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), gr.update(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
def reset():
return [
None,
random.randint(0, max_64_bit_int),
True,
127,
6,
0.1,
3,
"mp4",
"webp",
"auto"
]
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"]], 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)
version = gr.Radio([["Auto", "auto"], ["๐๐ปโโ๏ธ SVD (trained on 14 f/s)", "svd"], ["๐๐ปโโ๏ธ๐จ 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="๐ Animate", variant="primary")
reset_btn = gr.Button(value="๐งน Reinit page", variant="stop", elem_id="reset_button", visible = False)
with gr.Column():
video = gr.Video(label="Generated video", autoplay=True)
download_button = gr.DownloadButton(label="๐พ 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,
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
], queue = False, show_progress = False)
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, reset_btn],
fn=animate,
run_on_click=True,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch(share=True, show_api=False)