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Promptda Vits Hf

with ONNX weights to be compatible with Transformers.js.

KindOtherBase modeldepth-anything/promptda_vits_hfVersionRevision 06da7a6Cgrade

Model source

Kind
Other
File size
19.0–95.8 MB
Base model
depth-anything/promptda_vits_hf
Version
vRevision 06da7a6
Updated
Oct 20, 2026
Downloads
5
Source
Hugging Face

Source description

https://huggingface.co/depth-anything/promptda_vits_hf with ONNX weights to be compatible with Transformers.js.

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Sources

1 source
Hugging FacePrimaryVersion 06da7a62ff2166f358416fd5c98518354c17b460
huggingface.co/onnx-community/promptda_vits_hf/tree/06da7a62ff2166f358416fd5c98518354c17b460

Verified Aug 17

Model artifacts

8 artifacts

onnx/model_bnb4.onnx

onnx · 26.2 MB · SHA-256 b3687a86d275…b34b · Hugging Face

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onnx/model_fp16.onnx

onnx · 48.1 MB · SHA-256 7737c652bfe8…3a9f · Hugging Face

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onnx/model_int8.onnx

onnx · 26.5 MB · SHA-256 7ea0061d319c…a9a6 · Hugging Face

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onnx/model_q4.onnx

onnx · 27.5 MB · SHA-256 d289da0d4280…b3d4 · Hugging Face

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onnx/model_q4f16.onnx

onnx · 19.0 MB · SHA-256 0fb3dcea9e42…6ab7 · Hugging Face

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onnx/model_quantized.onnx

onnx · 26.5 MB · SHA-256 f2262bf0c406…8d4a · Hugging Face

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onnx/model_uint8.onnx

onnx · 26.5 MB · SHA-256 f2262bf0c406…8d4a · Hugging Face

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onnx/model.onnx

onnx · 95.8 MB · SHA-256 8734997066a1…90cb · Hugging Face

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Source excerpts

2 excerpts

--- library_name: transformers.js base_model: depth-anything/promptda_vits_hf pipeline_tag: depth-estimation --- https://huggingface.co/depth-anything/promptda_vits_hf with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Source context: 5 downloads · 0 likes · Pipeline depth-estimation · Library transformers.js · Repo onnx-community/promptda_vits_hf