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)