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import gradio as gr
#import gradio.helpers
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
#gradio.helpers.CACHED_FOLDER = '/data/cache'
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
max_64_bit_int = 2**63 - 1
def sample(
image: Image,
seed: Optional[int] = 42,
randomize_seed: bool = True,
motion_bucket_id: int = 127,
fps_id: int = 6,
version: str = "svd_xt",
cond_aug: float = 0.02,
decoding_t: int = 3, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
device: str = "cuda",
output_folder: str = "outputs",
if image.mode == "RGBA":
image = image.convert("RGB")
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 = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1).frames[0]
export_to_video(frames, video_path, fps=fps_id)
return video_path, seed
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
# Resize then crop 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]
# 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.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](, [paper](, [stability's ui waitlist](
#### Research release ([_non-commercial_]( generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](
with gr.Row():
with gr.Column():
image = gr.Image(label="Upload your image", type="pil")
generate_btn = gr.Button("Generate")
video = gr.Video()
with gr.Accordion("Advanced options", open=False):
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)
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)
image.upload(fn=resize_image, inputs=image, outputs=image, queue=False), inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
outputs=[video, seed],
if __name__ == "__main__":