configu
generative-models
configu | generative-models | |
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17 | 21 | |
1,506 | 22,508 | |
1.3% | 4.4% | |
9.1 | 7.3 | |
2 days ago | 26 days ago | |
TypeScript | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
configu
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Hacktoberfest 2023: Where Open Source Enthusiasts of All Levels Unite
As we celebrate Hacktoberfest, we at Configu invite you to be part of our mission to redefine software configuration management. We've set out to tackle the persistent challenge of configuration chaos, and we're making strides every day. If you're searching for a place to make a significant impact this Hacktoberfest, consider Configu. Delve into our open-source repository, understand our vision, and contribute to shaping our journey. If you're unsure where to begin or need some help along the way, our Configu Discord community is always here to guide you. For newcomers, we recommend starting with issues labeled 'good-first-issues'.
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Unmasking Ghost Parameters, or How to Save Time and Money
Enter Configu, an open source implementation of the concept of Configuration-as-Code, ensuring that the code remains the source of truth. But we didn't stop there. We've just launched a new feature called configu find that takes configuration management to the next level.
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Shift from ENV Files to Configuration-as-Code
Thanks Geva for sharing my article.
In this piece, I delve into the world of application configuration, discussing the drawbacks of using environment variables stored in env files and introducing the powerful alternative - Configuration as Code (CaC).
Looking forward to your thoughts! and If you're intrigued, I invite you to explore our open-source project on GitHub: https://github.com/configu/configu
- GitHub - Configu: Open-source project that puts an end to your configuration Chaos
- Configu - Open-source project that puts an end to your configuration Chaos
- Configu - Unified all your configuration solutions under the same interface
- FLaNK Stack for 4th of July
- Configu: a simple, modern, and generic standard for managing and collaborating software configurations ⚙️✨
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Configu: Unleashing the Power of Configuration as Code
View on GitHub
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?
background-removal-js - Remove backgrounds from images directly in the browser environment with ease and no additional costs or privacy concerns. Explore an interactive demo.
wizmap - Explore and interpret large embeddings in your browser with interactive visualization! 📍
gping - Ping, but with a graph
evernote-ai-chatbot
xgen - Salesforce open-source LLMs with 8k sequence length.
hacktoberfest-data - Generating stats from the raw Hacktoberfest application data.
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic