Deprecation of Git Authentication using password
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Sylvestre Bcht
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Reacted to
victor's
post with 🚀
1 day ago
Post
1790
Perfect example of why
Qwen/Qwen2.5-Coder-32B-Instruct is insane?
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
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Reacted to
clem's
post with 🔥
about 1 month ago
Post
4404
This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
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Deprecation of Git Authentication using password
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19
upvoted
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4 months ago
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4 months ago
Reacted to
bghira's
post with 🔥
4 months ago
Post
4377
Wanted to share some brief comparison of early training of the two-stage PixArt e-diffi pipeline.
On the left, we have the full stage 1 model generating all 50 steps on its own. This model is not trained at all on the final 400 timesteps of the schedule. On the right, we have the combined pipeline where stage 1 output is fed into stage 2.
Currently, the difference is rather minimal - but the small details are reliably improved.
In the watercolour example, the full generation (right side) has the texture of the watercolour paper, and the partial generation (left side) has a more flat digital art look to it.
For the blacksmith robot, the sparks emitted from the operation have a more natural blend to it. The robot's clothing appears to be undergoing some interesting transformation due to the undertrained state of the weights.
The medieval battle image has improved blades of grass, settling dust particles, and fabric in the flag.
The stage 2 model being trained does not seem to resolve any global coherence issues despite having 400 steps in its schedule, but it still noticeably changes the local coherence, eg. the consistency of fabrics and metals can be improved through stage 2 fine-tuning.
The stage 1 model is the workhorse of the output, as expected with the 600 timesteps in its schedule. Additional fine-tuning of this model will improve the overall global coherence of the outputs. I wish I could say it will not impact fine details, but a lot of that does seem to be carried forward.
As noted, these models are undertrained due to a lack of compute. But they are a promising look toward what an e-diffi PixArt might be capable of.
Does anyone want to build this out fully with me?
On the left, we have the full stage 1 model generating all 50 steps on its own. This model is not trained at all on the final 400 timesteps of the schedule. On the right, we have the combined pipeline where stage 1 output is fed into stage 2.
Currently, the difference is rather minimal - but the small details are reliably improved.
In the watercolour example, the full generation (right side) has the texture of the watercolour paper, and the partial generation (left side) has a more flat digital art look to it.
For the blacksmith robot, the sparks emitted from the operation have a more natural blend to it. The robot's clothing appears to be undergoing some interesting transformation due to the undertrained state of the weights.
The medieval battle image has improved blades of grass, settling dust particles, and fabric in the flag.
The stage 2 model being trained does not seem to resolve any global coherence issues despite having 400 steps in its schedule, but it still noticeably changes the local coherence, eg. the consistency of fabrics and metals can be improved through stage 2 fine-tuning.
The stage 1 model is the workhorse of the output, as expected with the 600 timesteps in its schedule. Additional fine-tuning of this model will improve the overall global coherence of the outputs. I wish I could say it will not impact fine details, but a lot of that does seem to be carried forward.
As noted, these models are undertrained due to a lack of compute. But they are a promising look toward what an e-diffi PixArt might be capable of.
Does anyone want to build this out fully with me?
- 1 reply
Reacted to
Xenova's
post with 🔥
4 months ago
Post
7898
Introducing Whisper Diarization: Multilingual speech recognition with word-level timestamps and speaker segmentation, running 100% locally in your browser thanks to 🤗 Transformers.js!
Tested on this iconic Letterman interview w/ Grace Hopper from 1983!
- Demo: Xenova/whisper-speaker-diarization
- Source code: Xenova/whisper-speaker-diarization
Tested on this iconic Letterman interview w/ Grace Hopper from 1983!
- Demo: Xenova/whisper-speaker-diarization
- Source code: Xenova/whisper-speaker-diarization
- 1 reply
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