Update app.py

app.py

@@ -56,7 +56,7 @@ def sample_denoising_batch(model, noise_shape, condition, *args,
 @torch.no_grad()
 def sample_text2video(model, prompt, n_samples, batch_size,
                       sample_type="ddim", sampler=None, 
-                      ddim_steps=50, eta=1.0, cfg_scale=15.0, 
+                      ddim_steps=50, eta=1.0, cfg_scale=7.5, 
                       decode_frame_bs=1,
                       ddp=False, all_gather=True, 
                       batch_progress=True, show_denoising_progress=False,
@@ -109,12 +109,22 @@ def save_results(videos,
     return os.path.join(save_subdir, f"{save_name}_{i:03d}.mp4")
 
 def get_video(prompt):
+    seed = 1000
+    seed_everything(seed)
     samples = sample_text2video(model, prompt, n_samples = 1, batch_size = 1,
                           sampler=ddim_sampler,
                           )
     return save_results(samples)
 
+title = 'Latent Video Diffusion Models'
+DESCRIPTION  = '<p>This model can only be used for non-commercial purposes. To learn more about the model, take a look at the <a href="https://github.com/VideoCrafter/VideoCrafter" style="text-decoration: underline;" target="_blank">model card</a>.</p>'
+
 prompt_inp = gr.Textbox(label = "Prompt")
 result = gr.Video(label='Result')
-iface = gr.Interface(fn=get_video, inputs=[prompt_inp], outputs=[result])
+iface = gr.Interface(fn=get_video, 
+                     inputs=[prompt_inp], 
+                     outputs=[result], 
+                     title = title, 
+                     description = DESCRIPTION,
+                     examples = [["An astronaut riding a horse"]])
 iface.launch()