Update app.py

app.py

@@ -1,107 +1,70 @@
 import gradio as gr
-#import gradio.helpers
 import torch
 import os
 from glob import glob
 from pathlib import Path
 from typing import Optional
-
 from diffusers import StableVideoDiffusionPipeline
 from diffusers.utils import load_image, export_to_video
 from PIL import Image
-
 import uuid
 import random
 from huggingface_hub import hf_hub_download
-import spaces
-
-pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
-)
-pipe.to("cuda")
 
 max_64_bit_int = 2**63 - 1
-
+pipe = StableVideoDiffusionPipeline.from_pretrained("vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16")
+pipe.to("cuda")
 @spaces.GPU(duration=120)
-def sample(
-    image: Image,
-    seed: Optional[int] = 42,
-    randomize_seed: bool = True,
-    motion_bucket_id: int = 127,
-    fps_id: int = 6,
-    version: str = "svd_xt",
-    cond_aug: float = 0.02,
-    decoding_t: int = 3,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
-    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)
+
+def sample(image, seed=42, randomize_seed=True, motion_bucket_id=127, fps_id=6, version="svd_xt", cond_aug=0.02, decoding_t=3, device="cuda", output_folder="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)
-    
     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_to_video(frames, video_path, fps=fps_id)
     torch.manual_seed(seed)
-    
     return video_path, frames, 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
+    target_aspect = output_size[0] / output_size[1]
+    image_aspect = image.width / image.height
     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('''# Community demo for Stable Video Diffusion - Img2Vid - 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))
-#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
-  ''')
-  with gr.Row():
-      with gr.Column():
-          image = gr.Image(label="Upload your image", type="pil")
-          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)
-              fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
-          generate_btn = gr.Button(value="Animate", variant="primary")
-      with gr.Column():
-          video = gr.Video(label="Generated video")
-          gallery = gr.Gallery(label="Generated frames")
-      
-  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
-  generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, gallery, seed], api_name="video")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="pil")
+            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", value=127, minimum=1, maximum=255)
+                fps_id = gr.Slider(label="Frames per second", value=6, minimum=5, maximum=30)
+            generate_btn = gr.Button(value="Animate", variant="primary")
+        with gr.Column():
+            video = gr.Video(label="Generated video")
+            gallery = gr.Gallery(label="Generated frames")
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, gallery, seed], api_name="video")
 
 if __name__ == "__main__":
     demo.launch(share=True, show_api=False)