Bump sharp and @huggingface/transformers

[dependabot]

Bumps sharp to 0.35.5 and updates ancestor dependency @huggingface/transformers. These dependencies need to be updated together.

Updates sharp from 0.34.5 to 0.35.5

Release notes

Sourced from sharp's releases.

v0.35.5

https://github.com/lovell/sharp-libvips/releases/tag/v1.3.4

• Add upper bounds check on length of linear and GIF delay arrays.

• Improve error handing when WebAssembly fallback also fails. #4593 @​lazerg

• TypeScript: Allow multi-frame options for JXL output. #4602 @​ramin-010

• TypeScript: Remove non-existent named export. #4604

• Increase accepted dimensions when extending an image. #4605

• Improve gain map support for extract and rotate operations. #4606

• Tests: Ensure composite tests pass on big endian platforms. #4609

v0.35.5-rc.1

https://github.com/lovell/sharp-libvips/releases/tag/v1.3.4-rc.1

• Add upper bounds check on length of linear and GIF delay arrays.

• Improve error handing when WebAssembly fallback also fails. #4593 @​lazerg

• TypeScript: Allow multi-frame options for JXL output. #4602 @​ramin-010

• TypeScript: Remove non-existent named export. #4604

• Increase accepted dimensions when extending an image. #4605

• Improve gain map support for extract operation. #4606

... (truncated)

Commits

• 51a990f Release v0.35.5
• 96de105 Upgrade to sharp-libvips v1.3.4
• 3a61390 CI: Configure Dependabot with all package.json locations
• 4940c50 Improve gain map support for rotate/flip/flop ops
• 20654aa Prerelease v0.35.5-rc.1
• ef4f934 CI: Upgrade to Ubuntu 26.04
• 358df95 Upgrade to libvips v8.18.7
• 49f4903 Improve gain map support for rotate-then-extract #4606
• 0e2e55e Silence a couple of compiler/static analysis warnings
• cef3b8c Improve gain map support for extract operation #4606
• Additional commits viewable in compare view

Updates @huggingface/transformers from 4.2.0 to 4.3.1

Release notes

Sourced from @​huggingface/transformers's releases.

4.3.1

What's new?

This release adds support for EmbeddingGemma 2. EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

Text search

The feature-extraction pipeline returns normalized embeddings, so the dot product of two embeddings is their cosine similarity:

import { pipeline, matmul } from "@huggingface/transformers";
const extractor = await pipeline("feature-extraction", "onnx-community/embeddinggemma-2-ONNX", {
device: "webgpu", // or "wasm" (browser) / "cpu" (Node.js)
dtype: "q4", // see "Choosing a dtype" below
});
const query = "task: search result | query: Which planet is known as the Red Planet?";
const documents = [
"title: none | text: Venus is often called Earth's twin because of its similar size and proximity.",
"title: none | text: Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"title: none | text: Jupiter, the largest planet in our solar system, has a prominent red spot.",
"title: none | text: Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
];
const embeddings = await extractor([query, ...documents], { pooling: "mean", normalize: true });
const query_embedding = embeddings.slice([0, 1]);
const document_embeddings = embeddings.slice([1, null]);
const scores = (await matmul(query_embedding, document_embeddings.transpose(1, 0))).tolist()[0];
const ranking = scores.map((score, i) => ({ score, document: documents[i] })).sort((a, b) => b.score - a.score);
console.log(ranking);
// [
//   { score: 0.854, document: "title: none | text: Mars, known for its reddish appearance, ..." },
//   { score: 0.783, document: "title: none | text: Saturn, famous for its rings, ..." },
//   { score: 0.752, document: "title: none | text: Jupiter, the largest planet in our solar system, ..." },
//   { score: 0.684, document: "title: none | text: Venus is often called Earth's twin, ..." },
// ]

Images, audio and video

For other modalities, use the processor and the model directly. Every input maps into the same embedding space, so any embedding can be compared with any other: here, text queries against an image, an audio clip and a video.

import { AutoModel, AutoProcessor, load_image, load_audio, load_video, cat, matmul } from "@huggingface/transformers";
const model_id = "onnx-community/embeddinggemma-2-ONNX";
const processor = await AutoProcessor.from_pretrained(model_id);
</tr></table>

... (truncated)

Commits

• 511bb61 [version] Update to 4.3.1
• e822439 new model (#1788)
• 40d3304 Bump doc-builder workflow pin (#1787)
• 836b9cb Scope GITHUB_TOKEN permissions per job (#1777)
• c5f2b97 Bump the actions group across 1 directory with 6 updates (#1703)
• a1728dd [version] Update to 4.3.0
• f9dec42 Bump dependencies (#1766)
• dce094e Massive documentation overhaul (#1665)
• 37fddf9 Fix RawAudio.toBlob() ignoring TypedArray byteOffset/length (#1712)
• eb5d05e Skip browser cache writes for non-http(s) resources (#1723)
• Additional commits viewable in compare view