mirror of
https://github.com/THUDM/CogVideo.git
synced 2025-12-03 03:02:09 +08:00
857e858aa8
- Integrated progress tracking with upscale model loading. - Implemented conditional latent upscaling using the upscale model. - Processed batch video frames using PyTorch and converted them to PIL images. - load_torch_file for params_ema convert weights
16 lines
480 B
Python
16 lines
480 B
Python
import utils
|
|
|
|
import torch
|
|
|
|
from inference.gradio_composite_demo.rife_model import load_rife_model, rife_inference_with_path
|
|
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
model_path = "/media/gpt4-pdf-chatbot-langchain/ECCV2022-RIFE/train_log"
|
|
# video_path = "/media/gpt4-pdf-chatbot-langchain/CogVideo/inference/output/20240823_110325.mp4"
|
|
model = load_rife_model(model_path)
|
|
|
|
# rife_inference_with_path(model, video_path)
|