ainodes-engine VS Text-To-Video-Finetuning

currently working on a pyqt node based app, qt has a lot of 3d power. XmYx/ainodes-engine (github.com)

1: git clone -b comfy-dev https://github.com/XmYx/ainodes-engine 2: cd ainodes-engine 3: setup_ainodes.bat then you can start ainodes the usual way, but also ComfyUI with run_comfyui.bat. If you install any custom node package for comfy using the comfy manager, those get imported into the desktop engine on next start. Compatibility is WIP, but most image and sampling nodes will work. (Disco Diffusion too) To install the Comfy Manager run the following from ainodes-engine folder: 1: cd src 2: cd ComfyUI 3: cd custom_nodes 4: git clone https://github.com/ltdrdata/ComfyUI-Manager.git

XmYx/ainodes-engine (github.com) patreon.com/deforum_ainodes

And take a look at the node engine if you are interested: https://github.com/XmYx/ainodes-engine

You can see some of our previous open source works at: XmYx/ainodes-engine (github.com)

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.