Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
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Muhammad Umair
umair894
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Multimodal Reidentification | Feature Upscaling | Object Tracking |PhD UESTC
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#27 opened 9 days ago
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Introducing Photo-Mate-v2, based on FLUX.1-Kontext-dev, for advanced image manipulation tasks. It supports transforming scenes into top-down/bottom-up perspectives, CAM-right/left-view and its reverse, as well as general kontext-specified object removal. Below is the list of demos and adapters.š„š¤
ā¤ Spaces [Demo] : https://huggingface.co/spaces/prithivMLmods/Kontext-Photo-Mate-v2
Kontext-Adapters :
ā¦ Kontext-Bottom-Up-View: prithivMLmods/Kontext-Bottom-Up-View
ā¦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
ā¦ Kontext-Top-Down-View: prithivMLmods/Kontext-Top-Down-View
ā¦ Kontext-CAM-Left-View: prithivMLmods/Kontext-CAM-Left-View
ā¦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
ā¦ Kontext-Unblur-Upscale: prithivMLmods/Kontext-Unblur-Upscale
ā¦ Kontext-0811-exp: prithivMLmods/Kontext-0811-exp
Photo-Mate Collection:
ā¦ Kontext CAM Angles: https://huggingface.co/collections/prithivMLmods/kontext-cam-angles
ā¦ i2i - Kontext (exp): https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp
ā¦ LZO-1 (Lossless Zoom Operator): https://huggingface.co/collections/prithivMLmods/lzo-1-lossless-zoom-operator
Related-Apps:
ā¦ Photo-Mate [Version 1.0]: prithivMLmods/Photo-Mate-i2i
ā¦ Image Generation Apps [Collection]: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
To know more about it, visit the app page or the respective model page!
@prithivMLmods
ā¤ Spaces [Demo] : https://huggingface.co/spaces/prithivMLmods/Kontext-Photo-Mate-v2
Kontext-Adapters :
ā¦ Kontext-Bottom-Up-View: prithivMLmods/Kontext-Bottom-Up-View
ā¦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
ā¦ Kontext-Top-Down-View: prithivMLmods/Kontext-Top-Down-View
ā¦ Kontext-CAM-Left-View: prithivMLmods/Kontext-CAM-Left-View
ā¦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
ā¦ Kontext-Unblur-Upscale: prithivMLmods/Kontext-Unblur-Upscale
ā¦ Kontext-0811-exp: prithivMLmods/Kontext-0811-exp
Photo-Mate Collection:
ā¦ Kontext CAM Angles: https://huggingface.co/collections/prithivMLmods/kontext-cam-angles
ā¦ i2i - Kontext (exp): https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp
ā¦ LZO-1 (Lossless Zoom Operator): https://huggingface.co/collections/prithivMLmods/lzo-1-lossless-zoom-operator
Related-Apps:
ā¦ Photo-Mate [Version 1.0]: prithivMLmods/Photo-Mate-i2i
ā¦ Image Generation Apps [Collection]: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
To know more about it, visit the app page or the respective model page!
@prithivMLmods
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A small blog post titled - Hall of Multimodal OCR VLMs and Demonstrations has been published on āļø https://huggingface.co/blog/prithivMLmods/multimodal-ocr-vlms on behalf of
strangervisionhf
It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining
strangervisionhf
It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining
transformers compatibility.
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Multilingual Tokenization Showdown
Analyzing 12 LLM Tokenizers Across 204 Languages.
First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages:
Norod78/WikiCat-Multilingual
For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language).
You can see a slideshow summary of the results here:
https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html
I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo:
https://github.com/Norod/wikicat-tokenizer-eval
Post on X:
https://x.com/Norod78/status/1984366900550266999
Analyzing 12 LLM Tokenizers Across 204 Languages.
First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages:
Norod78/WikiCat-Multilingual
For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language).
You can see a slideshow summary of the results here:
https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html
I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo:
https://github.com/Norod/wikicat-tokenizer-eval
Post on X:
https://x.com/Norod78/status/1984366900550266999