotics.",
            id: "nvidia/Cosmos-1.0-Diffusion-7B-Text2World",
        },
        {
            description: "Very fast model for video generation.",
            id: "Lightricks/LTX-Video-0.9.8-13B-distilled",
        },
    ],
    spaces: [
        {
            description: "An application that generates video from text.",
            id: "VideoCrafter/VideoCrafter",
        },
        {
            description: "Consistent video generation application.",
            id: "Wan-AI/Wan2.1",
        },
        {
            description: "A cutting edge video generation application.",
            id: "Pyramid-Flow/pyramid-flow",
        },
    ],
    summary: "Text-to-video models can be used in any application that requires generating consistent sequence of images from text. ",
    widgetModels: ["Wan-AI/Wan2.2-TI2V-5B"],
    youtubeId: undefined,
};
exports.default = taskData;
