Delete app.py

app.py

@@ -1,162 +0,0 @@
-import spaces
-import gradio as gr
-import numpy as np
-from PIL import Image
-import cv2
-from moviepy.editor import VideoFileClip
-from share_btn import community_icon_html, loading_icon_html, share_js
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-
-
-
-def convert_mp4_to_frames(video_path, duration=3):
-    # 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
-    num_frames = int(fps * duration)
-
-    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
-
-@spaces.GPU(duration=120)
-def infer(prompt, video_in, denoise_strength):
-
-    negative_prompt = "text, watermark, copyright, blurry, nsfw"
-
-    video = convert_mp4_to_frames(video_in, duration=3)
-    video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video]
-
-    pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float32, 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("cpu")
-    video_frames = pipe_xl(prompt, negative_prompt=negative_prompt, video=video_resized, strength=denoise_strength).frames
-    del pipe_xl
-    #torch.cuda.empty_cache()
-    video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")
-    
-    return "xl_result.mp4", gr.Group.update(visible=True)
-
-css = """
-#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.animate-spin {
-  animation: spin 1s linear infinite;
-}
-@keyframes spin {
-  from {
-      transform: rotate(0deg);
-  }
-  to {
-      transform: rotate(360deg);
-  }
-}
-#share-btn-container {
-  display: flex; 
-  padding-left: 0.5rem !important; 
-  padding-right: 0.5rem !important; 
-  background-color: #000000; 
-  justify-content: center; 
-  align-items: center; 
-  border-radius: 9999px !important; 
-  max-width: 13rem;
-}
-#share-btn-container:hover {
-  background-color: #060606;
-}
-#share-btn {
-  all: initial; 
-  color: #ffffff;
-  font-weight: 600; 
-  cursor:pointer; 
-  font-family: 'IBM Plex Sans', sans-serif; 
-  margin-left: 0.5rem !important; 
-  padding-top: 0.5rem !important; 
-  padding-bottom: 0.5rem !important;
-  right:0;
-}
-#share-btn * {
-  all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-  width: auto !important;
-  min-height: 0px !important;
-}
-#share-btn-container .wrap {
-  display: none !important;
-}
-#share-btn-container.hidden {
-  display: none!important;
-}
-img[src*='#center'] { 
-    display: block;
-    margin: auto;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(
-            """
-            <h1 style="text-align: center;">Zeroscope XL</h1>
-            <p style="text-align: center;">
-            This space is specifically designed for upscaling content made from <br />
-            <a href="https://huggingface.co/spaces/fffiloni/zeroscope">the zeroscope_v2_576w space</a> using vid2vid. <br />
-            Remember to use the same prompt that was used to generate the original clip.<br />
-            For demo purpose, video length is limited to 3 seconds.
-            </p>
-            
-            [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-XL?duplicate=true)
-            
-            """
-        )
-
-        video_in = gr.Video(sources=["upload"])
-        prompt_in = gr.Textbox(label="Prompt", placeholder="This must be the same prompt you used for the original clip :)", 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=10, maximum=100, step=1, value=40, interactive=False)
-        submit_btn = gr.Button("Submit")
-        video_result = gr.Video(label="Video Output", elem_id="video-output")
-
-        with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
-            community_icon = gr.HTML(community_icon_html)
-            loading_icon = gr.HTML(loading_icon_html)
-            share_button = gr.Button("Share to community", elem_id="share-btn")
-
-    submit_btn.click(fn=infer,
-                    inputs=[prompt_in, video_in, denoise_strength],
-                    outputs=[video_result, share_group])
-
-demo.queue(max_size=12).launch()