generative-models
graphic-walker
generative-models | graphic-walker | |
---|---|---|
21 | 20 | |
22,508 | 2,254 | |
4.4% | 2.6% | |
7.3 | 9.4 | |
27 days ago | 7 days ago | |
Python | TypeScript | |
MIT License | Apache License 2.0 |
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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:
graphic-walker
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Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL
[2] https://github.com/Kanaries/graphic-walker/issues/330
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Show HN: GPT and tableau-style interface in R for data visualization
GWalkR is an open-source R library that allows you to turn your data frame into a tableau style user interface for data exploration and visualization. It also allows you to analysis your data with natural language questions.
GWalkR is the R binding of graphic-walker: https://github.com/Kanaries/graphic-walker
- FLaNK Stack for 4th of July
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Observable Plot: The JavaScript library for exploratory data visualization
Big fan of D3.js and now there is Observable Plot! I am building several data visualization software for exploratory data analysis:
RATH, auto exploratory data analysis: https://github.com/Kanaries/Rath
GraphicWalker, embeddable data exploration component: https://github.com/Kanaries/graphic-walker
They are using vega-lite for now. But there is a limit of building more fancy and customized visualizations. It seems Plot has a more flexible layer based visualization system that can support larger design space.
Is Plot stable enough now to migrate from vega-lite based system to Plot based? Are there any large milestone or roadmap of Plot in future?
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Show HN: RATH – Open-Source Copilot and Autopilot for Data Analysis
+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.
+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.
RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.
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Hey /r/SQL! I created a tool for data analysts to save time and visualize data using DuckDB - looking for feedback
I know you said you *dont* want a tableau like interface, but in case you do this might be a cool open source project to check out: https://github.com/Kanaries/graphic-walker
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Show HN: Turn Your Pandas Dataframe to a Tableau-Style UI for Visual Analysis
> it seems like the heavy lifting is done by the web app here: https://github.com/Kanaries/graphic-walker
FWIW, both are made by the same entity, Kanaries.
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Tools for Integrating Interactive Dashboards on a Website without Licensing Problems?
You may try to use and modify based on the OpenSource Graphic Walker: https://github.com/Kanaries/graphic-walker
- Easier Data Visualization & Exploration in React: Graphic Walker
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Graphic Walker, A Different Type of Open Source Tableau Alternative
Graphic Walker is designed to be easy to embed in other applications as a React component. Check out the code here: https://github.com/Kanaries/graphic-walker
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.
Rath - Next generation of automated data exploratory analysis and visualization platform.
wizmap - Explore and interpret large embeddings in your browser with interactive visualization! 📍
superset - Apache Superset is a Data Visualization and Data Exploration Platform
evernote-ai-chatbot
streamlit - Streamlit — A faster way to build and share data apps.
gping - Ping, but with a graph
vega-embed - Publish Vega visualizations as embedded web components with interactive parameters.
xgen - Salesforce open-source LLMs with 8k sequence length.
pygwalker - PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis
configu - Open-source ConfigOps infrastructure ⚙️
pivottable - Open-source Javascript Pivot Table (aka Pivot Grid, Pivot Chart, Cross-Tab) implementation with drag'n'drop.