Upload folder using huggingface_hub (part 7)

node_modules/@huggingface/tasks/dist/esm/tasks/image-segmentation/data.d.ts.map

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+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-segmentation/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cA8Ff,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-segmentation/data.js

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+const taskData = {
+    datasets: [
+        {
+            description: "Scene segmentation dataset.",
+            id: "scene_parse_150",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "image-segmentation-input.jpeg",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                filename: "image-segmentation-output.png",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [
+        {
+            description: "Average Precision (AP) is the Area Under the PR Curve (AUC-PR). It is calculated for each semantic class separately",
+            id: "Average Precision",
+        },
+        {
+            description: "Mean Average Precision (mAP) is the overall average of the AP values",
+            id: "Mean Average Precision",
+        },
+        {
+            description: "Intersection over Union (IoU) is the overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic classes",
+            id: "Mean Intersection over Union",
+        },
+        {
+            description: "APα is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",
+            id: "APα",
+        },
+    ],
+    models: [
+        {
+            // TO DO: write description
+            description: "Solid panoptic segmentation model trained on COCO.",
+            id: "tue-mps/coco_panoptic_eomt_large_640",
+        },
+        {
+            description: "Background removal model.",
+            id: "briaai/RMBG-1.4",
+        },
+        {
+            description: "A multipurpose image segmentation model for high resolution images.",
+            id: "ZhengPeng7/BiRefNet",
+        },
+        {
+            description: "Powerful human-centric image segmentation model.",
+            id: "facebook/sapiens-seg-1b",
+        },
+        {
+            description: "Panoptic segmentation model trained on the COCO (common objects) dataset.",
+            id: "facebook/mask2former-swin-large-coco-panoptic",
+        },
+    ],
+    spaces: [
+        {
+            description: "A semantic segmentation application that can predict unseen instances out of the box.",
+            id: "facebook/ov-seg",
+        },
+        {
+            description: "One of the strongest segmentation applications.",
+            id: "jbrinkma/segment-anything",
+        },
+        {
+            description: "A human-centric segmentation model.",
+            id: "facebook/sapiens-pose",
+        },
+        {
+            description: "An instance segmentation application to predict neuronal cell types from microscopy images.",
+            id: "rashmi/sartorius-cell-instance-segmentation",
+        },
+        {
+            description: "An application that segments videos.",
+            id: "ArtGAN/Segment-Anything-Video",
+        },
+        {
+            description: "An panoptic segmentation application built for outdoor environments.",
+            id: "segments/panoptic-segment-anything",
+        },
+    ],
+    summary: "Image Segmentation divides an image into segments where each pixel in the image is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.",
+    widgetModels: ["nvidia/segformer-b0-finetuned-ade-512-512"],
+    youtubeId: "dKE8SIt9C-w",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-segmentation/inference.d.ts

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+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image Segmentation inference
+ */
+export interface ImageSegmentationInput {
+    /**
+     * The input image data as a base64-encoded string. If no `parameters` are provided, you can
+     * also provide the image data as a raw bytes payload.
+     */
+    inputs: Blob;
+    /**
+     * Additional inference parameters for Image Segmentation
+     */
+    parameters?: ImageSegmentationParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image Segmentation
+ */
+export interface ImageSegmentationParameters {
+    /**
+     * Threshold to use when turning the predicted masks into binary values.
+     */
+    mask_threshold?: number;
+    /**
+     * Mask overlap threshold to eliminate small, disconnected segments.
+     */
+    overlap_mask_area_threshold?: number;
+    /**
+     * Segmentation task to be performed, depending on model capabilities.
+     */
+    subtask?: ImageSegmentationSubtask;
+    /**
+     * Probability threshold to filter out predicted masks.
+     */
+    threshold?: number;
+    [property: string]: unknown;
+}
+/**
+ * Segmentation task to be performed, depending on model capabilities.
+ */
+export type ImageSegmentationSubtask = "instance" | "panoptic" | "semantic";
+export type ImageSegmentationOutput = ImageSegmentationOutputElement[];
+/**
+ * Outputs of inference for the Image Segmentation task
+ *
+ * A predicted mask / segment
+ */
+export interface ImageSegmentationOutputElement {
+    /**
+     * The label of the predicted segment.
+     */
+    label: string;
+    /**
+     * The corresponding mask as a black-and-white image (base64-encoded).
+     */
+    mask: string;
+    /**
+     * The score or confidence degree the model has.
+     */
+    score?: number;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-segmentation/inference.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"inference.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-segmentation/inference.ts"],"names":[],"mappings":"AAAA;;;;GAIG;AACH;;GAEG;AACH,MAAM,WAAW,sBAAsB;IACtC;;;OAGG;IACH,MAAM,EAAE,IAAI,CAAC;IACb;;OAEG;IACH,UAAU,CAAC,EAAE,2BAA2B,CAAC;IACzC,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,WAAW,2BAA2B;IAC3C;;OAEG;IACH,cAAc,CAAC,EAAE,MAAM,CAAC;IACxB;;OAEG;IACH,2BAA2B,CAAC,EAAE,MAAM,CAAC;IACrC;;OAEG;IACH,OAAO,CAAC,EAAE,wBAAwB,CAAC;IACnC;;OAEG;IACH,SAAS,CAAC,EAAE,MAAM,CAAC;IACnB,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,MAAM,wBAAwB,GAAG,UAAU,GAAG,UAAU,GAAG,UAAU,CAAC;AAC5E,MAAM,MAAM,uBAAuB,GAAG,8BAA8B,EAAE,CAAC;AACvE;;;;GAIG;AACH,MAAM,WAAW,8BAA8B;IAC9C;;OAEG;IACH,KAAK,EAAE,MAAM,CAAC;IACd;;OAEG;IACH,IAAI,EAAE,MAAM,CAAC;IACb;;OAEG;IACH,KAAK,CAAC,EAAE,MAAM,CAAC;IACf,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-segmentation/inference.js

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+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/data.d.ts

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+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/data.d.ts.map

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+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-text-to-image/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAiDf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/data.js

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+const taskData = {
+    datasets: [],
+    demo: {
+        inputs: [
+            {
+                filename: "image-text-to-image-input.jpeg",
+                type: "img",
+            },
+            {
+                label: "Input",
+                content: "A city above clouds, pastel colors, Victorian style",
+                type: "text",
+            },
+        ],
+        outputs: [
+            {
+                filename: "image-text-to-image-output.png",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [
+        {
+            description: "The Fréchet Inception Distance (FID) calculates the distance between distributions between synthetic and real samples. A lower FID score indicates better similarity between the distributions of real and generated images.",
+            id: "FID",
+        },
+        {
+            description: "CLIP Score measures the similarity between the generated image and the text prompt using CLIP embeddings. A higher score indicates better alignment with the text prompt.",
+            id: "CLIP",
+        },
+    ],
+    models: [
+        {
+            description: "A powerful model for image-text-to-image generation.",
+            id: "black-forest-labs/FLUX.2-dev",
+        },
+    ],
+    spaces: [
+        {
+            description: "An application for image-text-to-image generation.",
+            id: "black-forest-labs/FLUX.2-dev",
+        },
+    ],
+    summary: "Image-text-to-image models take an image and a text prompt as input and generate a new image based on the reference image and text instructions. These models are useful for image editing, style transfer, image variations, and guided image generation tasks.",
+    widgetModels: ["black-forest-labs/FLUX.2-dev"],
+    youtubeId: undefined,
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/inference.d.ts

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+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image Text To Image inference. Either inputs (image) or prompt (in parameters)
+ * must be provided, or both.
+ */
+export interface ImageTextToImageInput {
+    /**
+     * The input image data as a base64-encoded string. If no `parameters` are provided, you can
+     * also provide the image data as a raw bytes payload. Either this or prompt must be
+     * provided.
+     */
+    inputs?: Blob;
+    /**
+     * Additional inference parameters for Image Text To Image
+     */
+    parameters?: ImageTextToImageParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image Text To Image
+ */
+export interface ImageTextToImageParameters {
+    /**
+     * For diffusion models. A higher guidance scale value encourages the model to generate
+     * images closely linked to the text prompt at the expense of lower image quality.
+     */
+    guidance_scale?: number;
+    /**
+     * One prompt to guide what NOT to include in image generation.
+     */
+    negative_prompt?: string;
+    /**
+     * For diffusion models. The number of denoising steps. More denoising steps usually lead to
+     * a higher quality image at the expense of slower inference.
+     */
+    num_inference_steps?: number;
+    /**
+     * The text prompt to guide the image generation. Either this or inputs (image) must be
+     * provided.
+     */
+    prompt?: string;
+    /**
+     * Seed for the random number generator.
+     */
+    seed?: number;
+    /**
+     * The size in pixels of the output image. This parameter is only supported by some
+     * providers and for specific models. It will be ignored when unsupported.
+     */
+    target_size?: TargetSize;
+    [property: string]: unknown;
+}
+/**
+ * The size in pixels of the output image. This parameter is only supported by some
+ * providers and for specific models. It will be ignored when unsupported.
+ */
+export interface TargetSize {
+    height: number;
+    width: number;
+    [property: string]: unknown;
+}
+/**
+ * Outputs of inference for the Image Text To Image task
+ */
+export interface ImageTextToImageOutput {
+    /**
+     * The generated image returned as raw bytes in the payload.
+     */
+    image: unknown;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/inference.d.ts.map

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+{"version":3,"file":"inference.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-text-to-image/inference.ts"],"names":[],"mappings":"AAAA;;;;GAIG;AACH;;;GAGG;AACH,MAAM,WAAW,qBAAqB;IACrC;;;;OAIG;IACH,MAAM,CAAC,EAAE,IAAI,CAAC;IACd;;OAEG;IACH,UAAU,CAAC,EAAE,0BAA0B,CAAC;IACxC,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,WAAW,0BAA0B;IAC1C;;;OAGG;IACH,cAAc,CAAC,EAAE,MAAM,CAAC;IACxB;;OAEG;IACH,eAAe,CAAC,EAAE,MAAM,CAAC;IACzB;;;OAGG;IACH,mBAAmB,CAAC,EAAE,MAAM,CAAC;IAC7B;;;OAGG;IACH,MAAM,CAAC,EAAE,MAAM,CAAC;IAChB;;OAEG;IACH,IAAI,CAAC,EAAE,MAAM,CAAC;IACd;;;OAGG;IACH,WAAW,CAAC,EAAE,UAAU,CAAC;IACzB,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;;GAGG;AACH,MAAM,WAAW,UAAU;IAC1B,MAAM,EAAE,MAAM,CAAC;IACf,KAAK,EAAE,MAAM,CAAC;IACd,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,WAAW,sBAAsB;IACtC;;OAEG;IACH,KAAK,EAAE,OAAO,CAAC;IACf,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-image/inference.js

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+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-text/data.d.ts

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+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-text/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-text-to-text/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAiFf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-text/data.js

@@ -0,0 +1,81 @@
+const taskData = {
+    datasets: [
+        {
+            description: "Instructions composed of image and text.",
+            id: "liuhaotian/LLaVA-Instruct-150K",
+        },
+        {
+            description: "Collection of image-text pairs on scientific topics.",
+            id: "DAMO-NLP-SG/multimodal_textbook",
+        },
+        {
+            description: "A collection of datasets made for model fine-tuning.",
+            id: "HuggingFaceM4/the_cauldron",
+        },
+        {
+            description: "Screenshots of websites with their HTML/CSS codes.",
+            id: "HuggingFaceM4/WebSight",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "image-text-to-text-input.png",
+                type: "img",
+            },
+            {
+                label: "Text Prompt",
+                content: "Describe the position of the bee in detail.",
+                type: "text",
+            },
+        ],
+        outputs: [
+            {
+                label: "Answer",
+                content: "The bee is sitting on a pink flower, surrounded by other flowers. The bee is positioned in the center of the flower, with its head and front legs sticking out.",
+                type: "text",
+            },
+        ],
+    },
+    metrics: [],
+    models: [
+        {
+            description: "Small and efficient yet powerful vision language model.",
+            id: "HuggingFaceTB/SmolVLM-Instruct",
+        },
+        {
+            description: "Cutting-edge reasoning vision language model.",
+            id: "zai-org/GLM-4.5V",
+        },
+        {
+            description: "Cutting-edge small vision language model to convert documents to text.",
+            id: "rednote-hilab/dots.ocr",
+        },
+        {
+            description: "Small yet powerful model.",
+            id: "Qwen/Qwen2.5-VL-3B-Instruct",
+        },
+        {
+            description: "Image-text-to-text model with agentic capabilities.",
+            id: "microsoft/Magma-8B",
+        },
+    ],
+    spaces: [
+        {
+            description: "Leaderboard to evaluate vision language models.",
+            id: "opencompass/open_vlm_leaderboard",
+        },
+        {
+            description: "An application that compares object detection capabilities of different vision language models.",
+            id: "sergiopaniego/vlm_object_understanding",
+        },
+        {
+            description: "An application to compare different OCR models.",
+            id: "prithivMLmods/Multimodal-OCR",
+        },
+    ],
+    summary: "Image-text-to-text models take in an image and text prompt and output text. These models are also called vision-language models, or VLMs. The difference from image-to-text models is that these models take an additional text input, not restricting the model to certain use cases like image captioning, and may also be trained to accept a conversation as input.",
+    widgetModels: ["zai-org/GLM-4.5V"],
+    youtubeId: "IoGaGfU1CIg",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-text-to-video/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAiDf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/data.js

@@ -0,0 +1,48 @@
+const taskData = {
+    datasets: [],
+    demo: {
+        inputs: [
+            {
+                filename: "image-text-to-video-input.jpg",
+                type: "img",
+            },
+            {
+                label: "Input",
+                content: "Darth Vader is surfing on the waves.",
+                type: "text",
+            },
+        ],
+        outputs: [
+            {
+                filename: "image-text-to-video-output.gif",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [
+        {
+            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 powerful model for image-text-to-video generation.",
+            id: "Lightricks/LTX-Video",
+        },
+    ],
+    spaces: [
+        {
+            description: "An application for image-text-to-video generation.",
+            id: "Lightricks/ltx-video-distilled",
+        },
+    ],
+    summary: "Image-text-to-video models take an reference image and a text instructions as and generate a video based on them. These models are useful for animating still images, creating dynamic content from static references, and generating videos with specific motion or transformation guidance.",
+    widgetModels: ["Lightricks/LTX-Video"],
+    youtubeId: undefined,
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/inference.d.ts

@@ -0,0 +1,78 @@
+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image Text To Video inference. Either inputs (image) or prompt (in parameters)
+ * must be provided, or both.
+ */
+export interface ImageTextToVideoInput {
+    /**
+     * The input image data as a base64-encoded string. If no `parameters` are provided, you can
+     * also provide the image data as a raw bytes payload. Either this or prompt must be
+     * provided.
+     */
+    inputs?: Blob;
+    /**
+     * Additional inference parameters for Image Text To Video
+     */
+    parameters?: ImageTextToVideoParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image Text To Video
+ */
+export interface ImageTextToVideoParameters {
+    /**
+     * For diffusion models. A higher guidance scale value encourages the model to generate
+     * videos closely linked to the text prompt at the expense of lower image quality.
+     */
+    guidance_scale?: number;
+    /**
+     * One prompt to guide what NOT to include in video generation.
+     */
+    negative_prompt?: string;
+    /**
+     * The num_frames parameter determines how many video frames are generated.
+     */
+    num_frames?: number;
+    /**
+     * The number of denoising steps. More denoising steps usually lead to a higher quality
+     * video at the expense of slower inference.
+     */
+    num_inference_steps?: number;
+    /**
+     * The text prompt to guide the video generation. Either this or inputs (image) must be
+     * provided.
+     */
+    prompt?: string;
+    /**
+     * Seed for the random number generator.
+     */
+    seed?: number;
+    /**
+     * The size in pixel of the output video frames.
+     */
+    target_size?: TargetSize;
+    [property: string]: unknown;
+}
+/**
+ * The size in pixel of the output video frames.
+ */
+export interface TargetSize {
+    height: number;
+    width: number;
+    [property: string]: unknown;
+}
+/**
+ * Outputs of inference for the Image Text To Video task
+ */
+export interface ImageTextToVideoOutput {
+    /**
+     * The generated video returned as raw bytes in the payload.
+     */
+    video: unknown;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/inference.d.ts.map

@@ -0,0 +1 @@
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node_modules/@huggingface/tasks/dist/esm/tasks/image-text-to-video/inference.js

@@ -0,0 +1 @@
+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-3d/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-3d/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-to-3d/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAsEf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-3d/data.js

@@ -0,0 +1,72 @@
+const taskData = {
+    datasets: [
+        {
+            description: "A large dataset of over 10 million 3D objects.",
+            id: "allenai/objaverse-xl",
+        },
+        {
+            description: "A dataset of isolated object images for evaluating image-to-3D models.",
+            id: "dylanebert/iso3d",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "image-to-3d-image-input.png",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                label: "Result",
+                content: "image-to-3d-3d-output-filename.glb",
+                type: "text",
+            },
+        ],
+    },
+    metrics: [],
+    models: [
+        {
+            description: "Fast image-to-3D mesh model by Tencent.",
+            id: "TencentARC/InstantMesh",
+        },
+        {
+            description: "3D world generation model.",
+            id: "tencent/HunyuanWorld-1",
+        },
+        {
+            description: "A scaled up image-to-3D mesh model derived from TripoSR.",
+            id: "hwjiang/Real3D",
+        },
+        {
+            description: "Consistent image-to-3d generation model.",
+            id: "stabilityai/stable-point-aware-3d",
+        },
+    ],
+    spaces: [
+        {
+            description: "Leaderboard to evaluate image-to-3D models.",
+            id: "dylanebert/3d-arena",
+        },
+        {
+            description: "Image-to-3D demo with mesh outputs.",
+            id: "TencentARC/InstantMesh",
+        },
+        {
+            description: "Image-to-3D demo.",
+            id: "stabilityai/stable-point-aware-3d",
+        },
+        {
+            description: "Image-to-3D demo with mesh outputs.",
+            id: "hwjiang/Real3D",
+        },
+        {
+            description: "Image-to-3D demo with splat outputs.",
+            id: "dylanebert/LGM-mini",
+        },
+    ],
+    summary: "Image-to-3D models take in image input and produce 3D output.",
+    widgetModels: [],
+    youtubeId: "",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-to-image/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cA2Ff,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/data.js

@@ -0,0 +1,89 @@
+const taskData = {
+    datasets: [
+        {
+            description: "Synthetic dataset, for image relighting",
+            id: "VIDIT",
+        },
+        {
+            description: "Multiple images of celebrities, used for facial expression translation",
+            id: "huggan/CelebA-faces",
+        },
+        {
+            description: "12M image-caption pairs.",
+            id: "Spawning/PD12M",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "image-to-image-input.jpeg",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                filename: "image-to-image-output.png",
+                type: "img",
+            },
+        ],
+    },
+    isPlaceholder: false,
+    metrics: [
+        {
+            description: "Peak Signal to Noise Ratio (PSNR) is an approximation of the human perception, considering the ratio of the absolute intensity with respect to the variations. Measured in dB, a high value indicates a high fidelity.",
+            id: "PSNR",
+        },
+        {
+            description: "Structural Similarity Index (SSIM) is a perceptual metric which compares the luminance, contrast and structure of two images. The values of SSIM range between -1 and 1, and higher values indicate closer resemblance to the original image.",
+            id: "SSIM",
+        },
+        {
+            description: "Inception Score (IS) is an analysis of the labels predicted by an image classification model when presented with a sample of the generated images.",
+            id: "IS",
+        },
+    ],
+    models: [
+        {
+            description: "An image-to-image model to improve image resolution.",
+            id: "fal/AuraSR-v2",
+        },
+        {
+            description: "Powerful image editing model.",
+            id: "black-forest-labs/FLUX.1-Kontext-dev",
+        },
+        {
+            description: "Virtual try-on model.",
+            id: "yisol/IDM-VTON",
+        },
+        {
+            description: "Image re-lighting model.",
+            id: "kontext-community/relighting-kontext-dev-lora-v3",
+        },
+        {
+            description: "Strong model for inpainting and outpainting.",
+            id: "black-forest-labs/FLUX.1-Fill-dev",
+        },
+        {
+            description: "Strong model for image editing using depth maps.",
+            id: "black-forest-labs/FLUX.1-Depth-dev-lora",
+        },
+    ],
+    spaces: [
+        {
+            description: "Image editing application.",
+            id: "black-forest-labs/FLUX.1-Kontext-Dev",
+        },
+        {
+            description: "Image relighting application.",
+            id: "lllyasviel/iclight-v2-vary",
+        },
+        {
+            description: "An application for image upscaling.",
+            id: "jasperai/Flux.1-dev-Controlnet-Upscaler",
+        },
+    ],
+    summary: "Image-to-image is the task of transforming an input image through a variety of possible manipulations and enhancements, such as super-resolution, image inpainting, colorization, and more.",
+    widgetModels: ["Qwen/Qwen-Image"],
+    youtubeId: "",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/inference.d.ts

@@ -0,0 +1,69 @@
+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image To Image inference
+ */
+export interface ImageToImageInput {
+    /**
+     * The input image data as a base64-encoded string. If no `parameters` are provided, you can
+     * also provide the image data as a raw bytes payload.
+     */
+    inputs: Blob;
+    /**
+     * Additional inference parameters for Image To Image
+     */
+    parameters?: ImageToImageParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image To Image
+ */
+export interface ImageToImageParameters {
+    /**
+     * For diffusion models. A higher guidance scale value encourages the model to generate
+     * images closely linked to the text prompt at the expense of lower image quality.
+     */
+    guidance_scale?: number;
+    /**
+     * One prompt to guide what NOT to include in image generation.
+     */
+    negative_prompt?: string;
+    /**
+     * For diffusion models. The number of denoising steps. More denoising steps usually lead to
+     * a higher quality image at the expense of slower inference.
+     */
+    num_inference_steps?: number;
+    /**
+     * The text prompt to guide the image generation.
+     */
+    prompt?: string;
+    /**
+     * The size in pixels of the output image. This parameter is only supported by some
+     * providers and for specific models. It will be ignored when unsupported.
+     */
+    target_size?: TargetSize;
+    [property: string]: unknown;
+}
+/**
+ * The size in pixels of the output image. This parameter is only supported by some
+ * providers and for specific models. It will be ignored when unsupported.
+ */
+export interface TargetSize {
+    height: number;
+    width: number;
+    [property: string]: unknown;
+}
+/**
+ * Outputs of inference for the Image To Image task
+ */
+export interface ImageToImageOutput {
+    /**
+     * The output image returned as raw bytes in the payload.
+     */
+    image?: unknown;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/inference.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"inference.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-to-image/inference.ts"],"names":[],"mappings":"AAAA;;;;GAIG;AACH;;GAEG;AACH,MAAM,WAAW,iBAAiB;IACjC;;;OAGG;IACH,MAAM,EAAE,IAAI,CAAC;IACb;;OAEG;IACH,UAAU,CAAC,EAAE,sBAAsB,CAAC;IACpC,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,WAAW,sBAAsB;IACtC;;;OAGG;IACH,cAAc,CAAC,EAAE,MAAM,CAAC;IACxB;;OAEG;IACH,eAAe,CAAC,EAAE,MAAM,CAAC;IACzB;;;OAGG;IACH,mBAAmB,CAAC,EAAE,MAAM,CAAC;IAC7B;;OAEG;IACH,MAAM,CAAC,EAAE,MAAM,CAAC;IAChB;;;OAGG;IACH,WAAW,CAAC,EAAE,UAAU,CAAC;IACzB,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;;GAGG;AACH,MAAM,WAAW,UAAU;IAC1B,MAAM,EAAE,MAAM,CAAC;IACf,KAAK,EAAE,MAAM,CAAC;IACd,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B;AACD;;GAEG;AACH,MAAM,WAAW,kBAAkB;IAClC;;OAEG;IACH,KAAK,CAAC,EAAE,OAAO,CAAC;IAChB,CAAC,QAAQ,EAAE,MAAM,GAAG,OAAO,CAAC;CAC5B"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-image/inference.js

@@ -0,0 +1 @@
+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-to-text/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAyDf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/data.js

@@ -0,0 +1,58 @@
+const taskData = {
+    datasets: [
+        {
+            // TODO write proper description
+            description: "Dataset from 12M image-text of Reddit",
+            id: "red_caps",
+        },
+        {
+            // TODO write proper description
+            description: "Dataset from 3.3M images of Google",
+            id: "datasets/conceptual_captions",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "savanna.jpg",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                label: "Detailed description",
+                content: "a herd of giraffes and zebras grazing in a field",
+                type: "text",
+            },
+        ],
+    },
+    metrics: [],
+    models: [
+        {
+            description: "Strong OCR model.",
+            id: "allenai/olmOCR-7B-0725",
+        },
+        {
+            description: "Powerful image captioning model.",
+            id: "fancyfeast/llama-joycaption-beta-one-hf-llava",
+        },
+    ],
+    spaces: [
+        {
+            description: "SVG generator app from images.",
+            id: "multimodalart/OmniSVG-3B",
+        },
+        {
+            description: "An application that converts documents to markdown.",
+            id: "numind/NuMarkdown-8B-Thinking",
+        },
+        {
+            description: "An application that can caption images.",
+            id: "fancyfeast/joy-caption-beta-one",
+        },
+    ],
+    summary: "Image to text models output a text from a given image. Image captioning or optical character recognition can be considered as the most common applications of image to text.",
+    widgetModels: ["Salesforce/blip-image-captioning-large"],
+    youtubeId: "",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/inference.d.ts

@@ -0,0 +1,135 @@
+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image To Text inference
+ */
+export interface ImageToTextInput {
+    /**
+     * The input image data
+     */
+    inputs: Blob;
+    /**
+     * Additional inference parameters for Image To Text
+     */
+    parameters?: ImageToTextParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image To Text
+ */
+export interface ImageToTextParameters {
+    /**
+     * Parametrization of the text generation process
+     */
+    generation_parameters?: GenerationParameters;
+    /**
+     * The amount of maximum tokens to generate.
+     */
+    max_new_tokens?: number;
+    [property: string]: unknown;
+}
+/**
+ * Parametrization of the text generation process
+ */
+export interface GenerationParameters {
+    /**
+     * Whether to use sampling instead of greedy decoding when generating new tokens.
+     */
+    do_sample?: boolean;
+    /**
+     * Controls the stopping condition for beam-based methods.
+     */
+    early_stopping?: EarlyStoppingUnion;
+    /**
+     * If set to float strictly between 0 and 1, only tokens with a conditional probability
+     * greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+     * 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+     * Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+     */
+    epsilon_cutoff?: number;
+    /**
+     * Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+     * float strictly between 0 and 1, a token is only considered if it is greater than either
+     * eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+     * term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+     * the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+     * See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+     * for more details.
+     */
+    eta_cutoff?: number;
+    /**
+     * The maximum length (in tokens) of the generated text, including the input.
+     */
+    max_length?: number;
+    /**
+     * The maximum number of tokens to generate. Takes precedence over max_length.
+     */
+    max_new_tokens?: number;
+    /**
+     * The minimum length (in tokens) of the generated text, including the input.
+     */
+    min_length?: number;
+    /**
+     * The minimum number of tokens to generate. Takes precedence over min_length.
+     */
+    min_new_tokens?: number;
+    /**
+     * Number of groups to divide num_beams into in order to ensure diversity among different
+     * groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+     */
+    num_beam_groups?: number;
+    /**
+     * Number of beams to use for beam search.
+     */
+    num_beams?: number;
+    /**
+     * The value balances the model confidence and the degeneration penalty in contrastive
+     * search decoding.
+     */
+    penalty_alpha?: number;
+    /**
+     * The value used to modulate the next token probabilities.
+     */
+    temperature?: number;
+    /**
+     * The number of highest probability vocabulary tokens to keep for top-k-filtering.
+     */
+    top_k?: number;
+    /**
+     * If set to float < 1, only the smallest set of most probable tokens with probabilities
+     * that add up to top_p or higher are kept for generation.
+     */
+    top_p?: number;
+    /**
+     * Local typicality measures how similar the conditional probability of predicting a target
+     * token next is to the expected conditional probability of predicting a random token next,
+     * given the partial text already generated. If set to float < 1, the smallest set of the
+     * most locally typical tokens with probabilities that add up to typical_p or higher are
+     * kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+     */
+    typical_p?: number;
+    /**
+     * Whether the model should use the past last key/values attentions to speed up decoding
+     */
+    use_cache?: boolean;
+    [property: string]: unknown;
+}
+/**
+ * Controls the stopping condition for beam-based methods.
+ */
+export type EarlyStoppingUnion = boolean | "never";
+/**
+ * Outputs of inference for the Image To Text task
+ */
+export interface ImageToTextOutput {
+    generatedText: unknown;
+    /**
+     * The generated text.
+     */
+    generated_text?: string;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/inference.d.ts.map

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node_modules/@huggingface/tasks/dist/esm/tasks/image-to-text/inference.js

@@ -0,0 +1 @@
+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/image-to-video/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAyHf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/data.js

@@ -0,0 +1,117 @@
+const taskData = {
+    datasets: [
+        {
+            description: "A benchmark dataset for reference image controlled video generation.",
+            id: "ali-vilab/VACE-Benchmark",
+        },
+        {
+            description: "A dataset of video generation style preferences.",
+            id: "Rapidata/sora-video-generation-style-likert-scoring",
+        },
+        {
+            description: "A dataset with videos and captions throughout the videos.",
+            id: "BestWishYsh/ChronoMagic",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "image-to-video-input.jpg",
+                type: "img",
+            },
+            {
+                label: "Optional Text Prompt",
+                content: "This penguin is dancing",
+                type: "text",
+            },
+        ],
+        outputs: [
+            {
+                filename: "image-to-video-output.gif",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [
+        {
+            description: "Fréchet Video Distance (FVD) measures the perceptual similarity between the distributions of generated videos and a set of real videos, assessing overall visual quality and temporal coherence of the video generated from an input image.",
+            id: "fvd",
+        },
+        {
+            description: "CLIP Score measures the semantic similarity between a textual prompt (if provided alongside the input image) and the generated video frames. It evaluates how well the video's generated content and motion align with the textual description, conditioned on the initial image.",
+            id: "clip_score",
+        },
+        {
+            description: "First Frame Fidelity, often measured using LPIPS (Learned Perceptual Image Patch Similarity), PSNR, or SSIM, quantifies how closely the first frame of the generated video matches the input conditioning image.",
+            id: "lpips",
+        },
+        {
+            description: "Identity Preservation Score measures the consistency of identity (e.g., a person's face or a specific object's characteristics) between the input image and throughout the generated video frames, often calculated using features from specialized models like face recognition (e.g., ArcFace) or re-identification models.",
+            id: "identity_preservation",
+        },
+        {
+            description: "Motion Score evaluates the quality, realism, and temporal consistency of motion in the video generated from a static image. This can be based on optical flow analysis (e.g., smoothness, magnitude), consistency of object trajectories, or specific motion plausibility assessments.",
+            id: "motion_score",
+        },
+    ],
+    models: [
+        {
+            description: "LTX-Video, a 13B parameter model for high quality video generation",
+            id: "Lightricks/LTX-Video-0.9.7-dev",
+        },
+        {
+            description: "A 14B parameter model for reference image controlled video generation",
+            id: "Wan-AI/Wan2.1-VACE-14B",
+        },
+        {
+            description: "An image-to-video generation model using FramePack F1 methodology with Hunyuan-DiT architecture",
+            id: "lllyasviel/FramePack_F1_I2V_HY_20250503",
+        },
+        {
+            description: "A distilled version of the LTX-Video-0.9.7-dev model for faster inference",
+            id: "Lightricks/LTX-Video-0.9.7-distilled",
+        },
+        {
+            description: "An image-to-video generation model by Skywork AI, 14B parameters, producing 720p videos.",
+            id: "Skywork/SkyReels-V2-I2V-14B-720P",
+        },
+        {
+            description: "Image-to-video variant of Tencent's HunyuanVideo.",
+            id: "tencent/HunyuanVideo-I2V",
+        },
+        {
+            description: "A 14B parameter model for 720p image-to-video generation by Wan-AI.",
+            id: "Wan-AI/Wan2.1-I2V-14B-720P",
+        },
+        {
+            description: "A Diffusers version of the Wan2.1-I2V-14B-720P model for 720p image-to-video generation.",
+            id: "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers",
+        },
+    ],
+    spaces: [
+        {
+            description: "An application to generate videos fast.",
+            id: "Lightricks/ltx-video-distilled",
+        },
+        {
+            description: "Generate videos with the FramePack-F1",
+            id: "linoyts/FramePack-F1",
+        },
+        {
+            description: "Generate videos with the FramePack",
+            id: "lisonallen/framepack-i2v",
+        },
+        {
+            description: "Wan2.1 with CausVid LoRA",
+            id: "multimodalart/wan2-1-fast",
+        },
+        {
+            description: "A demo for Stable Video Diffusion",
+            id: "multimodalart/stable-video-diffusion",
+        },
+    ],
+    summary: "Image-to-video models take a still image as input and generate a video. These models can be guided by text prompts to influence the content and style of the output video.",
+    widgetModels: [],
+    youtubeId: undefined,
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/inference.d.ts

@@ -0,0 +1,75 @@
+/**
+ * Inference code generated from the JSON schema spec in ./spec
+ *
+ * Using src/scripts/inference-codegen
+ */
+/**
+ * Inputs for Image To Video inference
+ */
+export interface ImageToVideoInput {
+    /**
+     * The input image data as a base64-encoded string. If no `parameters` are provided, you can
+     * also provide the image data as a raw bytes payload.
+     */
+    inputs: Blob;
+    /**
+     * Additional inference parameters for Image To Video
+     */
+    parameters?: ImageToVideoParameters;
+    [property: string]: unknown;
+}
+/**
+ * Additional inference parameters for Image To Video
+ */
+export interface ImageToVideoParameters {
+    /**
+     * For diffusion models. A higher guidance scale value encourages the model to generate
+     * videos closely linked to the text prompt at the expense of lower image quality.
+     */
+    guidance_scale?: number;
+    /**
+     * One prompt to guide what NOT to include in video generation.
+     */
+    negative_prompt?: string;
+    /**
+     * The num_frames parameter determines how many video frames are generated.
+     */
+    num_frames?: number;
+    /**
+     * The number of denoising steps. More denoising steps usually lead to a higher quality
+     * video at the expense of slower inference.
+     */
+    num_inference_steps?: number;
+    /**
+     * The text prompt to guide the video generation.
+     */
+    prompt?: string;
+    /**
+     * Seed for the random number generator.
+     */
+    seed?: number;
+    /**
+     * The size in pixel of the output video frames.
+     */
+    target_size?: TargetSize;
+    [property: string]: unknown;
+}
+/**
+ * The size in pixel of the output video frames.
+ */
+export interface TargetSize {
+    height: number;
+    width: number;
+    [property: string]: unknown;
+}
+/**
+ * Outputs of inference for the Image To Video task
+ */
+export interface ImageToVideoOutput {
+    /**
+     * The generated video returned as raw bytes in the payload.
+     */
+    video: unknown;
+    [property: string]: unknown;
+}
+//# sourceMappingURL=inference.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/inference.d.ts.map

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node_modules/@huggingface/tasks/dist/esm/tasks/image-to-video/inference.js

@@ -0,0 +1 @@
+export {};

node_modules/@huggingface/tasks/dist/esm/tasks/index.d.ts

@@ -0,0 +1,91 @@
+import type { PipelineType } from "../pipelines.js";
+export type * from "./audio-classification/inference.js";
+export type * from "./automatic-speech-recognition/inference.js";
+export type { ChatCompletionInput, ChatCompletionInputMessage, ChatCompletionInputMessageChunkType, ChatCompletionOutput, ChatCompletionOutputComplete, ChatCompletionOutputMessage, ChatCompletionStreamOutput, ChatCompletionStreamOutputChoice, ChatCompletionStreamOutputDelta, } from "./chat-completion/inference.js";
+export type * from "./document-question-answering/inference.js";
+export type * from "./feature-extraction/inference.js";
+export type * from "./fill-mask/inference.js";
+export type { ImageClassificationInput, ImageClassificationOutput, ImageClassificationOutputElement, ImageClassificationParameters, } from "./image-classification/inference.js";
+export type * from "./image-to-image/inference.js";
+export type { ImageToTextInput, ImageToTextOutput, ImageToTextParameters } from "./image-to-text/inference.js";
+export type * from "./image-segmentation/inference.js";
+export type { ImageToVideoInput, ImageToVideoOutput, ImageToVideoParameters } from "./image-to-video/inference.js";
+export type { ImageTextToImageInput, ImageTextToImageOutput, ImageTextToImageParameters, } from "./image-text-to-image/inference.js";
+export type { ImageTextToVideoInput, ImageTextToVideoOutput, ImageTextToVideoParameters, } from "./image-text-to-video/inference.js";
+export type * from "./object-detection/inference.js";
+export type * from "./depth-estimation/inference.js";
+export type * from "./question-answering/inference.js";
+export type * from "./sentence-similarity/inference.js";
+export type * from "./summarization/inference.js";
+export type * from "./table-question-answering/inference.js";
+export type { TextToImageInput, TextToImageOutput, TextToImageParameters } from "./text-to-image/inference.js";
+export type { TextToVideoParameters, TextToVideoOutput, TextToVideoInput } from "./text-to-video/inference.js";
+export type { TextToSpeechParameters, TextToSpeechInput, TextToSpeechOutput } from "./text-to-speech/inference.js";
+export type * from "./token-classification/inference.js";
+export type { TranslationInput, TranslationOutput } from "./translation/inference.js";
+export type { ClassificationOutputTransform, TextClassificationInput, TextClassificationOutput, TextClassificationOutputElement, TextClassificationParameters, } from "./text-classification/inference.js";
+export type { TextGenerationOutputFinishReason, TextGenerationOutputPrefillToken, TextGenerationInput, TextGenerationOutput, TextGenerationOutputDetails, TextGenerationInputGenerateParameters, TextGenerationOutputBestOfSequence, TextGenerationOutputToken, TextGenerationStreamOutputStreamDetails, TextGenerationStreamOutput, } from "./text-generation/inference.js";
+export type * from "./video-classification/inference.js";
+export type * from "./visual-question-answering/inference.js";
+export type * from "./zero-shot-classification/inference.js";
+export type * from "./zero-shot-image-classification/inference.js";
+export type { BoundingBox, ZeroShotObjectDetectionInput, ZeroShotObjectDetectionOutput, ZeroShotObjectDetectionOutputElement, } from "./zero-shot-object-detection/inference.js";
+import type { ModelLibraryKey } from "../model-libraries.js";
+/**
+ * Model libraries compatible with each ML task
+ */
+export declare const TASKS_MODEL_LIBRARIES: Record<PipelineType, ModelLibraryKey[]>;
+export declare const TASKS_DATA: Record<PipelineType, TaskData | undefined>;
+export interface ExampleRepo {
+    description: string;
+    id: string;
+}
+export type TaskDemoEntry = {
+    filename: string;
+    type: "audio";
+} | {
+    data: Array<{
+        label: string;
+        score: number;
+    }>;
+    type: "chart";
+} | {
+    filename: string;
+    type: "img";
+} | {
+    table: string[][];
+    type: "tabular";
+} | {
+    content: string;
+    label: string;
+    type: "text";
+} | {
+    text: string;
+    tokens: Array<{
+        end: number;
+        start: number;
+        type: string;
+    }>;
+    type: "text-with-tokens";
+};
+export interface TaskDemo {
+    inputs: TaskDemoEntry[];
+    outputs: TaskDemoEntry[];
+}
+export interface TaskData {
+    datasets: ExampleRepo[];
+    demo: TaskDemo;
+    id: PipelineType;
+    canonicalId?: PipelineType;
+    isPlaceholder?: boolean;
+    label: string;
+    libraries: ModelLibraryKey[];
+    metrics: ExampleRepo[];
+    models: ExampleRepo[];
+    spaces: ExampleRepo[];
+    summary: string;
+    widgetModels: string[];
+    youtubeId?: string;
+}
+export type TaskDataCustom = Omit<TaskData, "id" | "label" | "libraries">;
+//# sourceMappingURL=index.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/index.d.ts.map

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node_modules/@huggingface/tasks/dist/esm/tasks/index.js

@@ -0,0 +1,195 @@
+import { PIPELINE_DATA } from "../pipelines.js";
+import anyToAny from "./any-to-any/data.js";
+import audioClassification from "./audio-classification/data.js";
+import audioTextToText from "./audio-text-to-text/data.js";
+import audioToAudio from "./audio-to-audio/data.js";
+import automaticSpeechRecognition from "./automatic-speech-recognition/data.js";
+import documentQuestionAnswering from "./document-question-answering/data.js";
+import featureExtraction from "./feature-extraction/data.js";
+import fillMask from "./fill-mask/data.js";
+import imageClassification from "./image-classification/data.js";
+import imageFeatureExtraction from "./image-feature-extraction/data.js";
+import imageToImage from "./image-to-image/data.js";
+import imageToText from "./image-to-text/data.js";
+import imageTextToText from "./image-text-to-text/data.js";
+import imageTextToImage from "./image-text-to-image/data.js";
+import imageTextToVideo from "./image-text-to-video/data.js";
+import imageSegmentation from "./image-segmentation/data.js";
+import imageToVideo from "./image-to-video/data.js";
+import maskGeneration from "./mask-generation/data.js";
+import objectDetection from "./object-detection/data.js";
+import depthEstimation from "./depth-estimation/data.js";
+import placeholder from "./placeholder/data.js";
+import reinforcementLearning from "./reinforcement-learning/data.js";
+import questionAnswering from "./question-answering/data.js";
+import sentenceSimilarity from "./sentence-similarity/data.js";
+import summarization from "./summarization/data.js";
+import tableQuestionAnswering from "./table-question-answering/data.js";
+import tabularClassification from "./tabular-classification/data.js";
+import tabularRegression from "./tabular-regression/data.js";
+import textToImage from "./text-to-image/data.js";
+import textToSpeech from "./text-to-speech/data.js";
+import tokenClassification from "./token-classification/data.js";
+import translation from "./translation/data.js";
+import textClassification from "./text-classification/data.js";
+import textGeneration from "./text-generation/data.js";
+import textRanking from "./text-ranking/data.js";
+import textToVideo from "./text-to-video/data.js";
+import unconditionalImageGeneration from "./unconditional-image-generation/data.js";
+import videoClassification from "./video-classification/data.js";
+import visualDocumentRetrieval from "./visual-document-retrieval/data.js";
+import visualQuestionAnswering from "./visual-question-answering/data.js";
+import zeroShotClassification from "./zero-shot-classification/data.js";
+import zeroShotImageClassification from "./zero-shot-image-classification/data.js";
+import zeroShotObjectDetection from "./zero-shot-object-detection/data.js";
+import imageTo3D from "./image-to-3d/data.js";
+import textTo3D from "./text-to-3d/data.js";
+import keypointDetection from "./keypoint-detection/data.js";
+import videoTextToText from "./video-text-to-text/data.js";
+import videoToVideo from "./video-to-video/data.js";
+/**
+ * Model libraries compatible with each ML task
+ */
+export const TASKS_MODEL_LIBRARIES = {
+    "audio-classification": ["speechbrain", "transformers", "transformers.js"],
+    "audio-to-audio": ["asteroid", "fairseq", "speechbrain"],
+    "automatic-speech-recognition": ["espnet", "nemo", "speechbrain", "transformers", "transformers.js"],
+    "audio-text-to-text": ["transformers"],
+    "depth-estimation": ["transformers", "transformers.js"],
+    "document-question-answering": ["transformers", "transformers.js"],
+    "feature-extraction": ["sentence-transformers", "transformers", "transformers.js"],
+    "fill-mask": ["transformers", "transformers.js"],
+    "graph-ml": ["transformers"],
+    "image-classification": ["keras", "timm", "transformers", "transformers.js"],
+    "image-feature-extraction": ["timm", "transformers"],
+    "image-segmentation": ["transformers", "transformers.js"],
+    "image-text-to-text": ["transformers"],
+    "image-text-to-image": ["diffusers"],
+    "image-text-to-video": ["diffusers"],
+    "image-to-image": ["diffusers", "transformers", "transformers.js"],
+    "image-to-text": ["transformers", "transformers.js"],
+    "image-to-video": ["diffusers"],
+    "keypoint-detection": ["transformers"],
+    "video-classification": ["transformers"],
+    "mask-generation": ["transformers"],
+    "multiple-choice": ["transformers"],
+    "object-detection": ["transformers", "transformers.js", "ultralytics"],
+    other: [],
+    "question-answering": ["adapter-transformers", "allennlp", "transformers", "transformers.js"],
+    robotics: [],
+    "reinforcement-learning": ["transformers", "stable-baselines3", "ml-agents", "sample-factory"],
+    "sentence-similarity": ["sentence-transformers", "spacy", "transformers.js"],
+    summarization: ["transformers", "transformers.js"],
+    "table-question-answering": ["transformers"],
+    "table-to-text": ["transformers"],
+    "tabular-classification": ["sklearn"],
+    "tabular-regression": ["sklearn"],
+    "tabular-to-text": ["transformers"],
+    "text-classification": ["adapter-transformers", "setfit", "spacy", "transformers", "transformers.js"],
+    "text-generation": ["transformers", "transformers.js"],
+    "text-ranking": ["sentence-transformers", "transformers"],
+    "text-retrieval": [],
+    "text-to-image": ["diffusers"],
+    "text-to-speech": ["espnet", "tensorflowtts", "transformers", "transformers.js"],
+    "text-to-audio": ["transformers", "transformers.js"],
+    "text-to-video": ["diffusers"],
+    "time-series-forecasting": [],
+    "token-classification": [
+        "adapter-transformers",
+        "flair",
+        "spacy",
+        "span-marker",
+        "stanza",
+        "transformers",
+        "transformers.js",
+    ],
+    translation: ["transformers", "transformers.js"],
+    "unconditional-image-generation": ["diffusers"],
+    "video-text-to-text": ["transformers"],
+    "visual-question-answering": ["transformers", "transformers.js"],
+    "voice-activity-detection": [],
+    "zero-shot-classification": ["transformers", "transformers.js"],
+    "zero-shot-image-classification": ["transformers", "transformers.js"],
+    "zero-shot-object-detection": ["transformers", "transformers.js"],
+    "text-to-3d": ["diffusers"],
+    "image-to-3d": ["diffusers"],
+    "any-to-any": ["transformers"],
+    "visual-document-retrieval": ["transformers"],
+    "video-to-video": ["diffusers"],
+};
+/**
+ * Return the whole TaskData object for a certain task.
+ * If the partialTaskData argument is left undefined,
+ * the default placeholder data will be used.
+ */
+function getData(type, partialTaskData = placeholder) {
+    return {
+        ...partialTaskData,
+        id: type,
+        label: PIPELINE_DATA[type].name,
+        libraries: TASKS_MODEL_LIBRARIES[type],
+    };
+}
+// To make comparisons easier, task order is the same as in const.ts
+// Tasks set to undefined won't have an associated task page.
+// Tasks that call getData() without the second argument will
+// have a "placeholder" page.
+export const TASKS_DATA = {
+    "any-to-any": getData("any-to-any", anyToAny),
+    "audio-classification": getData("audio-classification", audioClassification),
+    "audio-to-audio": getData("audio-to-audio", audioToAudio),
+    "audio-text-to-text": getData("audio-text-to-text", audioTextToText),
+    "automatic-speech-recognition": getData("automatic-speech-recognition", automaticSpeechRecognition),
+    "depth-estimation": getData("depth-estimation", depthEstimation),
+    "document-question-answering": getData("document-question-answering", documentQuestionAnswering),
+    "visual-document-retrieval": getData("visual-document-retrieval", visualDocumentRetrieval),
+    "feature-extraction": getData("feature-extraction", featureExtraction),
+    "fill-mask": getData("fill-mask", fillMask),
+    "graph-ml": undefined,
+    "image-classification": getData("image-classification", imageClassification),
+    "image-feature-extraction": getData("image-feature-extraction", imageFeatureExtraction),
+    "image-segmentation": getData("image-segmentation", imageSegmentation),
+    "image-to-image": getData("image-to-image", imageToImage),
+    "image-text-to-text": getData("image-text-to-text", imageTextToText),
+    "image-text-to-image": getData("image-text-to-image", imageTextToImage),
+    "image-text-to-video": getData("image-text-to-video", imageTextToVideo),
+    "image-to-text": getData("image-to-text", imageToText),
+    "image-to-video": getData("image-to-video", imageToVideo),
+    "keypoint-detection": getData("keypoint-detection", keypointDetection),
+    "mask-generation": getData("mask-generation", maskGeneration),
+    "multiple-choice": undefined,
+    "object-detection": getData("object-detection", objectDetection),
+    "video-classification": getData("video-classification", videoClassification),
+    other: undefined,
+    "question-answering": getData("question-answering", questionAnswering),
+    "reinforcement-learning": getData("reinforcement-learning", reinforcementLearning),
+    robotics: undefined,
+    "sentence-similarity": getData("sentence-similarity", sentenceSimilarity),
+    summarization: getData("summarization", summarization),
+    "table-question-answering": getData("table-question-answering", tableQuestionAnswering),
+    "table-to-text": undefined,
+    "tabular-classification": getData("tabular-classification", tabularClassification),
+    "tabular-regression": getData("tabular-regression", tabularRegression),
+    "tabular-to-text": undefined,
+    "text-classification": getData("text-classification", textClassification),
+    "text-generation": getData("text-generation", textGeneration),
+    "text-ranking": getData("text-ranking", textRanking),
+    "text-retrieval": undefined,
+    "text-to-image": getData("text-to-image", textToImage),
+    "text-to-speech": getData("text-to-speech", textToSpeech),
+    "text-to-audio": undefined,
+    "text-to-video": getData("text-to-video", textToVideo),
+    "time-series-forecasting": undefined,
+    "token-classification": getData("token-classification", tokenClassification),
+    translation: getData("translation", translation),
+    "unconditional-image-generation": getData("unconditional-image-generation", unconditionalImageGeneration),
+    "video-text-to-text": getData("video-text-to-text", videoTextToText),
+    "video-to-video": getData("video-to-video", videoToVideo),
+    "visual-question-answering": getData("visual-question-answering", visualQuestionAnswering),
+    "voice-activity-detection": undefined,
+    "zero-shot-classification": getData("zero-shot-classification", zeroShotClassification),
+    "zero-shot-image-classification": getData("zero-shot-image-classification", zeroShotImageClassification),
+    "zero-shot-object-detection": getData("zero-shot-object-detection", zeroShotObjectDetection),
+    "text-to-3d": getData("text-to-3d", textTo3D),
+    "image-to-3d": getData("image-to-3d", imageTo3D),
+};

node_modules/@huggingface/tasks/dist/esm/tasks/keypoint-detection/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/keypoint-detection/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/keypoint-detection/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAqDf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/keypoint-detection/data.js

@@ -0,0 +1,55 @@
+const taskData = {
+    datasets: [
+        {
+            description: "A dataset of hand keypoints of over 500k examples.",
+            id: "Vincent-luo/hagrid-mediapipe-hands",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "keypoint-detection-input.png",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                filename: "keypoint-detection-output.png",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [],
+    models: [
+        {
+            description: "A robust keypoint detection model.",
+            id: "magic-leap-community/superpoint",
+        },
+        {
+            description: "A robust keypoint matching model.",
+            id: "magic-leap-community/superglue_outdoor",
+        },
+        {
+            description: "Strong keypoint detection model used to detect human pose.",
+            id: "qualcomm/RTMPose-Body2d",
+        },
+        {
+            description: "Powerful keypoint matching model.",
+            id: "ETH-CVG/lightglue_disk",
+        },
+    ],
+    spaces: [
+        {
+            description: "An application that detects hand keypoints in real-time.",
+            id: "datasciencedojo/Hand-Keypoint-Detection-Realtime",
+        },
+        {
+            description: "An application for keypoint detection and matching.",
+            id: "ETH-CVG/LightGlue",
+        },
+    ],
+    summary: "Keypoint detection is the task of identifying meaningful distinctive points or features in an image.",
+    widgetModels: [],
+    youtubeId: "",
+};
+export default taskData;

node_modules/@huggingface/tasks/dist/esm/tasks/mask-generation/data.d.ts

@@ -0,0 +1,4 @@
+import type { TaskDataCustom } from "../index.js";
+declare const taskData: TaskDataCustom;
+export default taskData;
+//# sourceMappingURL=data.d.ts.map

node_modules/@huggingface/tasks/dist/esm/tasks/mask-generation/data.d.ts.map

@@ -0,0 +1 @@
+{"version":3,"file":"data.d.ts","sourceRoot":"","sources":["../../../../src/tasks/mask-generation/data.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,cAAc,EAAE,MAAM,aAAa,CAAC;AAElD,QAAA,MAAM,QAAQ,EAAE,cAgEf,CAAC;AAEF,eAAe,QAAQ,CAAC"}

node_modules/@huggingface/tasks/dist/esm/tasks/mask-generation/data.js

@@ -0,0 +1,64 @@
+const taskData = {
+    datasets: [
+        {
+            description: "Widely used benchmark dataset for multiple Vision tasks.",
+            id: "merve/coco2017",
+        },
+        {
+            description: "Medical Imaging dataset of the Human Brain for segmentation and mask generating tasks",
+            id: "rocky93/BraTS_segmentation",
+        },
+    ],
+    demo: {
+        inputs: [
+            {
+                filename: "mask-generation-input.png",
+                type: "img",
+            },
+        ],
+        outputs: [
+            {
+                filename: "mask-generation-output.png",
+                type: "img",
+            },
+        ],
+    },
+    metrics: [
+        {
+            description: "IoU is used to measure the overlap between predicted mask and the ground truth mask.",
+            id: "Intersection over Union (IoU)",
+        },
+    ],
+    models: [
+        {
+            description: "Small yet powerful mask generation model.",
+            id: "Zigeng/SlimSAM-uniform-50",
+        },
+        {
+            description: "Very strong mask generation model.",
+            id: "facebook/sam2-hiera-large",
+        },
+    ],
+    spaces: [
+        {
+            description: "An application that combines a mask generation model with a zero-shot object detection model for text-guided image segmentation.",
+            id: "merve/OWLSAM2",
+        },
+        {
+            description: "An application that compares the performance of a large and a small mask generation model.",
+            id: "merve/slimsam",
+        },
+        {
+            description: "An application based on an improved mask generation model.",
+            id: "SkalskiP/segment-anything-model-2",
+        },
+        {
+            description: "An application to remove objects from videos using mask generation models.",
+            id: "SkalskiP/SAM_and_ProPainter",
+        },
+    ],
+    summary: "Mask generation is the task of generating masks that identify a specific object or region of interest in a given image. Masks are often used in segmentation tasks, where they provide a precise way to isolate the object of interest for further processing or analysis.",
+    widgetModels: [],
+    youtubeId: "",
+};
+export default taskData;