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 = "๐พ 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=0)
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"], ["๐๐ปโโ๏ธ SVD (trained on 14 f/s)", "svd"], ["๐๐ปโโ๏ธ๐จ 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="๐ Animate", variant="primary")
reset_btn = gr.Button(value="๐งน 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="๐พ 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)