Update app.py

app.py

@@ -87,12 +87,13 @@ def generate_image_from_text(prompt, seed=0):
     ).images[0]
     return image
 
-@spaces.GPU(duration=600)
+import torch
+
 def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, seed=123, video_length=2, fs=8):
     translated_prompt = translate_prompt(prompt)
     print(f"Translated prompt: {translated_prompt}")
     resolution = (576, 1024)
-    save_fs = fs  # fs를 저장
+    save_fs = torch.tensor(fs)  # fs를 tensor로 변환
     seed_everything(seed)
     transform = transforms.Compose([
         transforms.Resize(min(resolution), antialias=True),
@@ -105,7 +106,7 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, seed=123, video_lengt
         steps = 60
     batch_size = 1
     channels = model.model.diffusion_model.out_channels
-    frames = int(video_length * save_fs)
+    frames = int(video_length * fs)
     h, w = resolution[0] // 8, resolution[1] // 8
     noise_shape = [batch_size, channels, frames, h, w]
     with torch.no_grad(), torch.cuda.amp.autocast():
@@ -121,9 +122,10 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, seed=123, video_lengt
         cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat], "fs": save_fs}
         batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
         video_path = './output.mp4'
-        save_videos(batch_samples, './', filenames=['output'], fps=save_fs)
+        save_videos(batch_samples, './', filenames=['output'], fps=fs)
     return video_path
 
+
 css = """
 .tab-nav {
     border-bottom: 2px solid #ddd;
@@ -158,7 +160,7 @@ css = """
 /* 탭별 색상 */
 .tab-nav button:nth-child(1) { border-top: 3px solid #ff6b6b; }
 .tab-nav button:nth-child(2) { border-top: 3px solid #4ecdc4; }
-.tab-nav button:nth-child(3) { border-top: 3px solid #45b7d1; }
+.tab-nav button:nth-child(3) { border-top: 3px solid #f7b731; }
 """
 
 def infer_t2v(prompt, seed=123, steps=50, cfg_scale=7.5, eta=1.0, fs=8, video_length=2):