txt2vid_2.py

import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
import shutil  # Import shutil for cross-filesystem moves

# Clear GPU memory
torch.cuda.empty_cache()

# Set memory allocation configuration
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True

# Load the pipeline
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe = pipe.to("cuda")

# Generate video frames
prompt = "Spiderman is surfing"
video_frames = pipe(prompt, num_frames=75).frames[0]

# Export video to a temporary path
video_path = export_to_video(video_frames)  # This returns the path to the saved video

# Define the desired output path
output_path = "/kaggle/working/spiderman_surfing.mp4"

# Move the video to the desired output path using shutil.move
shutil.move(video_path, output_path)
print(f"Video saved at: {output_path}")

# Utility function to convert bytes to gigabytes
def bytes_to_giga_bytes(bytes):
    return bytes / 1024 / 1024 / 1024

# Print memory usage
print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")