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Image generation

Input Text→ Output Image

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tiny-stable-diffusion-with-textual-inversion

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Image generation

Input Text→ Output Image

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tiny-videochat-flash-qwen

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

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tiny-xlm-roberta

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

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trocr-small-handwritten

optimum-intel-internal-testing

TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of DeiT, while the text decoder was initialized from the weights of UniLM. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens.

Image captioning

Input Image→ Output Text

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vit-with-attentions

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Image classification

Input Image→ Output Not verified

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vit-with-hidden_states

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Image classification

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wav2vec2-random-tiny-classifier

optimum-intel-internal-testing

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

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