Update app.py

app.py

@@ -130,68 +130,29 @@ def get_duration(
     return steps * 15
 
 @spaces.GPU(duration=get_duration)
+
 def generate_video(
     prompt,
     negative_prompt=default_negative_prompt,
-    duration_seconds = MAX_DURATION,
-    guidance_scale = 1,
-    guidance_scale_2 = 3,
-    steps = 4,
-    seed = 42,
-    randomize_seed = False,
+    duration_seconds=MAX_DURATION,
+    guidance_scale=1,
+    guidance_scale_2=3,
+    steps=4,
+    seed=42,
+    randomize_seed=False,
     progress=gr.Progress(track_tqdm=True),
 ):
-    """
-    Generate a video from a text prompt using the Wan 2.2 14B T2V model with Lightning LoRA.
-    
-    This function takes an input prompt and generates a video animation based on the provided
-    prompt and parameters. It uses an FP8 qunatized Wan 2.2 14B Text-to-Video model with Lightning LoRA
-    for fast generation in 4-8 steps.
-    
-    Args:
-        prompt (str): Text prompt describing the desired animation or motion.
-        negative_prompt (str, optional): Negative prompt to avoid unwanted elements. 
-            Defaults to default_negative_prompt (contains unwanted visual artifacts).
-        duration_seconds (float, optional): Duration of the generated video in seconds.
-            Defaults to 2. Clamped between MIN_FRAMES_MODEL/FIXED_FPS and MAX_FRAMES_MODEL/FIXED_FPS.
-        guidance_scale (float, optional): Controls adherence to the prompt. Higher values = more adherence.
-            Defaults to 1.0. Range: 0.0-20.0.
-        guidance_scale_2 (float, optional): Controls adherence to the prompt. Higher values = more adherence.
-            Defaults to 1.0. Range: 0.0-20.0.
-        steps (int, optional): Number of inference steps. More steps = higher quality but slower.
-            Defaults to 4. Range: 1-30.
-        seed (int, optional): Random seed for reproducible results. Defaults to 42.
-            Range: 0 to MAX_SEED (2147483647).
-        randomize_seed (bool, optional): Whether to use a random seed instead of the provided seed.
-            Defaults to False.
-        progress (gr.Progress, optional): Gradio progress tracker. Defaults to gr.Progress(track_tqdm=True).
-    
-    Returns:
-        tuple: A tuple containing:
-            - video_path (str): Path to the generated video file (.mp4)
-            - current_seed (int): The seed used for generation (useful when randomize_seed=True)
-    
-    Raises:
-        gr.Error: If input_image is None (no image uploaded).
-    
-    Note:
-        - The function automatically resizes the input image to the target dimensions
-        - Frame count is calculated as duration_seconds * FIXED_FPS (24)
-        - Output dimensions are adjusted to be multiples of MOD_VALUE (32)
-        - The function uses GPU acceleration via the @spaces.GPU decorator
-        - Generation time varies based on steps and duration (see get_duration function)
-    """
-    print("promot is ")
-    print(prompt)
-    
+    print("Prompt:", prompt)
+
     num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
     current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
 
+    # Generate frames
     output_frames_list = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
-        height=480,
-        width=832,
+        height=LANDSCAPE_HEIGHT,
+        width=LANDSCAPE_WIDTH,
         num_frames=num_frames,
         guidance_scale=float(guidance_scale),
         guidance_scale_2=float(guidance_scale_2),
@@ -199,14 +160,20 @@ def generate_video(
         generator=torch.Generator(device="cuda").manual_seed(current_seed),
     ).frames[0]
 
+  
+    video_path_raw = os.path.join(tempfile.gettempdir(), f"video_raw_{uuid.uuid4().hex[:8]}.mp4")
+    export_to_video(output_frames_list, video_path_raw, fps=FIXED_FPS)
+
    
+    video_path = os.path.join(tempfile.gettempdir(), f"video_{uuid.uuid4().hex[:8]}.mp4")
+    improve_video_quality(video_path_raw, video_path, bitrate="8M")
+
     
-    video_path = os.path.join(tempfile.gettempdir(), f"video_{current_seed}.mp4")
-    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
     hf_folder = upload_to_hf(video_path, prompt)
 
     return video_path, current_seed
 
+
 with gr.Blocks() as demo:
     gr.Markdown("# Fast 4 steps Wan 2.2 T2V (14B) with Lightning LoRA")
     gr.Markdown("run Wan 2.2 in just 4-8 steps, with [Wan 2.2 Lightning LoRA](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Wan22-Lightning), fp8 quantization & AoT compilation - compatible with 🧨 diffusers and ZeroGPU⚡️")