gping VS generative-models

gping is a souped-up version of the traditional ping utility that graphs network latency for multiple hosts as well as execution time for commands, with the option of custom colors. Our thanks for the suggestion go to fudgecakekistan.

This sounds similar to what I had in our neighborhood. Turned out there was damage on the wire up on the pole, and it started disconnecting at the break point as the wire heated up during the day and would reconnect normal as it cooled in the evening until the next day as the sun came up around 8-10am. I was able to monitor and graph the lost packets and within the terminal with https://github.com/orf/gping during the worst time period. You may go in to test at the wrong time of day and find no damaged wire but try going during the heat up time of day. Ask customers when the worst time is. I’m not a technician but a customer who happens to do IT work. I finally got a good lead tech from the cable provider to check the cable at just the right time of day and they found the break in the cable. There also was a lot of noise in the signal in general at certain frequencies. Our neighborhood now has perfect internet :D

using gping (https://github.com/orf/gping) to wifi router:

Latency Tool: gping

https://github.com/orf/gping - but probably not for 100 IPs..

git clone https://github.com/Stability-AI/generative-models.git && cd generative-models

Generative Models by Stability AI Github Repository

# 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

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

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 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: