The Fascinating Evolution of AI Video Generators
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Top Comments (10)
Yes, a deep dive into diffusion transformers for one of the next videos would be awesome!
Could you possibly make the same video for Openai's advanced voice mode?
You can try out Luma AI's Dream Machine here! https://luma.1stcollab.com/bycloudai I am really good at having great timing. MovieGen came out when I nearly finished the video. I'm sad. So here's a quick definition of DiT: A diffusion transformer (DiT) is a model that combines elements of diffusion models and transformers to generate data like image synthesis, audio generation, or text generation. Diffusion models are a class of probabilistic generative models that create data by iteratively denoising a latent variable, which starts from pure noise and is gradually transformed into a coherent sample. Transformers on the other hand, are neural network architectures known for their ability to model long-range dependencies in data, primarily through self-attention mechanisms. You could ultimately say that, a diffusion transformer is just a transformer with the goal of denoising. Yum. Here's MovieGen's paper: https://arxiv.org/abs/2410.13720 it contains a better run down to crafting the latest near SoTA video generation
A video on Diffusion Transformers = 😊👍
De-noised bread, got it!
Hey man, really appreciate your humor and memes, makes learning ML a lot more fun. Always looking forward to more!
We definitely need a dedicated video.
Baking Bread = great metaphor
That bread analogy was 100% chatgpt
I watched half of the video to remind myself that life can suck a lot sometimes.
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Top Comments (10)
Yes, a deep dive into diffusion transformers for one of the next videos would be awesome!
Could you possibly make the same video for Openai's advanced voice mode?
You can try out Luma AI's Dream Machine here! https://luma.1stcollab.com/bycloudai I am really good at having great timing. MovieGen came out when I nearly finished the video. I'm sad. So here's a quick definition of DiT: A diffusion transformer (DiT) is a model that combines elements of diffusion models and transformers to generate data like image synthesis, audio generation, or text generation. Diffusion models are a class of probabilistic generative models that create data by iteratively denoising a latent variable, which starts from pure noise and is gradually transformed into a coherent sample. Transformers on the other hand, are neural network architectures known for their ability to model long-range dependencies in data, primarily through self-attention mechanisms. You could ultimately say that, a diffusion transformer is just a transformer with the goal of denoising. Yum. Here's MovieGen's paper: https://arxiv.org/abs/2410.13720 it contains a better run down to crafting the latest near SoTA video generation
A video on Diffusion Transformers = 😊👍
De-noised bread, got it!
Hey man, really appreciate your humor and memes, makes learning ML a lot more fun. Always looking forward to more!
We definitely need a dedicated video.
Baking Bread = great metaphor
That bread analogy was 100% chatgpt
I watched half of the video to remind myself that life can suck a lot sometimes.