Update app.py

app.py

@@ -103,7 +103,43 @@ def generate_video(
     randomize_seed = False,
     progress=gr.Progress(track_tqdm=True),
 ):
-   
+    """
+    Generate a video from an input image using the Wan 2.1 I2V model with CausVid LoRA.
+    
+    This function takes an input image and generates a video animation based on the provided
+    prompt and parameters. It uses the Wan 2.1 14B Image-to-Video model with CausVid LoRA
+    for fast generation in 4-8 steps.
+    
+    Args:
+        input_image (PIL.Image): The input image to animate. Will be resized to target dimensions.
+        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).
+        num_frames (int, optional): Number of frames.
+            Defaults to MAX_FRAMES_MODEL
+        guidance_scale (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
+        - Output dimensions are adjusted to be multiples of MOD_VALUE (32)
+        - The function uses GPU acceleration via the @spaces.GPU decorator
+    """
     if input_image is None:
         raise gr.Error("Please upload an input image.")
     
@@ -130,8 +166,8 @@ def generate_video(
     return video_path, current_seed
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Fast 4 steps Wan 2.2 Wan-AI/Wan2.2-I2V-A14B-Diffusers")
-    #gr.Markdown("[CausVid](https://github.com/tianweiy/CausVid) is a distilled version of Wan 2.1 to run faster in just 4-8 steps, [extracted as LoRA by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors) and is compatible with 🧨 diffusers")
+    gr.Markdown("# Fast 4 steps Wan 2.1 I2V (14B) with CausVid LoRA")
+    gr.Markdown("[CausVid](https://github.com/tianweiy/CausVid) is a distilled version of Wan 2.1 to run faster in just 4-8 steps, [extracted as LoRA by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors) and is compatible with 🧨 diffusers")
     with gr.Row():
         with gr.Column():
             input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)")