generative-models
mdBook
generative-models | mdBook | |
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21 | 101 | |
22,649 | 16,848 | |
4.4% | 2.5% | |
7.3 | 8.6 | |
about 1 month ago | 4 days ago | |
Python | Rust | |
MIT License | Mozilla Public 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
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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:
mdBook
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Ask HN: How do you organize software documentation at work?
I'm responsible for a number of Java products. I try to provide high-quality Javadoc for all public library interfaces, library user's guides where appropriate, and development guides for applications. The latter two take the form of MDBook documents (https://rust-lang.github.io/mdBook/), with the document source living in the GitHub repo so that it's tied to the particular software release in a natural way.
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Outline: Self hostable, realtime, Markdown compatible knowledge base
My org has used mdBook: https://rust-lang.github.io/mdBook/ (That link is itself a rendered mdBook, so that'll give you an idea of the feature set.)
(While it's definitely a Rust "thing", if you just have a set of .md files, all you need is a "SUMMARY.md" (which contains the ToC) and a small config file; i.e., you don't have to have any Rust code to use it, and it works fine without. We document a large, mostly non-Rust codebase with it.)
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Ask HN: Best tools for self-authoring books in 2023?
If you want the lowest friction, open source, easily extensible Markdown to Web, Kindle, PDF, etc. tool, highly recommend mdBook: https://github.com/rust-lang/mdBook it’s written in Rust, but you don’t have to know any Rust to use it. And then wing is all CSS; for which there are many good (free) themes.
- Early performance results from the prototype CHERI ARM Morello microarchitecture
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- MdBook – A command line tool to create books with Markdown
- MdBook Create book from Markdown files. Like Gitbook but implemented in Rust
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.
gitbook - The open source frontend for GitBook doc sites
wizmap - Explore and interpret large embeddings in your browser with interactive visualization! 📍
MkDocs - Project documentation with Markdown.
evernote-ai-chatbot
Wiki.js - Wiki.js | A modern and powerful wiki app built on Node.js
gping - Ping, but with a graph
bookdown - Authoring Books and Technical Documents with R Markdown
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
obsidian-releases - Community plugins list, theme list, and releases of Obsidian.
xgen - Salesforce open-source LLMs with 8k sequence length.
Docusaurus - Easy to maintain open source documentation websites.