Create app.py

app.py

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+import gradio as gr
+import torch
+import os
+import uuid
+import random
+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
+from huggingface_hub import hf_hub_download
+
+
+pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+)
+pipe.to("cuda")
+pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+max_64_bit_int = 2**63 - 1
+
+# Function to sample video from the input image
+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)
+        
+    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, seed
+
+# Function to resize the uploaded image
+def resize_image(image, output_size=(1024, 576)):
+    target_aspect = output_size[0] / output_size[1]
+    image_aspect = image.width / image.height
+
+    if image_aspect > target_aspect:
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return cropped_image
+
+# Dynamically load image files from the 'images' directory
+def get_example_images():
+    image_dir = "images/"
+    image_files = glob(os.path.join(image_dir, "*.png")) + glob(os.path.join(image_dir, "*.jpg"))
+    return image_files
+
+# Gradio interface setup
+with gr.Blocks() as demo:
+    gr.Markdown('''# Stable Video Diffusion using Image 2 Video XT 
+        #### Research release: generate `4s` vid from a single image at (`25 frames` at `6 fps`).''')
+
+    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", value=127, minimum=1, maximum=255)
+        fps_id = gr.Slider(label="Frames per second", value=6, minimum=5, maximum=30)
+
+    image.upload(fn