Update app.py

app.py

@@ -31,30 +31,41 @@ import torch
 import imageio
 from diffusers import TextToVideoZeroPipeline
 import numpy as np
+import gradio as gr
 
 model_id = "runwayml/stable-diffusion-v1-5"
 pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 seed = 0
 video_length = 8
 chunk_size = 4
-prompt = "A panda is playing guitar on times square"
-
-# Generate the video chunk-by-chunk
-result = []
-chunk_ids = np.arange(0, video_length, chunk_size - 1)
-generator = torch.Generator(device="cuda")
-for i in range(len(chunk_ids)):
-    print(f"Processing chunk {i + 1} / {len(chunk_ids)}")
-    ch_start = chunk_ids[i]
-    ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1]
-    # Attach the first frame for Cross Frame Attention
-    frame_ids = [0] + list(range(ch_start, ch_end))
-    # Fix the seed for the temporal consistency
-    generator.manual_seed(seed)
-    output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)
-    result.append(output.images[1:])
-
-# Concatenate chunks and save
-result = np.concatenate(result)
-result = [(r * 255).astype("uint8") for r in result]
-imageio.mimsave("video.mp4", result, fps=4)
+def text_video(prompt):
+
+
+
+    # Generate the video chunk-by-chunk
+    result = []
+    chunk_ids = np.arange(0, video_length, chunk_size - 1)
+    generator = torch.Generator(device="cuda")
+    for i in range(len(chunk_ids)):
+        print(f"Processing chunk {i + 1} / {len(chunk_ids)}")
+        ch_start = chunk_ids[i]
+        ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1]
+        # Attach the first frame for Cross Frame Attention
+        frame_ids = [0] + list(range(ch_start, ch_end))
+        # Fix the seed for the temporal consistency
+        generator.manual_seed(seed)
+        output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)
+        result.append(output.images[1:])
+    
+    # Concatenate chunks and save
+    result = np.concatenate(result)
+    result = [(r * 255).astype("uint8") for r in result]
+    imageio.mimsave("video.mp4", result, fps=4)
+
+result = gr.Video(label="Generated Video")
+gr.Interface(
+    fn=text_video,
+    inputs=gr.Textbox(label="어떤 비디오를 생성할까요? : "),
+    outputs=result
+    
+).launch()