Text-To-Video-Finetuning VS lora

I used this repo for the finetuning: https://github.com/ExponentialML/Text-To-Video-Finetuning

Has anyone successfully done this? I walked through the steps and did not find what I wanted so wanting to know if anyone has a tutorial about fine-tuning Modelscope with https://github.com/ExponentialML/Text-To-Video-Finetuning

Well, actually today all I have is ModelScope txt2video, SadTalker and an understanding of how this technology works, but pretty soon I'll have this https://github.com/ExponentialML/Text-To-Video-Finetuning/pull/27 too. Then whatever advancements things like https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/ unlock will filter down to me too and so on it will go. My understanding of the tech will continue to deepen as I continue to retrain from traditional software engineering to machine learning. Things like Culitho (https://www.anandtech.com/show/18792/nvidias-culitho-to-speed-up-computational-lithography-for-2nm-and-beyond) and AlphaTensor (https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor) will continue to make compute faster and more affordable driving the cost of training/inference down and massively increasing the accessibility. Increasingly more functions will continue to be approximated closer and closer (https://www.youtube.com/watch?v=0QczhVg5HaI).

Made by strangeman3107 via https://github.com/ExponentialML/Text-To-Video-Finetuning. The original Diffusers weights https://huggingface.co/datasets/strangeman3107/animov-0.1

Just as one of Deforum Discord's server members linked me it, I was so inspired that I quickly wrote the Diffusers->pth (ModelScope original format) conversion script

Yes, there's a Diffusers based repo https://github.com/ExponentialML/Text-To-Video-Finetuning.

Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora

Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.

No, it's a deep learning thing

Low Rank Adaptation of Large Language Models.

Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.

Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.

Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685

Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...

Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685