Update app_4k.py

app_4k.py

@@ -223,7 +223,6 @@ def generate_video(prompt, height, width,
                    negative_prompt=default_negative_prompt,
                    duration_seconds=2, guidance_scale=1,
                    steps=4, seed=42, randomize_seed=False,
-                   use_ultrawan_4k=False,
                    progress=gr.Progress(track_tqdm=True)):
 
     if not prompt or prompt.strip() == "":
@@ -238,11 +237,11 @@ def generate_video(prompt, height, width,
         duration_seconds = min(duration_seconds, LIMITED_MAX_DURATION)
         steps = min(steps, LIMITED_MAX_STEPS)
 
-    # Ensure dimensions are multiples of MOD_VALUE
+    # Ensure dimensions are aligned
     target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
     target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
 
-    generator_pipe = pipe  # your model pipeline
+    generator_pipe = pipe
 
     num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
 
@@ -258,18 +257,18 @@ def generate_video(prompt, height, width,
             generator=torch.Generator(device="cuda").manual_seed(current_seed)
         ).frames[0]
 
-    # Export video
+    # Save video to temporary file
     with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
         video_path = tmpfile.name
 
     export_to_video(output_frames_list, video_path, fps=FIXED_OUTPUT_FPS)
 
-    # Optional 4K upscale
-    if use_ultrawan_4k:
-        upscale_to_4k_and_replace(video_path)
+    # Always upscale to 4K
+    upscale_to_4k_and_replace(video_path)
 
     return video_path, current_seed
 
+