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app.py

@@ -2,13 +2,13 @@ import torch
 import gradio as gr
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
+import os
 
-# Отключение CUDA (GPU)
-#torch.device('cuda')
 
 def generate_video(prompt):
     # load pipeline
-    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")  #.device
+    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16,
+                                             variant="fp16")
     pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 
     # optimize for GPU memory
@@ -18,9 +18,17 @@ def generate_video(prompt):
     # generate
     video_frames = pipe(prompt, num_inference_steps=25, num_frames=200).frames
 
+    # get absolute path to current working directory
+    current_directory = os.getcwd()
+
+    # create directory to store video
+    video_directory = os.path.join(current_directory, "generated_videos")
+    os.makedirs(video_directory, exist_ok=True)
+
     # convert to video
-    video_path = export_to_video(video_frames)
+    video_path = export_to_video(video_frames, os.path.join(video_directory, "generated_video.mp4"))
     return video_path
 
-demo = gr.Interface(fn=generate_video, inputs="text", outputs="file")
-demo.launch(share=True)
+
+iface = gr.Interface(fn=generate_video, inputs="text", outputs="file")
+iface.launch()