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import gradio as gr | |
import torch | |
import numpy as np | |
import modin.pandas as pd | |
from PIL import Image | |
from diffusers import DiffusionPipeline | |
from huggingface_hub import login | |
import os | |
from glob import glob | |
from pathlib import Path | |
from typing import Optional | |
import uuid | |
import random | |
token = os.environ['HF_TOKEN'] | |
login(token=token) | |
device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
torch.cuda.max_memory_allocated(device=device) | |
torch.cuda.empty_cache() | |
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt-1-1") | |
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
#pipe.enable_xformers_memory_efficient_attention() | |
#pipe = pipe.to(device) | |
torch.cuda.empty_cache() | |
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") | |
if(randomize_seed): | |
seed = random.randint(0, max_64_bit_int) | |
generator = torch.manual_seed(seed) | |
torch.cuda.empty_cache() | |
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, num_frames=25).frames[0] | |
export_to_video(frames, video_path, fps=fps_id) | |
torch.manual_seed(seed) | |
torch.cuda.empty_cache() | |
return video_path, seed | |
def resize_image(image, output_size=(1024, 578)): | |
# 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] | |
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)) | |
torch.cuda.empty_cache() | |
return cropped_image | |
with gr.Blocks() as demo: | |
#gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), [stability's ui waitlist](https://stability.ai/contact)) | |
#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd). | |
#''') | |
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) | |
generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video",)# inputs=image, outputs=[video, seed], fn=sample, cache_examples=True,) | |
if __name__ == "__main__": | |
demo.queue(max_size=20, api_open=False) | |
demo.launch(show_api=False) |