parquet-format
generative-models
parquet-format | generative-models | |
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4 | 21 | |
1,655 | 22,508 | |
1.8% | 4.4% | |
7.2 | 7.3 | |
5 days ago | 26 days ago | |
Thrift | Python | |
Apache License 2.0 | MIT License |
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parquet-format
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Summing columns in remote Parquet files using DuckDB
Right, there's all sorts of metadata and often stats included in any parquet file: https://github.com/apache/parquet-format#file-format
The offsets of said metadata are well-defined (i.e. in the footer) so for S3 / blob storage so long as you can efficiently request a range of bytes you can pull the metadata without having to read all the data.
- FLaNK Stack for 4th of July
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I have question related to Parquet files and AWS Glue
As i read here https://github.com/apache/parquet-format/blob/master/LogicalTypes.md , they are store in Integer formats and these integers represent the number of days (for Date) or number of milliseconds, microseconds or nanoseconds (for DateTime) since 1970-01-01. This works as expected with the parquet file that written by our ETL tool from internal database --> S3, all Data/DateTime columns are Integers, means that in Glue Job, i have to convert these Integers back to Date/Datetime value to do some transformation on them. But when parquet files are written by Spark, they are Date/DateTime (or TimeStamp to be more concise) format not Integers (i checked by read these files again in other Glue Job) and that make me confused.
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Parquet: More than just “Turbo CSV”
Date is confusing with a timezone (UTC or otherwise) and the doco makes no such suggestion.
The Parquet datatypes documentation is pretty clear that there is a flag isAdjustedToUTC to define if the timestamp should be interpreted as having Instant semantics or Local semantics.
https://github.com/apache/parquet-format/blob/master/Logical...
Still no option to include a TZ offset in the data (so the same datum can be interpreted with both Local and Instant semantics) but not bad really.
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?
rapidgzip - Gzip Decompression and Random Access for Modern Multi-Core Machines
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.
xgen - Salesforce open-source LLMs with 8k sequence length.
wizmap - Explore and interpret large embeddings in your browser with interactive visualization! 📍
evernote-ai-chatbot
FastSAM - Fast Segment Anything
gping - Ping, but with a graph
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic