Create app.py

app.py

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+import torch
+import gradio as gr
+from diffnext.pipelines import NOVAPipeline
+from diffnext.utils import export_to_image, export_to_video
+
+# Inizializzazione del modello
+model_id = "BAAI/nova-d48w1024-osp480"
+model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
+pipe = NOVAPipeline.from_pretrained(model_id, **model_args)
+pipe = pipe.to("cuda")
+
+# Funzioni per generare immagine e video
+def generate_image(prompt: str):
+    image = pipe(prompt, max_latent_length=1).frames[0, 0]
+    export_to_image(image, "output.jpg")
+    return "output.jpg"
+
+def generate_video(prompt: str):
+    video = pipe(prompt, max_latent_length=9).frames[0]
+    export_to_video(video, "output.mp4", fps=12)
+    return "output.mp4"
+
+def generate_video_high_quality(prompt: str):
+    video = pipe(
+        prompt,
+        max_latent_length=9,
+        num_inference_steps=128,
+        num_diffusion_steps=100,
+    ).frames[0]
+    export_to_video(video, "output_v2.mp4", fps=12)
+    return "output_v2.mp4"
+
+
+)
+
+iface3 = gr.Interface(
+    fn=generate_video_high_quality,
+    inputs=gr.Textbox(label="Enter Prompt"),
+    outputs=gr.Video(label="High Quality Generated Video"),
+    live=True
+)
+
+
+iface3.launch(share=True)