Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| 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 | |
| MAX_64_BIT_INT = 2**63 - 1 | |
| DEFAULT_SEED = 42 | |
| DEFAULT_OUTPUT_FOLDER = "outputs" | |
| pipe = StableVideoDiffusionPipeline.from_pretrained("vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16") | |
| pipe.to("cpu") | |
| 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 | |
| def generate_video(image, seed, motion_bucket_id, fps_id): | |
| if image.mode == "RGBA": | |
| image = image.convert("RGB") | |
| generator = torch.manual_seed(seed) | |
| frames = pipe(image, decode_chunk_size=3, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0] | |
| return frames | |
| def export_video(frames, video_path, fps_id): | |
| export_to_video(frames, video_path, fps=fps_id) | |
| def sample( | |
| image, | |
| seed=DEFAULT_SEED, | |
| randomize_seed=True, | |
| motion_bucket_id=127, | |
| fps_id=6, | |
| version="svd_xt", | |
| cond_aug=0.02, | |
| decoding_t=3, | |
| device="cuda", | |
| output_folder=DEFAULT_OUTPUT_FOLDER, | |
| ): | |
| 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 = generate_video(image, seed, motion_bucket_id, fps_id) | |
| export_video(frames, video_path, fps_id) | |
| torch.manual_seed(seed) | |
| return video_path, frames, seed | |
| with gr.Blocks() as demo: | |
| 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=DEFAULT_SEED, 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") | |
| if __name__ == "__main__": | |
| demo.launch(share=True, show_api=False) |