Update base/app.py

base/app.py

@@ -13,9 +13,6 @@ from huggingface_hub import snapshot_download
 config_path = "./base/configs/sample.yaml"
 args = OmegaConf.load("./base/configs/sample.yaml")
 device = "cuda" if torch.cuda.is_available() else "cpu"
-### download models
-# snapshot_download('Vchitect/LaVie',cache_dir='./pretrained_models')
-# snapshot_download('CompVis/stable-diffusion-v1-4',cache_dir='./pretrained_models')
 
 # ------- get model ---------------
 model_t2V = model_t2v_fun(args)
@@ -45,13 +42,8 @@ def infer(prompt, seed_inp, ddim_steps,cfg):
     if not os.path.exists(args.output_folder):
         os.mkdir(args.output_folder)
     torchvision.io.write_video(args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4', videos[0], fps=8)
-    # imageio.mimwrite(args.output_folder + prompt.replace(' ', '_') + '.mp4', videos[0], fps=8)
-    # video = cv2.VideoCapture(args.output_folder + prompt.replace(' ', '_') + '.mp4')
-    # video = imageio.get_reader(args.output_folder + prompt.replace(' ', '_') + '.mp4',  'ffmpeg')
 
 
-    # video = model_t2V(prompt, seed_inp, ddim_steps)
-
     return args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4'
 
 print(1)
@@ -81,7 +73,6 @@ title = """
     </div>
 """
 
-# print(1)
 with gr.Blocks(css='style.css') as demo:
     gr.Markdown("<font color=red size=10><center>LaVie: Text-to-Video generation</center></font>")
     with gr.Column():
@@ -97,58 +88,15 @@ with gr.Blocks(css='style.css') as demo:
                 cfg = gr.Number(label="guidance_scale",value=7.5)
                 # seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=400, elem_id="seed-in")
 
-                # with gr.Row():
-                #     # control_task = gr.Dropdown(label="Task", choices=["Text-2-video", "Image-2-video"], value="Text-2-video", multiselect=False, elem_id="controltask-in")
-                #     ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=250, step=1)
-                #     seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456, elem_id="seed-in")
-                
-                # ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=250, step=1)
-                # ex = gr.Examples(
-                #     examples = [['a corgi walking in the park at sunrise, oil painting style',400,50,7],
-                #             ['a cut teddy bear reading a book in the park, oil painting style, high quality',700,50,7],
-                #             ['an epic tornado attacking above a glowing city at night, the tornado is made of smoke, highly detailed',230,50,7],
-                #             ['a jar filled with fire, 4K video, 3D rendered, well-rendered',400,50,7],
-                #             ['a teddy bear walking in the park, oil painting style, high quality',400,50,7],
-                #             ['a teddy bear walking on the street, 2k, high quality',100,50,7],
-                #             ['a panda taking a selfie, 2k, high quality',400,50,7],
-                #             ['a polar bear playing drum kit in NYC Times Square, 4k, high resolution',400,50,7],
-                #             ['jungle river at sunset, ultra quality',400,50,7],
-                #             ['a shark swimming in clear Carribean ocean, 2k, high quality',400,50,7],
-                #             ['A steam train moving on a mountainside by Vincent van Gogh',230,50,7],
-                #             ['a confused grizzly bear in calculus class',1000,50,7]],
-                #     fn = infer,
-                #     inputs=[prompt, seed_inp, ddim_steps,cfg],
-                #     # outputs=[video_out],
-                #     cache_examples=False,
-                #     examples_per_page = 6
-                # )
-                # ex.dataset.headers = [""]
 
             with gr.Column():
                 submit_btn = gr.Button("Generate video")
                 clean_btn = gr.Button("Clean video")
-            # submit_btn = gr.Button("Generate video", size='sm')
-                # video_out = gr.Video(label="Video result", elem_id="video-output", height=320, width=512)
                 video_out = gr.Video(label="Video result", elem_id="video-output")
-            # with gr.Row():
-            #     video_out = gr.Video(label="Video result", elem_id="video-output", height=320, width=512)
-            #     submit_btn = gr.Button("Generate video", size='sm')
-            
 
-                # video_out = gr.Video(label="Video result", elem_id="video-output", height=320, width=512)
             inputs = [prompt, seed_inp, ddim_steps,cfg]
             outputs = [video_out]
-        # gr.Examples(
-        #     value = [['An astronaut riding a horse',123,50],
-        #              ['a panda eating bamboo on a rock',123,50],
-        #              ['Spiderman is surfing',123,50]],
-        #     label = "example of sampling",
-        #     show_label = True,
-        #     headers = ['prompt','seed','steps'],
-        #     datatype = ['str','number','number'],
-        #     row_count=4,
-        #     col_count=(3,"fixed")
-        # )
+
         ex = gr.Examples(
             examples = [['a corgi walking in the park at sunrise, oil painting style',400,50,7],
                     ['a cut teddy bear reading a book in the park, oil painting style, high quality',700,50,7],
@@ -169,8 +117,6 @@ with gr.Blocks(css='style.css') as demo:
         )
         ex.dataset.headers = [""]
         
-    # control_task.change(change_task_options, inputs=[control_task], outputs=[canny_opt, hough_opt, normal_opt], queue=False)
-    # submit_btn.click(clean, inputs=[], outputs=[video_out], queue=False)
     clean_btn.click(clean, inputs=[], outputs=[video_out], queue=False)
     submit_btn.click(infer, inputs, outputs)
     # share_button.click(None, [], [], _js=share_js)