Update app.py

app.py

@@ -1,58 +1,52 @@
 import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
 import subprocess
-import os
 
-# Path to save the generated video
-output_dir = './results'
+# Carica il modello HunyuanVideo
+model = torch.hub.load('tencent/HunyuanVideo', 'HunyuanVideo', pretrained=True)
 
-# Ensure the output directory exists
-os.makedirs(output_dir, exist_ok=True)
-
-# Define the function to generate the video
-def generate_video(prompt, video_size, video_length, infer_steps, seed, save_path):
-    # Command to run the HunyuanVideo script
+# Definisci la funzione per generare video
+def generate_video(prompt, video_size=(720, 1280), video_length=129, infer_steps=50):
+    # Prepara il prompt per il modello
+    prompt = prompt.strip()
+    
+    # Genera il video utilizzando il modello
     command = [
         "python3", "sample_video.py",
-        "--prompt", prompt,
-        "--video-size", video_size,
+        "--video-size", str(video_size[0]), str(video_size[1]),
         "--video-length", str(video_length),
         "--infer-steps", str(infer_steps),
-        "--seed", str(seed),
-        "--save-path", save_path
+        "--prompt", prompt,
+        "--flow-reverse",
+        "--use-cpu-offload",
+        "--save-path", "./results"
     ]
     
-    # Run the video generation process
-    try:
-        subprocess.run(command, check=True)
-        # Return the path to the generated video file
-        generated_video_path = os.path.join(save_path, "generated_video.mp4")
-        return generated_video_path
-    except subprocess.CalledProcessError as e:
-        return f"Error generating video: {e}"
-
-# Create the Gradio interface
-def create_interface():
-    # Define input components
-    prompt_input = gr.Textbox(label="Prompt", placeholder="Enter the prompt for the video.")
-    video_size_input = gr.Textbox(label="Video Size", value="720 1280")
-    video_length_input = gr.Slider(label="Video Length", minimum=1, maximum=200, value=129)
-    infer_steps_input = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=30)
-    seed_input = gr.Slider(label="Seed", minimum=0, maximum=1000, value=0)
-    save_path_input = gr.Textbox(label="Save Path", value=output_dir)
-
-    # Define output component
-    output_video = gr.Video(label="Generated Video")
-
-    # Create Gradio interface
-    interface = gr.Interface(
-        fn=generate_video,
-        inputs=[prompt_input, video_size_input, video_length_input, infer_steps_input, seed_input, save_path_input],
-        outputs=[output_video],
-        live=True
-    )
+    # Esegue il comando
+    subprocess.run(command)
     
-    # Launch the interface
-    interface.launch()
+    # Carica il video generato
+    video_path = "./results/generated_video.mp4"
+    
+    # Ritorna il video generato
+    return video_path
+
+# Crea l'interfaccia Gradio
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="Inserisci il prompt", placeholder="Un gatto cammina sull'erba, stile realistico."),
+        gr.Dropdown(label="Dimensione del video", choices=[(720, 1280), (544, 960)], value=(720, 1280)),
+        gr.Slider(label="Lunghezza del video (frame)", minimum=1, maximum=300, value=129),
+        gr.Slider(label="Passi di inferenza", minimum=1, maximum=100, value=50)
+    ],
+    outputs=gr.Video(label="Video generato"),
+    title="Generazione di video con HunyuanVideo",
+    description="Genera video utilizzando il modello HunyuanVideo fornendo un prompt di testo."
+)
 
-if __name__ == "__main__":
-    create_interface()
+# Avvia l'applicazione Gradio
+iface.launch()