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Add LVFace library #1685

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70d4f83
add Library
GitHup2016yjh Aug 13, 2025
81ec481
Merge branch 'main' into dev_lvface
GitHup2016yjh Aug 13, 2025
ce6e472
Update Add LVFace library
GitHup2016yjh Aug 14, 2025
2da0490
Update LVFace library
GitHup2016yjh Aug 15, 2025
4943378
Update packages/tasks/src/model-libraries.ts
pcuenca Aug 15, 2025
35a49f2
Merge branch 'main' into dev_lvface
pcuenca Aug 15, 2025
e5ae248
Update LVFace library
GitHup2016yjh Aug 15, 2025
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18 changes: 18 additions & 0 deletions packages/tasks/src/model-libraries-snippets.ts
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Expand Up @@ -1703,6 +1703,24 @@ export const vfimamba = (model: ModelData): string[] => [
model = Model.from_pretrained("${model.id}")`,
];

export const lvface = (model: ModelData): string[] => [
`## Initialize the inferencer from inference_onnx.py
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can you please add imports 🥹 feel free to remove comments to make it more minimal

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yes^ - let's make the snippets complete and minimal.

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Excuse me, would it be necessary for me to delete some of the comments to reduce the number of lines taken by them?

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I think in general we keep them minimal, so lesser the better

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I think in general we keep them minimal, so lesser the better

Thank you for the reminder. I have already compressed it to just one line.

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Apologies for the confusion here @GitHup2016yjh - what we meant by minimal snippet was to have something that enables people to test your model in say a Google Colab without looking at your GitHub repo or documentation for more details.

You can see couple of examples of this in the rest of the snippets in the same file.

An ideal snippet has three main parts:

  1. Setting up environment in the commments (# pip instal x y z)
  2. Load the model weights from hugging face (inferencer in your case)
  3. Run inference on a sample input

Let me know if this is not clear - happy to elaborate more.

Thanks again for your contribution!

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Apologies for the confusion here @GitHup2016yjh - what we meant by minimal snippet was to have something that enables people to test your model in say a Google Colab without looking at your GitHub repo or documentation for more details.

You can see couple of examples of this in the rest of the snippets in the same file.

An ideal snippet has three main parts:

  1. Setting up environment in the commments (# pip instal x y z)
  2. Load the model weights from hugging face (inferencer in your case)
  3. Run inference on a sample input

Let me know if this is not clear - happy to elaborate more.

Thanks again for your contribution!

Thank you for the reminder. I have revised this part of the content. Is it correct now?

inferencer = LVFaceONNXInferencer(
model_path=${model.id}, # Path to your ONNX model
use_gpu=True # Set to False for CPU-only inference
)

## Extract feature from local image
feat1 = inferencer.infer_from_image("path/to/image1.jpg")

## Extract feature from URL
feat2 = inferencer.infer_from_url("https://example.com/image1.jpg")

## Calculate cosine similarity
similarity = inferencer.calculate_similarity(feat1, feat2)
print(f"Similarity score: {similarity:.6f}") # Output example: 0.872345`,
];

export const voicecraft = (model: ModelData): string[] => [
`from voicecraft import VoiceCraft

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7 changes: 7 additions & 0 deletions packages/tasks/src/model-libraries.ts
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Expand Up @@ -1156,6 +1156,13 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
countDownloads: `path_extension:"pkl"`,
snippets: snippets.vfimamba,
},
"LVFace": {
prettyLabel: "LVFace",
repoName: "LVFace",
repoUrl: "https://github.com/bytedance/LVFace",
countDownloads: `path_extension:"pt" OR path_extension:"onnx"`,
snippets: snippets.lvface,
},
voicecraft: {
prettyLabel: "VoiceCraft",
repoName: "VoiceCraft",
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