Hugging Face's logo

Spaces:
AmpleBasis
/
SUPIR
Paused

App Files Community
SUPIR
File size: 5,280 Bytes 
632c988
0c09666
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
632c988
0c09666
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16bdd2b
0c09666
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
 
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()