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, List, Union from diffusers import StableVideoDiffusionPipeline, StableVideoDragNUWAPipeline from diffusers.utils import export_to_video, export_to_gif 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") dragnuwaPipe = StableVideoDragNUWAPipeline.from_pretrained( "a-r-r-o-w/dragnuwa-svd", torch_dtype=torch.float16, variant="fp16", low_cpu_mem_usage=False, device_map=None ) dragnuwaPipe.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 = 25, noise_aug_strength: float = 0.1, decoding_t: int = 3, video_format: str = "mp4", frame_format: str = "webp", version: str = "auto", width: int = 1024, height: int = 576, motion_control: bool = False, num_inference_steps: int = 25 ): start = time.time() if image is None: raise gr.Error("Please provide an image to animate.") output_folder = "outputs" image_data = resize_image(image, output_size=(width, height)) if image_data.mode == "RGBA": image_data = image_data.convert("RGB") if motion_control: image_data = [image_data] * 3 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_data, seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, version, width, height, num_inference_steps ) os.makedirs(output_folder, exist_ok=True) base_count = len(glob(os.path.join(output_folder, "*." + video_format))) result_path = os.path.join(output_folder, f"{base_count:06d}." + video_format) if video_format == "gif": video_path = None gif_path = result_path export_to_gif(image=frames, output_gif_path=gif_path, fps=fps_id) else: video_path = result_path gif_path = None 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 [ # Display for video gr.update(value = video_path, visible = video_format != "gif"), # Display for gif gr.update(value = gif_path, visible = video_format == "gif"), # Download button gr.update(label = "๐Ÿ’พ Download animation in *." + video_format + " format", value=result_path, visible=True), # Frames gr.update(label = "Generated frames in *." + frame_format + " format", format = frame_format, value = frames, visible = True), # Used seed seed, # Information gr.update(value = information, visible = True), # Reset button gr.update(visible = True) ] @torch.no_grad() @spaces.GPU(duration=180) def animate_on_gpu( image_data: Union[Image.Image, List[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", width: int = 1024, height: int = 576, num_inference_steps: int = 25 ): generator = torch.manual_seed(seed) if version == "dragnuwa": return dragnuwaPipe(image_data, width=width, height=height, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25, num_inference_steps=num_inference_steps).frames[0] elif version == "svdxt": return fps25Pipe(image_data, width=width, height=height, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25, num_inference_steps=num_inference_steps).frames[0] else: return fps14Pipe(image_data, width=width, height=height, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25, num_inference_steps=num_inference_steps).frames[0] def resize_image(image, output_size=(1024, 576)): # Do not touch the image if the size is good if image.width == output_size[0] and image.height == output_size[1]: return image # 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 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 return resized_image.crop((left, top, right, bottom)) def reset(): return [ None, random.randint(0, max_64_bit_int), True, 127, 6, 0.1, 3, "mp4", "webp", "auto", 1024, 576, False, 25 ] with gr.Blocks() as demo: gr.HTML("""

Image-to-Video

Animate your image 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. If you need 128 frames, rather use ExVideo.

""") with gr.Row(): with gr.Column(): image = gr.Image(label="Upload your image", type="pil") with gr.Accordion("Advanced options", open=False): width = gr.Slider(label="Width", info="Width of the video", value=1024, minimum=256, maximum=1024, step=8) height = gr.Slider(label="Height", info="Height of the video", value=576, minimum=256, maximum=576, step=8) motion_control = gr.Checkbox(label="Motion control (experimental)", info="Fix the camera", value=False) video_format = gr.Radio([["*.mp4", "mp4"], ["*.avi", "avi"], ["*.wmv", "wmv"], ["*.mkv", "mkv"], ["*.mov", "mov"], ["*.gif", "gif"]], label="Video format for result", info="File extention", value="mp4", interactive=True) frame_format = gr.Radio([["*.webp", "webp"], ["*.png", "png"], ["*.jpeg", "jpeg"], ["*.gif (unanimated)", "gif"], ["*.bmp", "bmp"]], label="Image format for frames", info="File extention", value="webp", interactive=True) fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=25, 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) num_inference_steps = gr.Slider(label="Number inference steps", info="More denoising steps usually lead to a higher quality video at the expense of slower inference", value=25, minimum=1, maximum=100, step=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) version = gr.Radio([["Auto", "auto"], ["๐Ÿƒ๐Ÿปโ€โ™€๏ธ SVD (trained on 14 f/s)", "svd"], ["๐Ÿƒ๐Ÿปโ€โ™€๏ธ๐Ÿ’จ SVD-XT (trained on 25 f/s)", "svdxt"], ["DragNUWA (unstable)", "dragnuwa"]], 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="๐Ÿš€ Animate", variant="primary") reset_btn = gr.Button(value="๐Ÿงน Reinit page", variant="stop", elem_id="reset_button", visible = False) with gr.Column(): video_output = gr.Video(label="Generated video", format="mp4", autoplay=True, show_download_button=False) gif_output = gr.Image(label="Generated video", format="gif", show_download_button=False, visible=False) download_button = gr.DownloadButton(label="๐Ÿ’พ Download video", visible=False) information_msg = gr.HTML(visible=False) gallery = gr.Gallery(label="Generated frames", visible=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, width, height, motion_control, num_inference_steps ], outputs=[ video_output, gif_output, download_button, gallery, seed, information_msg, reset_btn ], api_name="video") reset_btn.click(fn = reset, inputs = [], outputs = [ image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version, width, height, motion_control, num_inference_steps ], queue = False, show_progress = False) gr.Examples( examples=[ ["Examples/Fire.webp", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto", 1024, 576, False, 25], ["Examples/Water.png", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto", 1024, 576, False, 25], ["Examples/Town.jpeg", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto", 1024, 576, False, 25] ], inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version, width, height, motion_control, num_inference_steps], outputs=[video_output, gif_output, download_button, gallery, seed, information_msg, reset_btn], fn=animate, run_on_click=True, cache_examples=False, ) if __name__ == "__main__": demo.launch(share=True, show_api=False)