import gradio as gr import torch import os import random import time import math import spaces from glob import glob from pathlib import Path from typing import Optional from diffusers import StableVideoDiffusionPipeline from diffusers.utils import export_to_video from PIL import Image fps25Pipe = StableVideoDiffusionPipeline.from_pretrained( "vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16" ) fps25Pipe.to("cuda") fps14Pipe = StableVideoDiffusionPipeline.from_pretrained( "stabilityai/stable-video-diffusion-img2vid", torch_dtype=torch.float16, variant="fp16" ) fps14Pipe.to("cuda") max_64_bit_int = 2**63 - 1 def animate( image: Image, seed: Optional[int] = 42, randomize_seed: bool = True, motion_bucket_id: int = 127, fps_id: int = 6, noise_aug_strength: float = 0.1, decoding_t: int = 3, video_format: str = "mp4", frame_format: str = "webp", version: str = "auto", output_folder: str = "outputs", ): start = time.time() if image.mode == "RGBA": image = image.convert("RGB") if randomize_seed: seed = random.randint(0, max_64_bit_int) if version == "auto": if 14 < fps_id: version = "svdxt" else: version = "svd" frames = animate_on_gpu( image, seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, version ) os.makedirs(output_folder, exist_ok=True) base_count = len(glob(os.path.join(output_folder, "*." + video_format))) video_path = os.path.join(output_folder, f"{base_count:06d}." + video_format) export_to_video(frames, video_path, fps=fps_id) end = time.time() secondes = int(end - start) minutes = math.floor(secondes / 60) secondes = secondes - (minutes * 60) hours = math.floor(minutes / 60) minutes = minutes - (hours * 60) information = ("Start the process again if you want a different result. " if randomize_seed else "") + \ "Wait 2 min before a new run to avoid quota penalty or use another computer. " + \ "The video has been generated in " + \ ((str(hours) + " h, ") if hours != 0 else "") + \ ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \ str(secondes) + " sec." return gr.update(value=video_path, format=video_format), gr.update(value=video_path, visible=True), gr.update(label="Generated frames in *." + frame_format + " format", format = frame_format, value = frames, visible=True), seed, gr.update(value = information, visible = True) @spaces.GPU(duration=120) def animate_on_gpu( image: Image, seed: Optional[int] = 42, motion_bucket_id: int = 127, fps_id: int = 6, noise_aug_strength: float = 0.1, decoding_t: int = 3, version: str = "svdxt" ): generator = torch.manual_seed(seed) if version == "svdxt": return fps25Pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25).frames[0] else: return fps14Pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25).frames[0] 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 # Do not touch the image if the size is good if image.width == output_size[0] and image.height == output_size[1]: return image # Resize 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.HTML("""

Image-to-Video

Animate your images into 25 frames of 1024x576 pixels freely, without account, without watermark and download the video

This demo is based on Stable Video Diffusion artificial intelligence. No prompt or camera control is handled here. To control motions, rather use MotionCtrl SVD.

""") with gr.Row(): with gr.Column(): image = gr.Image(label="Upload your image", type="pil") with gr.Accordion("Advanced options", open=False): 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) 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) noise_aug_strength = gr.Slider(label="Noise strength", info="The noise to add", value=0.1, minimum=0, maximum=1, step=0.1) decoding_t = gr.Slider(label="Decoding", info="Number of frames decoded at a time; this eats more VRAM; reduce if necessary", value=3, minimum=1, maximum=5, step=1) video_format = gr.Radio([["*.mp4", "mp4"], ["*.ogg", "ogg"], ["*.webm", "webm"]], label="Video format for result", info="File extention", value="mp4", interactive=True) frame_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif (unanimated)", "gif"], ["*.bmp", "bmp"]], label="Image format for frames", info="File extention", value="webp", interactive=True) version = gr.Radio([["Auto", "auto"], ["🏃🏻‍♀️ SVD (trained on 14 f/s)", "svd"], ["🏃🏻‍♀️💨 SVD-XT (trained on 25 f/s)", "svdxt"]], label="Model", info="Trained model", value="auto", interactive=True) 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) generate_btn = gr.Button(value="🚀 Animate", variant="primary") with gr.Column(): video = gr.Video(label="Generated video", autoplay=True) download_button = gr.DownloadButton(label="💾 Download video", visible=False) information_msg = gr.HTML(visible = False) gallery = gr.Gallery(label="Generated frames", visible=False) image.upload(fn=resize_image, inputs=image, outputs=image, queue=False) generate_btn.click(fn=animate, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version], outputs=[video, download_button, gallery, seed, information_msg], api_name="video") gr.Examples( examples=[ ["Examples/Fire.webp", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"], ["Examples/Water.png", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"], ["Examples/Town.jpeg", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"] ], inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version], outputs=[video, download_button, gallery, seed, information_msg], fn=animate, run_on_click=True, cache_examples=False, ) if __name__ == "__main__": demo.launch(share=True, show_api=False)