temporal-shift-module
generative-models
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temporal-shift-module | generative-models | |
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3 | 21 | |
2,019 | 22,196 | |
0.9% | 9.1% | |
3.0 | 7.6 | |
7 months ago | 10 days ago | |
Python | Python | |
MIT License | MIT License |
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temporal-shift-module
- Stable Video Diffusion
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Can two-stream networks trained for video action recognition be used for real-time usecases?
My question mostly has to do with optical flow. One of the two-stream networks I'm interested in trying out is TSN-TSM, as there are pre-trained weights available for it on the Assembly101 dataset released a few months ago.
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I am having a hard time understanding this paper(Temporal shift module). Can some who have read it before or willing to read it explain me better in a more elaborate way?
This is the paper. (https://arxiv.org/abs/1811.08383). Here they are talking about how they can achieve temporal modelling by moving channels, which I assume are the RGB channels across frames. But I am super confused by the lingo. Here is the repo (https://github.com/mit-han-lab/temporal-shift-module). I can't give better rewards except virtual hugs. Thank you.
generative-models
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Creating Videos with Stable Video Diffusion
git clone https://github.com/Stability-AI/generative-models.git && cd generative-models
- Show HN: I have created a free text-to-image website that supports SDXL Turbo
- How To Increase Performance Time on MacOS
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Introducing Stable Video Diffusion: Stability AI's New AI Research Tool for Image-to-Video Synthesis
Generative Models by Stability AI Github Repository
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image-to-video tutorial
# clone SD repo !git clone https://github.com/Stability-AI/generative-models.git # cd into working directory # the % sets the pwd globally as usually each command is run in a subshell in google colab %cd /content/generative-models/ # installing dependencies !pip install -r requirements/pt2.txt !pip install . # HACK # I was getting ModuleNotFoundError: No module named 'scripts' # This is what ChatGPT suggested (let me know if there is a better way) file_path = '/content/generative-models/scripts/sampling/simple_video_sample.py' new_text = "import sys\nsys.path.append('/content/generative-models')\n\n" with open(file_path, 'r') as file: original_content = file.read() updated_content = new_text + original_content with open(file_path, 'w') as file: file.write(updated_content) # Need to create a checkpoints/ folder - that is where the system looks for weights import os dir_name = 'checkpoints' if not os.path.exists(dir_name): os.makedirs(dir_name) print(f"Directory '{dir_name}' created") else: print(f"Directory '{dir_name}' already exists") # Download weights into checkpoints/ folder from huggingface_hub import hf_hub_download hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid", filename="svd.safetensors", local_dir="checkpoints", local_dir_use_symlinks=False) # I can't remember if this step is needed but it aims to reduce the memory footprint of pytorch # I kept getting CUDA out of memory # I got these instructions from the out of memory error message os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512' print(os.environ['PYTORCH_CUDA_ALLOC_CONF']) # Inside of scripts/sampling/simple_video_sample.py you need to make 2 updates 1. input_path (line 26): update to the location of your file (I attached Gdrive so mine was "/content/drive/MyDrive/examples/car.jpeg" 2. decoding_t (line 34): update it to 5. you need to do this for memory preservation (CUDA out of memory). I'm not sure if 5 is the best value but it worked for me # Finally generate the video (output will be in the outputs/ folder) !python scripts/sampling/simple_video_sample.py
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Stable Video Diffusion
It looks like the huggingface page links their github that seems to have python scripts to run these: https://github.com/Stability-AI/generative-models
- GitHub - Stability-AI/generative-models: Generative Models by Stability AI
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How does ComfyUI load SDXL 1.0 so VRAM-efficiently? How do I do the same in vanilla python code?
However, when using the example code from HuggingFace or setting up stuff from the StabilityAI/generative-models repo in a jupyter notebook, I end up using 21 GB of VRAM just for running the default pipeline (with no base model output). If I try to run the extra `base.vae.decode(base_latents)` after generation to get unrefined outputs, I get a CUDA out of memory error as it blows past the 24GB of my NVIDIA RTX 3090.
- SDXL 1.0 is out!
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SDXL 0.9 Anyone having luck NOT centering subjects?
SDXL uses cropping information as part of the conditioning. Images were randomly cropped during training and the coordinates of the crop were included as two integers at the end of the conditioning vector. If you're using ComfyUI you can use the CLIPTextEncodeSDXL node to specify where the upper left corner of the image should appear to be in relation to some hypothetical uncropped image. Here's a figure with examples from the report on SDXL:
What are some alternatives?
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
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python-socketio - Python Socket.IO server and client
wizmap - Explore and interpret large embeddings in your browser with interactive visualization! 📍
react-native-sensors - A developer friendly approach for sensors in React Native
evernote-ai-chatbot
conifer - Fast inference of Boosted Decision Trees in FPGAs
gping - Ping, but with a graph
conifer - Collect and revisit web pages.
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
gsgen - [CVPR 2024] Text-to-3D using Gaussian Splatting
xgen - Salesforce open-source LLMs with 8k sequence length.