Update app.py

app.py

@@ -3,7 +3,6 @@ import torch
 import os
 import uuid
 import random
-
 from glob import glob
 from pathlib import Path
 from typing import Optional
@@ -12,6 +11,7 @@ from diffusers.utils import load_image, export_to_video
 from PIL import Image
 from huggingface_hub import hf_hub_download
 
+
 pipe = StableVideoDiffusionPipeline.from_pretrained(
     "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
 )
@@ -19,6 +19,7 @@ pipe.to("cuda")
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 max_64_bit_int = 2**63 - 1
 
+
 def sample(
     image: Image,
     seed: Optional[int] = 42,
@@ -31,60 +32,48 @@ def sample(
     device: str = "cuda",
     output_folder: str = "outputs",
 ):
+    
     if image.mode == "RGBA":
         image = image.convert("RGB")
     if(randomize_seed):
         seed = random.randint(0, max_64_bit_int)
+        
     generator = torch.manual_seed(seed)
 
-    # Count completed mp4 videos and set the path
     os.makedirs(output_folder, exist_ok=True)
     base_count = len(glob(os.path.join(output_folder, "*.mp4")))
     video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
-
     frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
-
-    # Export frames to video
     export_to_video(frames, video_path, fps=fps_id)
     torch.manual_seed(seed)
-
-    # Return the video and seed
     return video_path, seed
 
 def resize_image(image, output_size=(1024, 576)):
-    # Calculate aspect ratios
     target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
     image_aspect = image.width / image.height  # Aspect ratio of the original image
 
-    # Resize then crop if the original image is larger
     if image_aspect > target_aspect:
-        # Resize the image to match the target height, maintaining aspect ratio
         new_height = output_size[1]
         new_width = int(new_height * image_aspect)
         resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        
-        # Calculate coordinates for cropping
         left = (new_width - output_size[0]) / 2
         top = 0
         right = (new_width + output_size[0]) / 2
         bottom = output_size[1]
     else:
-        # Resize the image to match the target width, maintaining aspect ratio
         new_width = output_size[0]
         new_height = int(new_width / image_aspect)
         resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        
-        # Calculate coordinates for cropping
         left = 0
         top = (new_height - output_size[1]) / 2
         right = output_size[0]
         bottom = (new_height + output_size[1]) / 2
 
-    # Crop the image
     cropped_image = resized_image.crop((left, top, right, bottom))
     return cropped_image
 
 with gr.Blocks() as demo:
+    
   gr.Markdown('''# Stable Video Diffusion using Image 2 Video XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), 
       [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), 
       [stability's ui waitlist](https://stability.ai/contact))
@@ -92,12 +81,15 @@ with gr.Blocks() as demo:
   ''')
     
   with gr.Row():
+      
     with gr.Column():
         image = gr.Image(label="Upload your image", type="pil")
         generate_btn = gr.Button("Generate")
+        
     video = gr.Video()
       
   with gr.Accordion("Advanced options", open=False):
+      
       seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1)
       randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
       motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)