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| import gradio as gr | |
| import numpy as np | |
| import os | |
| from PIL import Image | |
| import cv2 | |
| from moviepy.editor import VideoFileClip | |
| import torch | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers.utils import export_to_video | |
| SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret') | |
| DESCRIPTION = 'This space is an API service meant to be used by VideoChain and VideoQuest.\nWant to use this space for yourself? Please use the original code: [https://huggingface.co/spaces/fffiloni/zeroscope-XL](https://huggingface.co/spaces/fffiloni/zeroscope-XL)' | |
| pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/17") | |
| pipe_xl.vae.enable_slicing() | |
| pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config) | |
| pipe_xl.enable_model_cpu_offload() | |
| pipe_xl.to("cuda") | |
| def convert_mp4_to_frames(video_path): | |
| # Read the video file | |
| video = cv2.VideoCapture(video_path) | |
| # Get the frames per second (fps) of the video | |
| fps = video.get(cv2.CAP_PROP_FPS) | |
| # Calculate the number of frames to extract | |
| # Note: we cannot go beyond 3 seconds on the large A10G | |
| # num_frames = int(fps * min(duration, 3)) | |
| # let's use a fixed max for now, no need to have a duration field | |
| num_frames = int(fps * 3) | |
| frames = [] | |
| frame_count = 0 | |
| # Iterate through each frame | |
| while True: | |
| # Read a frame | |
| ret, frame = video.read() | |
| # If the frame was not successfully read or we have reached the desired duration, break the loop | |
| if not ret or frame_count == num_frames: | |
| break | |
| # Convert BGR to RGB | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| # Append the frame to the list of frames | |
| frames.append(frame) | |
| frame_count += 1 | |
| # Release the video object | |
| video.release() | |
| # Convert the list of frames to a numpy array | |
| frames = np.array(frames) | |
| return frames | |
| def infer(prompt, video_in, denoise_strength, secret_token): | |
| if secret_token != SECRET_TOKEN: | |
| raise gr.Error( | |
| f'Invalid secret token. Please fork the original space if you want to use it for yourself.') | |
| negative_prompt = "text, watermark, copyright, blurry, cropped, noisy, pixelated, nsfw" | |
| video = convert_mp4_to_frames(video_in) | |
| video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video] | |
| video_frames = pipe_xl(prompt, negative_prompt=negative_prompt, video=video_resized, strength=denoise_strength).frames | |
| video_path = export_to_video(video_frames, output_video_path="xl_result.mp4") | |
| return "xl_result.mp4" #, gr.Group.update(visible=True) | |
| with gr.Blocks() as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Column(): | |
| secret_token = gr.Text(label='Secret Token', max_lines=1) | |
| video_in = gr.Video(type="numpy", source="upload") | |
| prompt_in = gr.Textbox(label="Prompt", elem_id="prompt-in") | |
| denoise_strength = gr.Slider(label="Denoise strength", minimum=0.6, maximum=0.9, step=0.01, value=0.66) | |
| #inference_steps = gr.Slider(label="Inference Steps", minimum=7, maximum=100, step=1, value=40, interactive=False) | |
| submit_btn = gr.Button("Submit") | |
| video_result = gr.Video(label="Video Output", elem_id="video-output") | |
| submit_btn.click(fn=infer, | |
| inputs=[prompt_in, video_in, denoise_strength, secret_token], | |
| outputs=[video_result], | |
| api_name="zero_xl" | |
| ) | |
| demo.queue(max_size=6).launch() | |