Update base/app.py

base/app.py

@@ -1,116 +1,116 @@
-import gradio as gr
-from text_to_video import model_t2v_fun,setup_seed
-from omegaconf import OmegaConf
-import torch
-import imageio
-import os
-import cv2
-import torchvision
-config_path = "/mnt/petrelfs/zhouyan/project/lavie-release/base/configs/sample.yaml"
-args = OmegaConf.load("/mnt/petrelfs/zhouyan/project/lavie-release/base/configs/sample.yaml")
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# ------- get model ---------------
-model_t2V = model_t2v_fun(args)
-model_t2V.to(device)
-if device == "cuda":
-    model_t2V.enable_xformers_memory_efficient_attention()
-
-# model_t2V.enable_xformers_memory_efficient_attention()
-css = """
-h1 {
-  text-align: center;
-}
-#component-0 {
-  max-width: 730px;
-  margin: auto;
-}
-"""
-
-def infer(prompt, seed_inp, ddim_steps):
-
-
-    setup_seed(seed_inp)
-    videos = model_t2V(prompt, video_length=16, height = 320, width= 512, num_inference_steps=ddim_steps, guidance_scale=7).video
-    print(videos[0].shape)
-    if not os.path.exists(args.output_folder):
-        os.mkdir(args.output_folder)
-    torchvision.io.write_video(args.output_folder + prompt.replace(' ', '_') + '-.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.replace(' ', '_') + '-.mp4'
-
-print(1)
-
-# def clean():
-#     return gr.Image.update(value=None, visible=False), gr.Video.update(value=None)
-def clean():
-    return gr.Video.update(value=None)
-
-title = """
-    <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-        "
-        >
-        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-            Intern·Vchitect (Text-to-Video)
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        Apply Intern·Vchitect to generate a video 
-        </p>
-    </div>
-"""
-
-# print(1)
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown("<font color=red size=10><center>LaVie</center></font>")
-    with gr.Row(elem_id="col-container"):
- 
-        with gr.Column():
-                
-            prompt = gr.Textbox(value="a teddy bear walking on the street", label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in", min_width=200, lines=2)
-            
-            ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=50, step=1)
-            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)
-        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]
-        outputs = [video_out]
-
-        
-    # 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)
-
-    print(2)
-demo.queue(max_size=12).launch(server_name="0.0.0.0", server_port=7860)
-
-
+import gradio as gr
+from text_to_video import model_t2v_fun,setup_seed
+from omegaconf import OmegaConf
+import torch
+import imageio
+import os
+import cv2
+import torchvision
+config_path = "./base/configs/sample.yaml"
+args = OmegaConf.load("./base/configs/sample.yaml")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# ------- get model ---------------
+model_t2V = model_t2v_fun(args)
+model_t2V.to(device)
+if device == "cuda":
+    model_t2V.enable_xformers_memory_efficient_attention()
+
+# model_t2V.enable_xformers_memory_efficient_attention()
+css = """
+h1 {
+  text-align: center;
+}
+#component-0 {
+  max-width: 730px;
+  margin: auto;
+}
+"""
+
+def infer(prompt, seed_inp, ddim_steps):
+
+
+    setup_seed(seed_inp)
+    videos = model_t2V(prompt, video_length=16, height = 320, width= 512, num_inference_steps=ddim_steps, guidance_scale=7).video
+    print(videos[0].shape)
+    if not os.path.exists(args.output_folder):
+        os.mkdir(args.output_folder)
+    torchvision.io.write_video(args.output_folder + prompt.replace(' ', '_') + '-.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.replace(' ', '_') + '-.mp4'
+
+print(1)
+
+# def clean():
+#     return gr.Image.update(value=None, visible=False), gr.Video.update(value=None)
+def clean():
+    return gr.Video.update(value=None)
+
+title = """
+    <div style="text-align: center; max-width: 700px; margin: 0 auto;">
+        <div
+        style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+        "
+        >
+        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+            Intern·Vchitect (Text-to-Video)
+        </h1>
+        </div>
+        <p style="margin-bottom: 10px; font-size: 94%">
+        Apply Intern·Vchitect to generate a video 
+        </p>
+    </div>
+"""
+
+# print(1)
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown("<font color=red size=10><center>LaVie</center></font>")
+    with gr.Row(elem_id="col-container"):
+ 
+        with gr.Column():
+                
+            prompt = gr.Textbox(value="a teddy bear walking on the street", label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in", min_width=200, lines=2)
+            
+            ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=50, step=1)
+            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)
+        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]
+        outputs = [video_out]
+
+        
+    # 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)
+
+    print(2)
+demo.queue(max_size=12).launch(server_name="0.0.0.0", server_port=7860)
+
+