diff --git a/src/pipelines.js b/src/pipelines.js
index afb627a4a..3f6b73765 100644
--- a/src/pipelines.js
+++ b/src/pipelines.js
@@ -1,15 +1,15 @@
 /**
  * @file Pipelines provide a high-level, easy to use, API for running machine learning models.
- * 
+ *
  * **Example:** Instantiate pipeline using the `pipeline` function.
  * ```javascript
  * import { pipeline } from '@huggingface/transformers';
- * 
+ *
  * const classifier = await pipeline('sentiment-analysis');
  * const output = await classifier('I love transformers!');
  * // [{'label': 'POSITIVE', 'score': 0.999817686}]
  * ```
- * 
+ *
  * @module pipelines
  */
 
@@ -78,7 +78,7 @@ import { RawImage } from './utils/image.js';
 
 
 /**
- * @typedef {string | RawImage | URL} ImageInput
+ * @typedef {string | RawImage | URL | Blob | HTMLCanvasElement | OffscreenCanvas} ImageInput
  * @typedef {ImageInput|ImageInput[]} ImagePipelineInputs
  */
 
@@ -152,7 +152,7 @@ function get_bounding_box(box, asInteger) {
 /**
  * @callback DisposeType Disposes the item.
  * @returns {Promise<void>} A promise that resolves when the item has been disposed.
- * 
+ *
  * @typedef {Object} Disposable
  * @property {DisposeType} dispose A promise that resolves when the pipeline has been disposed.
  */
@@ -189,7 +189,7 @@ export class Pipeline extends Callable {
  * @property {string} task The task of the pipeline. Useful for specifying subtasks.
  * @property {PreTrainedModel} model The model used by the pipeline.
  * @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline.
- * 
+ *
  * @typedef {ModelTokenizerConstructorArgs} TextPipelineConstructorArgs An object used to instantiate a text-based pipeline.
  */
 
@@ -198,7 +198,7 @@ export class Pipeline extends Callable {
  * @property {string} task The task of the pipeline. Useful for specifying subtasks.
  * @property {PreTrainedModel} model The model used by the pipeline.
  * @property {Processor} processor The processor used by the pipeline.
- * 
+ *
  * @typedef {ModelProcessorConstructorArgs} AudioPipelineConstructorArgs An object used to instantiate an audio-based pipeline.
  * @typedef {ModelProcessorConstructorArgs} ImagePipelineConstructorArgs An object used to instantiate an image-based pipeline.
  */
@@ -210,7 +210,7 @@ export class Pipeline extends Callable {
  * @property {PreTrainedModel} model The model used by the pipeline.
  * @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline.
  * @property {Processor} processor The processor used by the pipeline.
- * 
+ *
  * @typedef {ModelTokenizerProcessorConstructorArgs} TextAudioPipelineConstructorArgs An object used to instantiate a text- and audio-based pipeline.
  * @typedef {ModelTokenizerProcessorConstructorArgs} TextImagePipelineConstructorArgs An object used to instantiate a text- and image-based pipeline.
  */
@@ -220,15 +220,15 @@ export class Pipeline extends Callable {
  * @property {string} label The label predicted.
  * @property {number} score The corresponding probability.
  * @typedef {TextClassificationSingle[]} TextClassificationOutput
- * 
+ *
  * @typedef {Object} TextClassificationPipelineOptions Parameters specific to text classification pipelines.
  * @property {number} [top_k=1] The number of top predictions to be returned.
- * 
+ *
  * @callback TextClassificationPipelineCallback Classify the text(s) given as inputs.
  * @param {string|string[]} texts The input text(s) to be classified.
  * @param {TextClassificationPipelineOptions} [options] The options to use for text classification.
  * @returns {Promise<TextClassificationOutput|TextClassificationOutput[]>} An array or object containing the predicted labels and scores.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & TextClassificationPipelineCallback & Disposable} TextClassificationPipelineType
  */
 
@@ -241,7 +241,7 @@ export class Pipeline extends Callable {
  * const output = await classifier('I love transformers!');
  * // [{ label: 'POSITIVE', score: 0.999788761138916 }]
  * ```
- * 
+ *
  * **Example:** Multilingual sentiment-analysis w/ `Xenova/bert-base-multilingual-uncased-sentiment` (and return top 5 classes).
  * ```javascript
  * const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
@@ -254,7 +254,7 @@ export class Pipeline extends Callable {
  * //   { label: '2 stars', score: 0.0009423971059732139 }
  * // ]
  * ```
- * 
+ *
  * **Example:** Toxic comment classification w/ `Xenova/toxic-bert` (and return all classes).
  * ```javascript
  * const classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
@@ -339,21 +339,21 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
  * @property {number} [start] The index of the start of the corresponding entity in the sentence.
  * @property {number} [end] The index of the end of the corresponding entity in the sentence.
  * @typedef {TokenClassificationSingle[]} TokenClassificationOutput
- * 
+ *
  * @typedef {Object} TokenClassificationPipelineOptions Parameters specific to token classification pipelines.
  * @property {string[]} [ignore_labels] A list of labels to ignore.
- * 
+ *
  * @callback TokenClassificationPipelineCallback Classify each token of the text(s) given as inputs.
  * @param {string|string[]} texts One or several texts (or one list of texts) for token classification.
  * @param {TokenClassificationPipelineOptions} [options] The options to use for token classification.
  * @returns {Promise<TokenClassificationOutput|TokenClassificationOutput[]>} The result.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & TokenClassificationPipelineCallback & Disposable} TokenClassificationPipelineType
  */
 
 /**
  * Named Entity Recognition pipeline using any `ModelForTokenClassification`.
- * 
+ *
  * **Example:** Perform named entity recognition with `Xenova/bert-base-NER`.
  * ```javascript
  * const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
@@ -363,7 +363,7 @@ export class TextClassificationPipeline extends (/** @type {new (options: TextPi
  * //   { entity: 'B-LOC', score: 0.9994474053382874, index: 9, word: 'London' }
  * // ]
  * ```
- * 
+ *
  * **Example:** Perform named entity recognition with `Xenova/bert-base-NER` (and return all labels).
  * ```javascript
  * const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
@@ -459,22 +459,22 @@ export class TokenClassificationPipeline extends (/** @type {new (options: TextP
  * @property {number} [start] The character start index of the answer (in the tokenized version of the input).
  * @property {number} [end] The character end index of the answer (in the tokenized version of the input).
  * @property {string} answer The answer to the question.
- * 
+ *
  * @typedef {Object} QuestionAnsweringPipelineOptions Parameters specific to question answering pipelines.
  * @property {number} [top_k=1] The number of top answer predictions to be returned.
- * 
+ *
  * @callback QuestionAnsweringPipelineCallback Answer the question(s) given as inputs by using the context(s).
  * @param {string|string[]} question One or several question(s) (must be used in conjunction with the `context` argument).
  * @param {string|string[]} context One or several context(s) associated with the question(s) (must be used in conjunction with the `question` argument).
  * @param {QuestionAnsweringPipelineOptions} [options] The options to use for question answering.
  * @returns {Promise<QuestionAnsweringOutput|QuestionAnsweringOutput[]>} An array or object containing the predicted answers and scores.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & QuestionAnsweringPipelineCallback & Disposable} QuestionAnsweringPipelineType
  */
 
 /**
  * Question Answering pipeline using any `ModelForQuestionAnswering`.
- * 
+ *
  * **Example:** Run question answering with `Xenova/distilbert-base-uncased-distilled-squad`.
  * ```javascript
  * const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
@@ -599,10 +599,10 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
  * @property {number} token The predicted token id (to replace the masked one).
  * @property {string} token_str The predicted token (to replace the masked one).
  * @typedef {FillMaskSingle[]} FillMaskOutput
- * 
+ *
  * @typedef {Object} FillMaskPipelineOptions Parameters specific to fill mask pipelines.
  * @property {number} [top_k=5] When passed, overrides the number of predictions to return.
- * 
+ *
  * @callback FillMaskPipelineCallback Fill the masked token in the text(s) given as inputs.
  * @param {string|string[]} texts One or several texts (or one list of prompts) with masked tokens.
  * @param {FillMaskPipelineOptions} [options] The options to use for masked language modelling.
@@ -610,13 +610,13 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
  * and the sequence with the predicted token filled in, or an array of such arrays (one for each input text).
  * If only one input text is given, the output will be an array of objects.
  * @throws {Error} When the mask token is not found in the input text.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & FillMaskPipelineCallback & Disposable} FillMaskPipelineType
  */
 
 /**
  * Masked language modeling prediction pipeline using any `ModelWithLMHead`.
- * 
+ *
  * **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-uncased`.
  * ```javascript
  * const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
@@ -629,7 +629,7 @@ export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPip
  * //   { token_str: 'life', score: 0.01859794743359089, token: 1297, sequence: 'The goal of life is life.' }
  * // ]
  * ```
- * 
+ *
  * **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-cased` (and return top result).
  * ```javascript
  * const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
@@ -706,18 +706,18 @@ export class FillMaskPipeline extends (/** @type {new (options: TextPipelineCons
  * @typedef {Object} Text2TextGenerationSingle
  * @property {string} generated_text The generated text.
  * @typedef {Text2TextGenerationSingle[]} Text2TextGenerationOutput
- * 
+ *
  * @callback Text2TextGenerationPipelineCallback Generate the output text(s) using text(s) given as inputs.
  * @param {string|string[]} texts Input text for the encoder.
  * @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<Text2TextGenerationOutput|Text2TextGenerationOutput[]>}
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & Text2TextGenerationPipelineCallback & Disposable} Text2TextGenerationPipelineType
  */
 
 /**
  * Text2TextGenerationPipeline class for generating text using a model that performs text-to-text generation tasks.
- * 
+ *
  * **Example:** Text-to-text generation w/ `Xenova/LaMini-Flan-T5-783M`.
  * ```javascript
  * const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
@@ -793,18 +793,18 @@ export class Text2TextGenerationPipeline extends (/** @type {new (options: TextP
  * @typedef {Object} SummarizationSingle
  * @property {string} summary_text The summary text.
  * @typedef {SummarizationSingle[]} SummarizationOutput
- * 
+ *
  * @callback SummarizationPipelineCallback Summarize the text(s) given as inputs.
  * @param {string|string[]} texts One or several articles (or one list of articles) to summarize.
  * @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<SummarizationOutput|SummarizationOutput[]>}
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & SummarizationPipelineCallback & Disposable} SummarizationPipelineType
  */
 
 /**
  * A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline.
- * 
+ *
  * **Example:** Summarization w/ `Xenova/distilbart-cnn-6-6`.
  * ```javascript
  * const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
@@ -840,23 +840,23 @@ export class SummarizationPipeline extends (/** @type {new (options: TextPipelin
  * @typedef {Object} TranslationSingle
  * @property {string} translation_text The translated text.
  * @typedef {TranslationSingle[]} TranslationOutput
- * 
+ *
  * @callback TranslationPipelineCallback Translate the text(s) given as inputs.
  * @param {string|string[]} texts Texts to be translated.
  * @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<TranslationOutput|TranslationOutput[]>}
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & TranslationPipelineCallback & Disposable} TranslationPipelineType
  */
 
 /**
  * Translates text from one language to another.
- * 
+ *
  * **Example:** Multilingual translation w/ `Xenova/nllb-200-distilled-600M`.
- * 
+ *
  * See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
  * for the full list of languages and their corresponding codes.
- * 
+ *
  * ```javascript
  * const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
  * const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', {
@@ -865,12 +865,12 @@ export class SummarizationPipeline extends (/** @type {new (options: TextPipelin
  * });
  * // [{ translation_text: 'La vie est comme une boîte à chocolat.' }]
  * ```
- * 
+ *
  * **Example:** Multilingual translation w/ `Xenova/m2m100_418M`.
- * 
+ *
  * See [here](https://huggingface.co/facebook/m2m100_418M#languages-covered)
  * for the full list of languages and their corresponding codes.
- * 
+ *
  * ```javascript
  * const translator = await pipeline('translation', 'Xenova/m2m100_418M');
  * const output = await translator('生活就像一盒巧克力。', {
@@ -879,12 +879,12 @@ export class SummarizationPipeline extends (/** @type {new (options: TextPipelin
  * });
  * // [{ translation_text: 'Life is like a box of chocolate.' }]
  * ```
- * 
+ *
  * **Example:** Multilingual translation w/ `Xenova/mbart-large-50-many-to-many-mmt`.
- * 
+ *
  * See [here](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered)
  * for the full list of languages and their corresponding codes.
- * 
+ *
  * ```javascript
  * const translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
  * const output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', {
@@ -913,21 +913,21 @@ function isChat(x) {
 
 /**
  * @typedef {import('./tokenizers.js').Message[]} Chat
- * 
+ *
  * @typedef {Object} TextGenerationSingle
  * @property {string|Chat} generated_text The generated text.
  * @typedef {TextGenerationSingle[]} TextGenerationOutput
- * 
+ *
  * @typedef {Object} TextGenerationSpecificParams Parameters specific to text-generation pipelines.
  * @property {boolean} [add_special_tokens] Whether or not to add special tokens when tokenizing the sequences.
  * @property {boolean} [return_full_text=true] If set to `false` only added text is returned, otherwise the full text is returned.
  * @typedef {import('./generation/configuration_utils.js').GenerationConfig & TextGenerationSpecificParams} TextGenerationConfig
- * 
+ *
  * @callback TextGenerationPipelineCallback Complete the prompt(s) given as inputs.
  * @param {string|string[]|Chat|Chat[]} texts One or several prompts (or one list of prompts) to complete.
  * @param {Partial<TextGenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<TextGenerationOutput|TextGenerationOutput[]>} An array or object containing the generated texts.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & TextGenerationPipelineCallback & Disposable} TextGenerationPipelineType
  */
 
@@ -935,7 +935,7 @@ function isChat(x) {
  * Language generation pipeline using any `ModelWithLMHead` or `ModelForCausalLM`.
  * This pipeline predicts the words that will follow a specified text prompt.
  * NOTE: For the full list of generation parameters, see [`GenerationConfig`](./utils/generation#module_utils/generation.GenerationConfig).
- * 
+ *
  * **Example:** Text generation with `Xenova/distilgpt2` (default settings).
  * ```javascript
  * const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
@@ -943,7 +943,7 @@ function isChat(x) {
  * const output = await generator(text);
  * // [{ generated_text: "I enjoy walking with my cute dog, and I love to play with the other dogs." }]
  * ```
- * 
+ *
  * **Example:** Text generation with `Xenova/distilgpt2` (custom settings).
  * ```javascript
  * const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
@@ -962,7 +962,7 @@ function isChat(x) {
  * //   "generated_text": "Once upon a time, there was an abundance of information about the most important and influential"
  * // }]
  * ```
- * 
+ *
  * **Example:** Run code generation with `Xenova/codegen-350M-mono`.
  * ```javascript
  * const generator = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
@@ -1081,7 +1081,7 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
  * @property {string} sequence The sequence for which this is the output.
  * @property {string[]} labels The labels sorted by order of likelihood.
  * @property {number[]} scores The probabilities for each of the labels.
- * 
+ *
  * @typedef {Object} ZeroShotClassificationPipelineOptions Parameters specific to zero-shot classification pipelines.
  * @property {string} [hypothesis_template="This example is {}."] The template used to turn each
  * candidate label into an NLI-style hypothesis. The candidate label will replace the {} placeholder.
@@ -1089,14 +1089,14 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
  * If `false`, the scores are normalized such that the sum of the label likelihoods for each sequence
  * is 1. If `true`, the labels are considered independent and probabilities are normalized for each
  * candidate by doing a softmax of the entailment score vs. the contradiction score.
- * 
+ *
  * @callback ZeroShotClassificationPipelineCallback Classify the sequence(s) given as inputs.
  * @param {string|string[]} texts The sequence(s) to classify, will be truncated if the model input is too large.
  * @param {string|string[]} candidate_labels The set of possible class labels to classify each sequence into.
  * Can be a single label, a string of comma-separated labels, or a list of labels.
  * @param {ZeroShotClassificationPipelineOptions} [options] The options to use for zero-shot classification.
  * @returns {Promise<ZeroShotClassificationOutput|ZeroShotClassificationOutput[]>} An array or object containing the predicted labels and scores.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & ZeroShotClassificationPipelineCallback & Disposable} ZeroShotClassificationPipelineType
  */
 
@@ -1105,7 +1105,7 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
  * trained on NLI (natural language inference) tasks. Equivalent of `text-classification`
  * pipelines, but these models don't require a hardcoded number of potential classes, they
  * can be chosen at runtime. It usually means it's slower but it is **much** more flexible.
- * 
+ *
  * **Example:** Zero shot classification with `Xenova/mobilebert-uncased-mnli`.
  * ```javascript
  * const classifier = await pipeline('zero-shot-classification', 'Xenova/mobilebert-uncased-mnli');
@@ -1118,7 +1118,7 @@ export class TextGenerationPipeline extends (/** @type {new (options: TextPipeli
  * //   scores: [ 0.5562091040482018, 0.1843621307860853, 0.13942646639336376, 0.12000229877234923 ]
  * // }
  * ```
- * 
+ *
  * **Example:** Zero shot classification with `Xenova/nli-deberta-v3-xsmall` (multi-label).
  * ```javascript
  * const classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall');
@@ -1232,20 +1232,20 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * @property {'none'|'mean'|'cls'} [pooling="none"] The pooling method to use.
  * @property {boolean} [normalize=false] Whether or not to normalize the embeddings in the last dimension.
  * @property {boolean} [quantize=false] Whether or not to quantize the embeddings.
- * @property {'binary'|'ubinary'} [precision='binary'] The precision to use for quantization. 
- * 
+ * @property {'binary'|'ubinary'} [precision='binary'] The precision to use for quantization.
+ *
  * @callback FeatureExtractionPipelineCallback Extract the features of the input(s).
  * @param {string|string[]} texts One or several texts (or one list of texts) to get the features of.
  * @param {FeatureExtractionPipelineOptions} [options] The options to use for feature extraction.
  * @returns {Promise<Tensor>} The features computed by the model.
- * 
+ *
  * @typedef {TextPipelineConstructorArgs & FeatureExtractionPipelineCallback & Disposable} FeatureExtractionPipelineType
  */
 
 /**
  * Feature extraction pipeline using no model head. This pipeline extracts the hidden
  * states from the base transformer, which can be used as features in downstream tasks.
- * 
+ *
  * **Example:** Run feature extraction with `bert-base-uncased` (without pooling/normalization).
  * ```javascript
  * const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
@@ -1256,7 +1256,7 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * //   dims: [1, 8, 768]
  * // }
  * ```
- * 
+ *
  * **Example:** Run feature extraction with `bert-base-uncased` (with pooling/normalization).
  * ```javascript
  * const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
@@ -1267,7 +1267,7 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * //   dims: [1, 768]
  * // }
  * ```
- * 
+ *
  * **Example:** Calculating embeddings with `sentence-transformers` models.
  * ```javascript
  * const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
@@ -1348,19 +1348,19 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
 /**
  * @typedef {Object} ImageFeatureExtractionPipelineOptions Parameters specific to image feature extraction pipelines.
  * @property {boolean} [pool=null] Whether or not to return the pooled output. If set to `false`, the model will return the raw hidden states.
- * 
+ *
  * @callback ImageFeatureExtractionPipelineCallback Extract the features of the input(s).
  * @param {ImagePipelineInputs} images One or several images (or one list of images) to get the features of.
  * @param {ImageFeatureExtractionPipelineOptions} [options] The options to use for image feature extraction.
  * @returns {Promise<Tensor>} The image features computed by the model.
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & ImageFeatureExtractionPipelineCallback & Disposable} ImageFeatureExtractionPipelineType
  */
 
 /**
  * Image feature extraction pipeline using no model head. This pipeline extracts the hidden
  * states from the base transformer, which can be used as features in downstream tasks.
- * 
+ *
  * **Example:** Perform image feature extraction with `Xenova/vit-base-patch16-224-in21k`.
  * ```javascript
  * const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k');
@@ -1373,7 +1373,7 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
  * //   size: 151296
  * // }
  * ```
- * 
+ *
  * **Example:** Compute image embeddings with `Xenova/clip-vit-base-patch32`.
  * ```javascript
  * const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/clip-vit-base-patch32');
@@ -1429,12 +1429,12 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
  * @property {string} label The label predicted.
  * @property {number} score The corresponding probability.
  * @typedef {AudioClassificationSingle[]} AudioClassificationOutput
- * 
+ *
  * @typedef {Object} AudioClassificationPipelineOptions Parameters specific to audio classification pipelines.
  * @property {number} [top_k=5] The number of top labels that will be returned by the pipeline.
  * If the provided number is `null` or higher than the number of labels available in the model configuration,
  * it will default to the number of labels.
- * 
+ *
  * @callback AudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
  * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
  * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
@@ -1443,14 +1443,14 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
  * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
  * @param {AudioClassificationPipelineOptions} [options] The options to use for audio classification.
  * @returns {Promise<AudioClassificationOutput|AudioClassificationOutput[]>} An array or object containing the predicted labels and scores.
- * 
+ *
  * @typedef {AudioPipelineConstructorArgs & AudioClassificationPipelineCallback & Disposable} AudioClassificationPipelineType
  */
 
 /**
  * Audio classification pipeline using any `AutoModelForAudioClassification`.
  * This pipeline predicts the class of a raw waveform or an audio file.
- * 
+ *
  * **Example:** Perform audio classification with `Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech`.
  * ```javascript
  * const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech');
@@ -1461,7 +1461,7 @@ export class ImageFeatureExtractionPipeline extends (/** @type {new (options: Im
  * //   { label: 'female', score: 0.001845747814513743 }
  * // ]
  * ```
- * 
+ *
  * **Example:** Perform audio classification with `Xenova/ast-finetuned-audioset-10-10-0.4593` and return top 4 results.
  * ```javascript
  * const classifier = await pipeline('audio-classification', 'Xenova/ast-finetuned-audioset-10-10-0.4593');
@@ -1526,12 +1526,12 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
  * @typedef {Object} ZeroShotAudioClassificationOutput
  * @property {string} label The label identified by the model. It is one of the suggested `candidate_label`.
  * @property {number} score The score attributed by the model for that label (between 0 and 1).
- * 
+ *
  * @typedef {Object} ZeroShotAudioClassificationPipelineOptions Parameters specific to zero-shot audio classification pipelines.
  * @property {string} [hypothesis_template="This is a sound of {}."] The sentence used in conjunction with `candidate_labels`
  * to attempt the audio classification by replacing the placeholder with the candidate_labels.
  * Then likelihood is estimated by using `logits_per_audio`.
- * 
+ *
  * @callback ZeroShotAudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
  * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
  * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
@@ -1541,14 +1541,14 @@ export class AudioClassificationPipeline extends (/** @type {new (options: Audio
  * @param {string[]} candidate_labels The candidate labels for this audio.
  * @param {ZeroShotAudioClassificationPipelineOptions} [options] The options to use for zero-shot audio classification.
  * @returns {Promise<ZeroShotAudioClassificationOutput[]|ZeroShotAudioClassificationOutput[][]>} An array of objects containing the predicted labels and scores.
- * 
+ *
  * @typedef {TextAudioPipelineConstructorArgs & ZeroShotAudioClassificationPipelineCallback & Disposable} ZeroShotAudioClassificationPipelineType
  */
 
 /**
  * Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
  * provide an audio and a set of `candidate_labels`.
- * 
+ *
  * **Example**: Perform zero-shot audio classification with `Xenova/clap-htsat-unfused`.
  * ```javascript
  * const classifier = await pipeline('zero-shot-audio-classification', 'Xenova/clap-htsat-unfused');
@@ -1581,7 +1581,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
             audio = [/** @type {AudioInput} */ (audio)];
         }
 
-        // Insert label into hypothesis template 
+        // Insert label into hypothesis template
         const texts = candidate_labels.map(
             x => hypothesis_template.replace('{}', x)
         );
@@ -1625,7 +1625,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * @property {string} text The recognized text.
  * @property {Chunk[]} [chunks] When using `return_timestamps`, the `chunks` will become a list
  * containing all the various text chunks identified by the model.
- * 
+ *
  * @typedef {Object} AutomaticSpeechRecognitionSpecificParams Parameters specific to automatic-speech-recognition pipelines.
  * @property {boolean|'word'} [return_timestamps] Whether to return timestamps or not. Default is `false`.
  * @property {number} [chunk_length_s] The length of audio chunks to process in seconds. Default is 0 (no chunking).
@@ -1635,7 +1635,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * @property {string} [task] The task to perform. Default is `null`, meaning it should be auto-detected.
  * @property {number} [num_frames] The number of frames in the input audio.
  * @typedef {import('./generation/configuration_utils.js').GenerationConfig & AutomaticSpeechRecognitionSpecificParams} AutomaticSpeechRecognitionConfig
- * 
+ *
  * @callback AutomaticSpeechRecognitionPipelineCallback Transcribe the audio sequence(s) given as inputs to text.
  * @param {AudioPipelineInputs} audio The input audio file(s) to be transcribed. The input is either:
  * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
@@ -1644,7 +1644,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
  * @param {Partial<AutomaticSpeechRecognitionConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<AutomaticSpeechRecognitionOutput|AutomaticSpeechRecognitionOutput[]>} An object containing the transcription text and optionally timestamps if `return_timestamps` is `true`.
- * 
+ *
  * @typedef {TextAudioPipelineConstructorArgs & AutomaticSpeechRecognitionPipelineCallback & Disposable} AutomaticSpeechRecognitionPipelineType
  */
 
@@ -1658,7 +1658,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * const output = await transcriber(url);
  * // { text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." }
  * ```
- * 
+ *
  * **Example:** Transcribe English w/ timestamps.
  * ```javascript
  * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
@@ -1672,7 +1672,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * //   ]
  * // }
  * ```
- * 
+ *
  * **Example:** Transcribe English w/ word-level timestamps.
  * ```javascript
  * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
@@ -1691,7 +1691,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * //   ]
  * // }
  * ```
- * 
+ *
  * **Example:** Transcribe French.
  * ```javascript
  * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
@@ -1699,7 +1699,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * const output = await transcriber(url, { language: 'french', task: 'transcribe' });
  * // { text: " J'adore, j'aime, je n'aime pas, je déteste." }
  * ```
- * 
+ *
  * **Example:** Translate French to English.
  * ```javascript
  * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
@@ -1707,7 +1707,7 @@ export class ZeroShotAudioClassificationPipeline extends (/** @type {new (option
  * const output = await transcriber(url, { language: 'french', task: 'translate' });
  * // { text: " I love, I like, I don't like, I hate." }
  * ```
- * 
+ *
  * **Example:** Transcribe/translate audio longer than 30 seconds.
  * ```javascript
  * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
@@ -1930,18 +1930,18 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
  * @typedef {Object} ImageToTextSingle
  * @property {string} generated_text The generated text.
  * @typedef {ImageToTextSingle[]} ImageToTextOutput
- * 
+ *
  * @callback ImageToTextPipelineCallback Assign labels to the image(s) passed as inputs.
  * @param {ImagePipelineInputs} texts The images to be captioned.
  * @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<ImageToTextOutput|ImageToTextOutput[]>} An object (or array of objects) containing the generated text(s).
- * 
+ *
  * @typedef {TextImagePipelineConstructorArgs & ImageToTextPipelineCallback & Disposable} ImageToTextPipelineType
  */
 
 /**
  * Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.
- * 
+ *
  * **Example:** Generate a caption for an image w/ `Xenova/vit-gpt2-image-captioning`.
  * ```javascript
  * const captioner = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
@@ -1949,7 +1949,7 @@ export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options
  * const output = await captioner(url);
  * // [{ generated_text: 'a cat laying on a couch with another cat' }]
  * ```
- * 
+ *
  * **Example:** Optical Character Recognition (OCR) w/ `Xenova/trocr-small-handwritten`.
  * ```javascript
  * const captioner = await pipeline('image-to-text', 'Xenova/trocr-small-handwritten');
@@ -1995,22 +1995,22 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
  * @property {string} label The label identified by the model.
  * @property {number} score The score attributed by the model for that label.
  * @typedef {ImageClassificationSingle[]} ImageClassificationOutput
- * 
+ *
  * @typedef {Object} ImageClassificationPipelineOptions Parameters specific to image classification pipelines.
- * @property {number} [top_k=1] The number of top labels that will be returned by the pipeline. 
- * 
+ * @property {number} [top_k=1] The number of top labels that will be returned by the pipeline.
+ *
  * @callback ImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The input images(s) to be classified.
  * @param {ImageClassificationPipelineOptions} [options] The options to use for image classification.
  * @returns {Promise<ImageClassificationOutput|ImageClassificationOutput[]>} An array or object containing the predicted labels and scores.
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & ImageClassificationPipelineCallback & Disposable} ImageClassificationPipelineType
  */
 
 /**
  * Image classification pipeline using any `AutoModelForImageClassification`.
  * This pipeline predicts the class of an image.
- * 
+ *
  * **Example:** Classify an image.
  * ```javascript
  * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
@@ -2020,7 +2020,7 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
  * //   { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
  * // ]
  * ```
- * 
+ *
  * **Example:** Classify an image and return top `n` classes.
  * ```javascript
  * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
@@ -2032,7 +2032,7 @@ export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipe
  * //   { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 },
  * // ]
  * ```
- * 
+ *
  * **Example:** Classify an image and return all classes.
  * ```javascript
  * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
@@ -2099,7 +2099,7 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
  * @property {string|null} label The label of the segment.
  * @property {number|null} score The score of the segment.
  * @property {RawImage} mask The mask of the segment.
- * 
+ *
  * @typedef {Object} ImageSegmentationPipelineOptions Parameters specific to image segmentation pipelines.
  * @property {number} [threshold=0.5] Probability threshold to filter out predicted masks.
  * @property {number} [mask_threshold=0.5] Threshold to use when turning the predicted masks into binary values.
@@ -2108,19 +2108,19 @@ export class ImageClassificationPipeline extends (/** @type {new (options: Image
  * depending on model capabilities. If not set, the pipeline will attempt to resolve (in that order).
  * @property {number[]} [label_ids_to_fuse=null] List of label ids to fuse. If not set, do not fuse any labels.
  * @property {number[][]} [target_sizes=null] List of target sizes for the input images. If not set, use the original image sizes.
- * 
+ *
  * @callback ImageSegmentationPipelineCallback Segment the input images.
  * @param {ImagePipelineInputs} images The input images.
  * @param {ImageSegmentationPipelineOptions} [options] The options to use for image segmentation.
  * @returns {Promise<ImageSegmentationPipelineOutput[]>} The annotated segments.
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & ImageSegmentationPipelineCallback & Disposable} ImageSegmentationPipelineType
  */
 
 /**
  * Image segmentation pipeline using any `AutoModelForXXXSegmentation`.
  * This pipeline predicts masks of objects and their classes.
- * 
+ *
  * **Example:** Perform image segmentation with `Xenova/detr-resnet-50-panoptic`.
  * ```javascript
  * const segmenter = await pipeline('image-segmentation', 'Xenova/detr-resnet-50-panoptic');
@@ -2278,19 +2278,19 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
 
 /**
  * @typedef {Object} BackgroundRemovalPipelineOptions Parameters specific to image segmentation pipelines.
- * 
+ *
  * @callback BackgroundRemovalPipelineCallback Segment the input images.
  * @param {ImagePipelineInputs} images The input images.
  * @param {BackgroundRemovalPipelineOptions} [options] The options to use for image segmentation.
  * @returns {Promise<RawImage[]>} The images with the background removed.
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & BackgroundRemovalPipelineCallback & Disposable} BackgroundRemovalPipelineType
  */
 
 /**
  * Background removal pipeline using certain `AutoModelForXXXSegmentation`.
  * This pipeline removes the backgrounds of images.
- * 
+ *
  * **Example:** Perform background removal with `Xenova/modnet`.
  * ```javascript
  * const segmenter = await pipeline('background-removal', 'Xenova/modnet');
@@ -2301,7 +2301,7 @@ export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePi
  * // ]
  * ```
  */
-export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageSegmentationPipelineType} */ (ImageSegmentationPipeline)) {
+export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => BackgroundRemovalPipelineType} */ (/** @type {any} */(ImageSegmentationPipeline))) {
     /**
      * Create a new BackgroundRemovalPipeline.
      * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
@@ -2336,25 +2336,25 @@ export class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePi
  * @typedef {Object} ZeroShotImageClassificationOutput
  * @property {string} label The label identified by the model. It is one of the suggested `candidate_label`.
  * @property {number} score The score attributed by the model for that label (between 0 and 1).
- * 
+ *
  * @typedef {Object} ZeroShotImageClassificationPipelineOptions Parameters specific to zero-shot image classification pipelines.
  * @property {string} [hypothesis_template="This is a photo of {}"] The sentence used in conjunction with `candidate_labels`
  * to attempt the image classification by replacing the placeholder with the candidate_labels.
  * Then likelihood is estimated by using `logits_per_image`.
- * 
+ *
  * @callback ZeroShotImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The input images.
  * @param {string[]} candidate_labels The candidate labels for this image.
  * @param {ZeroShotImageClassificationPipelineOptions} [options] The options to use for zero-shot image classification.
  * @returns {Promise<ZeroShotImageClassificationOutput[]|ZeroShotImageClassificationOutput[][]>} An array of objects containing the predicted labels and scores.
- * 
+ *
  * @typedef {TextImagePipelineConstructorArgs & ZeroShotImageClassificationPipelineCallback & Disposable} ZeroShotImageClassificationPipelineType
  */
 
 /**
  * Zero shot image classification pipeline. This pipeline predicts the class of
  * an image when you provide an image and a set of `candidate_labels`.
- * 
+ *
  * **Example:** Zero shot image classification w/ `Xenova/clip-vit-base-patch32`.
  * ```javascript
  * const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
@@ -2384,7 +2384,7 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
         const isBatched = Array.isArray(images);
         const preparedImages = await prepareImages(images);
 
-        // Insert label into hypothesis template 
+        // Insert label into hypothesis template
         const texts = candidate_labels.map(
             x => hypothesis_template.replace('{}', x)
         );
@@ -2431,23 +2431,23 @@ export class ZeroShotImageClassificationPipeline extends (/** @type {new (option
  * @property {number} score The score attributed by the model for that label.
  * @property {BoundingBox} box The bounding box of detected object in image's original size, or as a percentage if `percentage` is set to true.
  * @typedef {ObjectDetectionPipelineSingle[]} ObjectDetectionPipelineOutput
- * 
+ *
  * @typedef {Object} ObjectDetectionPipelineOptions Parameters specific to object detection pipelines.
  * @property {number} [threshold=0.9] The threshold used to filter boxes by score.
  * @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false).
- * 
+ *
  * @callback ObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The input images.
  * @param {ObjectDetectionPipelineOptions} [options] The options to use for object detection.
- * @returns {Promise<ObjectDetectionPipelineOutput|ObjectDetectionPipelineOutput[]>} A list of objects or a list of list of objects. 
- * 
+ * @returns {Promise<ObjectDetectionPipelineOutput|ObjectDetectionPipelineOutput[]>} A list of objects or a list of list of objects.
+ *
  * @typedef {ImagePipelineConstructorArgs & ObjectDetectionPipelineCallback & Disposable} ObjectDetectionPipelineType
  */
 
 /**
  * Object detection pipeline using any `AutoModelForObjectDetection`.
  * This pipeline predicts bounding boxes of objects and their classes.
- * 
+ *
  * **Example:** Run object-detection with `Xenova/detr-resnet-50`.
  * ```javascript
  * const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
@@ -2521,27 +2521,27 @@ export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipe
  * @property {string} label Text query corresponding to the found object.
  * @property {number} score Score corresponding to the object (between 0 and 1).
  * @property {BoundingBox} box Bounding box of the detected object in image's original size, or as a percentage if `percentage` is set to true.
- * 
+ *
  * @typedef {Object} ZeroShotObjectDetectionPipelineOptions Parameters specific to zero-shot object detection pipelines.
  * @property {number} [threshold=0.1] The probability necessary to make a prediction.
  * @property {number} [top_k=null] The number of top predictions that will be returned by the pipeline.
  * If the provided number is `null` or higher than the number of predictions available, it will default
  * to the number of predictions.
  * @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false).
- * 
+ *
  * @callback ZeroShotObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The input images.
  * @param {string[]} candidate_labels What the model should recognize in the image.
  * @param {ZeroShotObjectDetectionPipelineOptions} [options] The options to use for zero-shot object detection.
  * @returns {Promise<ZeroShotObjectDetectionOutput[]|ZeroShotObjectDetectionOutput[][]>} An array of objects containing the predicted labels, scores, and bounding boxes.
- * 
+ *
  * @typedef {TextImagePipelineConstructorArgs & ZeroShotObjectDetectionPipelineCallback & Disposable} ZeroShotObjectDetectionPipelineType
  */
 
 /**
  * Zero-shot object detection pipeline. This pipeline predicts bounding boxes of
  * objects when you provide an image and a set of `candidate_labels`.
- * 
+ *
  * **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32`.
  * ```javascript
  * const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
@@ -2571,7 +2571,7 @@ export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipe
  * //   }
  * // ]
  * ```
- * 
+ *
  * **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32` (returning top 4 matches and setting a threshold).
  * ```javascript
  * const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
@@ -2686,13 +2686,13 @@ export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: T
  * @typedef {Object} DocumentQuestionAnsweringSingle
  * @property {string} answer The generated text.
  * @typedef {DocumentQuestionAnsweringSingle[]} DocumentQuestionAnsweringOutput
- * 
+ *
  * @callback DocumentQuestionAnsweringPipelineCallback Answer the question given as input by using the document.
  * @param {ImageInput} image The image of the document to use.
  * @param {string} question A question to ask of the document.
  * @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
  * @returns {Promise<DocumentQuestionAnsweringOutput|DocumentQuestionAnsweringOutput[]>} An object (or array of objects) containing the answer(s).
- * 
+ *
  * @typedef {TextImagePipelineConstructorArgs & DocumentQuestionAnsweringPipelineCallback & Disposable} DocumentQuestionAnsweringPipelineType
  */
 
@@ -2700,7 +2700,7 @@ export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: T
  * Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`.
  * The inputs/outputs are similar to the (extractive) question answering pipeline; however,
  * the pipeline takes an image (and optional OCR'd words/boxes) as input instead of text context.
- * 
+ *
  * **Example:** Answer questions about a document with `Xenova/donut-base-finetuned-docvqa`.
  * ```javascript
  * const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
@@ -2770,22 +2770,22 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
  * @typedef {Object} TextToAudioOutput
  * @property {Float32Array} audio The generated audio waveform.
  * @property {number} sampling_rate The sampling rate of the generated audio waveform.
- * 
+ *
  * @typedef {Object} TextToAudioPipelineOptions Parameters specific to text-to-audio pipelines.
  * @property {Tensor|Float32Array|string|URL} [speaker_embeddings=null] The speaker embeddings (if the model requires it).
- * 
+ *
  * @callback TextToAudioPipelineCallback Generates speech/audio from the inputs.
  * @param {string|string[]} texts The text(s) to generate.
  * @param {TextToAudioPipelineOptions} options Parameters passed to the model generation/forward method.
  * @returns {Promise<TextToAudioOutput>} An object containing the generated audio and sampling rate.
- * 
+ *
  * @typedef {TextToAudioPipelineConstructorArgs & TextToAudioPipelineCallback & Disposable} TextToAudioPipelineType
  */
 
 /**
  * Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`.
  * This pipeline generates an audio file from an input text and optional other conditional inputs.
- * 
+ *
  * **Example:** Generate audio from text with `Xenova/speecht5_tts`.
  * ```javascript
  * const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });
@@ -2796,17 +2796,17 @@ export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options:
  * //   sampling_rate: 16000
  * // }
  * ```
- * 
+ *
  * You can then save the audio to a .wav file with the `wavefile` package:
  * ```javascript
  * import wavefile from 'wavefile';
  * import fs from 'fs';
- * 
+ *
  * const wav = new wavefile.WaveFile();
  * wav.fromScratch(1, out.sampling_rate, '32f', out.audio);
  * fs.writeFileSync('out.wav', wav.toBuffer());
  * ```
- * 
+ *
  * **Example:** Multilingual speech generation with `Xenova/mms-tts-fra`. See [here](https://huggingface.co/models?pipeline_tag=text-to-speech&other=vits&sort=trending) for the full list of available languages (1107).
  * ```javascript
  * const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra');
@@ -2912,13 +2912,13 @@ export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPi
  * @callback ImageToImagePipelineCallback Transform the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The images to transform.
  * @returns {Promise<RawImage|RawImage[]>} The transformed image or list of images.
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & ImageToImagePipelineCallback & Disposable} ImageToImagePipelineType
  */
 
 /**
  * Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous image input.
- * 
+ *
  * **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64`
  * ```javascript
  * const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64');
@@ -2963,17 +2963,17 @@ export class ImageToImagePipeline extends (/** @type {new (options: ImagePipelin
  * @typedef {Object} DepthEstimationPipelineOutput
  * @property {Tensor} predicted_depth The raw depth map predicted by the model.
  * @property {RawImage} depth The processed depth map as an image (with the same size as the input image).
- * 
+ *
  * @callback DepthEstimationPipelineCallback Predicts the depth for the image(s) passed as inputs.
  * @param {ImagePipelineInputs} images The images to compute depth for.
  * @returns {Promise<DepthEstimationPipelineOutput|DepthEstimationPipelineOutput[]>} An image or a list of images containing result(s).
- * 
+ *
  * @typedef {ImagePipelineConstructorArgs & DepthEstimationPipelineCallback & Disposable} DepthEstimationPipelineType
  */
 
 /**
  * Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.
- * 
+ *
  * **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas`
  * ```javascript
  * const depth_estimator = await pipeline('depth-estimation', 'Xenova/dpt-hybrid-midas');
@@ -3358,7 +3358,7 @@ const TASK_ALIASES = Object.freeze({
 
 /**
  * Utility factory method to build a `Pipeline` object.
- * 
+ *
  * @template {PipelineType} T The type of pipeline to return.
  * @param {T} task The task defining which pipeline will be returned. Currently accepted tasks are:
  *  - `"audio-classification"`: will return a `AudioClassificationPipeline`.
diff --git a/src/utils/image.js b/src/utils/image.js
index 40f51625e..9d1bb3606 100644
--- a/src/utils/image.js
+++ b/src/utils/image.js
@@ -100,7 +100,7 @@ export class RawImage {
 
     /**
      * Helper method for reading an image from a variety of input types.
-     * @param {RawImage|string|URL} input
+     * @param {RawImage|string|URL|Blob|HTMLCanvasElement|OffscreenCanvas} input
      * @returns The image object.
      *
      * **Example:** Read image from a URL.
@@ -119,6 +119,14 @@ export class RawImage {
             return input;
         } else if (typeof input === 'string' || input instanceof URL) {
             return await this.fromURL(input);
+        } else if (input instanceof Blob) {
+            return await this.fromBlob(input);
+        } else if (
+            (typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement)
+            ||
+            (typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)
+        ) {
+            return this.fromCanvas(input);
         } else {
             throw new Error(`Unsupported input type: ${typeof input}`);
         }