Text-To-Video-Finetuning
automatic
Text-To-Video-Finetuning | automatic | |
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19 | 185 | |
507 | 5,161 | |
- | - | |
10.0 | 10.0 | |
6 months ago | 7 days ago | |
Python | Python | |
MIT License | GNU Affero General Public License v3.0 |
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Text-To-Video-Finetuning
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Announcing zeroscope_v2_XL: a new 1024x576 video model based on Modelscope
I used this repo for the finetuning: https://github.com/ExponentialML/Text-To-Video-Finetuning
- Inspired by u/Many-Ad-6225's Mortal Kombat remastering post, test of a Liu Kang animation x4 upscale (ModelScope vid2vid)
- Text-to-Video Model Fine-Tuned with 512x512 Anime-Style for Diffusers
- How do you custom train Modelscope?
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ModelScope 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
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What will happen once AI is capable of letting 1 person make a whole Hollywood-quality film?
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).
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Animov-0.1 โ High-resolution anime fine-tune of ModelScope text2video is now available in Auto1111! Trained on 384x384 anime fragments by strangeman3107, makes 2 seconds long videos with only 8.6G of VRAM (16 frames at 8 fps)
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
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Auto1111 text2video Major Update! Animate pictures and loop videos with inpainting keyframes. 125 frames (8 secs) video now takes only 12gbs of VRAM thanks to torch2 optimization. WebAPI is released, no delay between runs! (ModelScope)
Yes, there's a Diffusers based repo https://github.com/ExponentialML/Text-To-Video-Finetuning.
- sd-webui-text2video has been updated and now it works with Xformers
automatic
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
> it won't ever be a viable option
For production workloads, I generally agree. It's an unsupported hack with a questionable future, I wouldn't do anything money-making with it.
However, for tinkering and consumer workloads, it already works pretty well. Enough of cuDNN and cuBLAS work to run PyTorch and in turn, Stable Diffusion with https://github.com/lshqqytiger/ZLUDA - there's even a fairly user-friendly setup process already in https://github.com/vladmandic/automatic .
I was able to get a personal non-ML related project working on my AMD card in just a few minutes, which saved me a lot of development time before I then deployed the production workload on NV hardware (this is probably why AMD pulled the plug on the project - it's almost more of a boost to NV than anything else, AMD really need people to be writing code on ROCm to deploy on AMD datacenter hardware).
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Show HN: Comflowy โ A ComfyUI Tutorial for Beginners
While I currently use SD.Next[1], I have tested ComfyUI locally with my AMD card. The UI can be daunting, but you learn quite a great deal about how a Stable Diffusion pipeline works. In addition some innovations and advances find their way into ComfyUI first.
[1] https://github.com/vladmandic/automatic
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Just me or SDXL is bad for rendering trees, grasses, vegetation in general ? Looks a stop motion or unfinished painting. How can I fix it ?
I used SD.NEXT ( https://github.com/vladmandic/automatic ) and https://civitai.com/models/82098/add-more-details-detail-enhancer-tweaker-lora and epicphotogasm_lastUnicorn
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Is SDXL supposed to be this slow on my system?
I found this thread on GitHub talking about how this was fixed in the latest version with an optional setting. I tried enabling it, as they mentioned, but it just resulted in an immediate CUDA out of memory error when starting generation. So it seems I'm actually needing the shared memory, which I assume is my issue.
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Another Monday, another big release from SDNext!
As always, do check out our more detailed changelog, give us a quick install from our Repo, and stop by our Discord Server for any questions or help you may need.
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What's the best stable diffusion client for base m1 MacBook air?
SD.Next
- Intel Arc 770 with Linux Mint, support requested!
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SDNext - Controlnet keeps being disabled after installing SDXL ?
Today I finally wanted to give SDXL a chance, so I set everythin up according to Vladmandic's Wiki https://github.com/vladmandic/automatic/wiki/SD-XL
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Vlad SD.Next SDXL DirectML: 'StableDiffusionXLPipeline' object has no attribute 'alphas_cumprod'
I'm trying to get SDXL working on Vlad's SDNext, but I keep getting the error in the title when trying to run basic operations. I'm not sure what's going on, I followed his guide for it to a T.
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[P] Stable Diffusion XL (SDXL) Benchmark - 769 images per dollar on consumer GPUs
We used an inference container based on SDNext, along with a custom worker written in Typescript that implemented the job processing pipeline. The worker used HTTP to communicate with both the SDNext container and with our batch framework.
What are some alternatives?
sd-webui-modelscope-text2video - Auto1111 extension consisting of implementation of text2video diffusion models (like ModelScope or VideoCrafter) using only Auto1111 webui dependencies [Moved to: https://github.com/deforum-art/sd-webui-text2video]
SHARK - SHARK - High Performance Machine Learning Distribution
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion-webui-colab - stable diffusion webui colab
VideoCrafter - VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
kohya_ss
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui-ux - Stable Diffusion web UI UX
Pallaidium - Generative AI for the Blender VSE: Text, video or image to video, image and audio in Blender Video Sequence Editor.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
stable-diffusion-webui-wd14-tagger - Labeling extension for Automatic1111's Web UI