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multimodalartย 
posted an update 3 months ago
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9081
Want to iterate on a Hugging Face Space with an LLM?

Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!

multimodalart/repo2txt
multimodalartย 
posted an update 7 months ago
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18006
Self-Forcing - a real-time video distilled model from Wan 2.1 by @adobe is out, and they open sourced it ๐Ÿ

I've built a live real time demo on Spaces ๐Ÿ“น๐Ÿ’จ

multimodalart/self-forcing
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akhaliqย 
posted an update about 1 year ago
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48760
Google drops Gemini 2.0 Flash Thinking

a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more

now available in anychat, try it out: https://huggingface.co/spaces/akhaliq/anychat
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akhaliqย 
posted an update about 1 year ago
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47849
QwQ-32B-Preview is now available in anychat

A reasoning model that is competitive with OpenAI o1-mini and o1-preview

try it out: https://huggingface.co/spaces/akhaliq/anychat
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akhaliqย 
posted an update about 1 year ago
akhaliqย 
posted an update about 1 year ago
multimodalartย 
posted an update over 1 year ago
csyxweiย 
updated a Space over 1 year ago
akhaliqย 
posted an update over 1 year ago
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21203
Phased Consistency Model

Phased Consistency Model (2405.18407)

The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.