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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 | |