Create app.py

app.py

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+import os
+import gradio as gr
+from loguru import logger
+
+# Funzione per scaricare i modelli
+def download_models():
+    logger.info("Scaricamento dei modelli...")
+    os.system("apt update && apt install aria2 -y")
+    
+    base_url = "https://huggingface.co/camenduru/HunyuanVideo"
+    models = {
+        "transformers/mp_rank_00_model_states.pt": "ckpts/hunyuan-video-t2v-720p/transformers",
+        "vae/config.json": "ckpts/hunyuan-video-t2v-720p/vae",
+        "vae/pytorch_model.pt": "ckpts/hunyuan-video-t2v-720p/vae",
+        "text_encoder/config.json": "ckpts/text_encoder",
+        "text_encoder/generation_config.json": "ckpts/text_encoder",
+        "text_encoder/model-00001-of-00004.safetensors": "ckpts/text_encoder",
+        "text_encoder/model-00002-of-00004.safetensors": "ckpts/text_encoder",
+        "text_encoder/model-00003-of-00004.safetensors": "ckpts/text_encoder",
+        "text_encoder/model-00004-of-00004.safetensors": "ckpts/text_encoder",
+        "text_encoder/model.safetensors.index.json": "ckpts/text_encoder",
+        "text_encoder/special_tokens_map.json": "ckpts/text_encoder",
+        "text_encoder/tokenizer.json": "ckpts/text_encoder",
+        "text_encoder/tokenizer_config.json": "ckpts/text_encoder",
+    }
+
+    for file_path, folder in models.items():
+        os.makedirs(folder, exist_ok=True)
+        command = (
+            f"aria2c --console-log-level=error -c -x 16 -s 16 -k 1M "
+            f"{base_url}/resolve/main/{file_path} -d {folder} -o {os.path.basename(file_path)}"
+        )
+        logger.info(f"Scaricando: {file_path}")
+        os.system(command)
+    
+    logger.info("Download completato.")
+
+# Funzione per generare il video
+def generate_video(prompt, video_size, video_length, infer_steps, seed):
+    download_models()
+    logger.info("Clonazione del repository...")
+    os.system("git clone https://github.com/Tencent/HunyuanVideo /content/HunyuanVideo")
+    os.chdir("/content/HunyuanVideo")
+    
+    save_path = "./results/generated_video.mp4"
+    command = (
+        f"python sample_video.py "
+        f"--video-size {video_size[0]} {video_size[1]} "
+        f"--video-length {video_length} "
+        f"--infer-steps {infer_steps} "
+        f"--prompt '{prompt}' "
+        f"--flow-reverse "
+        f"--seed {seed} "
+        f"--use-cpu-offload "
+        f"--save-path {save_path}"
+    )
+    logger.info("Esecuzione del modello...")
+    os.system(command)
+
+    if os.path.exists(save_path):
+        return save_path
+    else:
+        logger.error("Video non generato correttamente.")
+        return None
+
+# Interfaccia Gradio
+def infer(prompt, width, height, video_length, infer_steps, seed):
+    video_size = (width, height)
+    video_path = generate_video(prompt, video_size, video_length, infer_steps, seed)
+    if video_path:
+        return video_path
+    return "Errore nella generazione del video."
+
+with gr.Blocks() as demo:
+    gr.Markdown("# HunyuanVideo - Generazione di video basati su testo")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Descrivi il tuo video (es. a cat is running, realistic.)")
+            width = gr.Slider(label="Larghezza Video", minimum=360, maximum=1920, step=1, value=720)
+            height = gr.Slider(label="Altezza Video", minimum=360, maximum=1080, step=1, value=1280)
+            video_length = gr.Slider(label="Durata Video (frames)", minimum=10, maximum=300, step=1, value=129)
+            infer_steps = gr.Slider(label="Passi di Inferenza", minimum=10, maximum=100, step=1, value=50)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=1000, step=1, value=0)
+            submit_btn = gr.Button("Genera Video")
+        with gr.Column():
+            output = gr.Video(label="Video Generato")
+
+    submit_btn.click(infer, inputs=[prompt, width, height, video_length, infer_steps, seed], outputs=output)
+
+demo.launch()