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Runpod quick commands

  • This will purge your python cache : pip cache purge
  • This will turn of Pod after 2 minutes : sleep 2m; runpodctl stop pod $RUNPOD_POD_ID
  • This will turn of Pod after 2 hours : sleep 2h; runpodctl stop pod $RUNPOD_POD_ID
  • To download a google drive file into RunPod : gdown id_of_the_file - you can see id in the url when you copy

Download civitai model

wget -O "/workspace/stable-diffusion-webui/models/Stable-diffusion/Consistent-Factor_v5.safetensors" https://civitai.com/api/download/models/89824

wget https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -O /workspace/stable-diffusion-webui/models/VAE/vae-ft-mse-840000-ema-pruned.safetensors

wget https://huggingface.co/SG161222/Realistic_Vision_V2.0/resolve/main/Realistic_Vision_V2.0.safetensors -O /workspace/stable-diffusion-webui/models/Stable-diffusion/Realistic_Vision_V2.0.safetensors

yes | apt install libcudnn8=8.9.2.26-1+cuda11.8 libcudnn8-dev=8.9.2.26-1+cuda11.8

pip uninstall torch torchvision torchaudio

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

git clone https://github.com/d8ahazard/sd_dreambooth_extension /workspace/stable-diffusion-webui/extensions/sd_dreambooth_extension

pip install -r /workspace/stable-diffusion-webui/extensions/sd_dreambooth_extension/requirements.txt

pip install xformers==dev

Install Tensorflow With GPU On Windows

python -m pip install "tensorflow<2.11

Below command will extract every frame of a 30 FPS video as PNG with ffmpeg

ffmpeg -i "org.mp4" -vf "fps=30.00" -start_number 100000 -q:v 1 %06d.png

cuda check script

dont forget to change cudnn file path

import ctypes
import torch

def get_cudnn_version(file_path):
    try:
        cudnn = ctypes.WinDLL(file_path)
        cudnn_version = cudnn.cudnnGetVersion()
        major = cudnn_version // 1000
        minor = (cudnn_version // 100) % 10
        patch = cudnn_version % 100
        return f"CUDNN version: {major}.{minor}.{patch}"
    except OSError as e:
        return f"Error: {str(e)}"

def display_cuda_info():
    if torch.cuda.is_available():
        print("CUDA is available on this system.")
        
        # CUDA version
        print("CUDA Version:", torch.version.cuda)
        
        # Number of CUDA-enabled GPUs
        num_gpus = torch.cuda.device_count()
        print("Number of GPUs:", num_gpus)
        
        # GPU details
        for i in range(num_gpus):
            gpu_device = torch.cuda.get_device_name(i)
            print("\nGPU", i+1)
            print("Name:", gpu_device)
            
            gpu_memory = torch.cuda.get_device_properties(i).total_memory
            print("Memory:", round(gpu_memory/1024**3, 1), "GB")
            
            gpu_compute_capability = torch.cuda.get_device_capability(i)
            print("Compute Capability:", gpu_compute_capability)
            
            # CUDNN version
            cudnn_file_path = r"C:\automatic_web_ui\venv\Lib\site-packages\torch\lib\cudnn64_8.dll"
            cudnn_version = get_cudnn_version(cudnn_file_path)
            print(cudnn_version)
            
    else:
        print("CUDA is not available on this system.")

# Display CUDA and CUDNN information
display_cuda_info()