Hugging Face's logo

Spaces:
fffiloni
/
SVFR-demo
Running on L40S

App Files Community
1
SVFR-demo
File size: 4,271 Bytes 
38f6355
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
28328f5
 
 
38f6355
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7379673
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38f6355
 
 
 
 
 
 
 
 
 
 
 
 
 
51f880b
38f6355
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import torch
import sys
import os
import subprocess
import shutil
import tempfile
import uuid
import gradio as gr
from glob import glob
from huggingface_hub import snapshot_download

# Download models
os.makedirs("models", exist_ok=True)

snapshot_download(
    repo_id = "fffiloni/SVFR",
    local_dir = "./models"  
)

# List of subdirectories to create inside "checkpoints"
subfolders = [
    "stable-video-diffusion-img2vid-xt"
]
# Create each subdirectory
for subfolder in subfolders:
    os.makedirs(os.path.join("models", subfolder), exist_ok=True)

snapshot_download(
    repo_id = "stabilityai/stable-video-diffusion-img2vid-xt",
    local_dir = "./models/stable-video-diffusion-img2vid-xt"  
)

def infer(lq_sequence, task_name):
    
    unique_id = str(uuid.uuid4())
    output_dir = f"results_{unique_id}"

    if task_name == "BFR":
        task_id = "0"
    elif task_name == "colorization":
        task_id = "1"
    elif task_name == "BFR + colorization":
        task_id = "0,1"
    
    try:
        # Run the inference command
        subprocess.run(
            [
                "python", "infer.py",
                "--config", "config/infer.yaml",
                "--task_ids", f"{task_id}",
                "--input_path", f"{lq_sequence}",
                "--output_dir", f"{output_dir}",
            ],
            check=True
        )

        # Search for the mp4 file in a subfolder of output_dir
        output_video = glob(os.path.join(output_dir,"*.mp4"))
        print(output_video)
        
        if output_video:
            output_video_path = output_video[0]  # Get the first match
        else:
            output_video_path = None
        
        print(output_video_path)
        return output_video_path
    
    except subprocess.CalledProcessError as e:
        raise gr.Error(f"Error during inference: {str(e)}")

css="""
div#col-container{
    margin: 0 auto;
    max-width: 982px;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# SVFR: A Unified Framework for Generalized Video Face Restoration")
        gr.Markdown("SVFR is a unified framework for face video restoration that supports tasks such as BFR, Colorization, Inpainting, and their combinations within one cohesive system.")
        gr.HTML("""
        <div style="display:flex;column-gap:4px;">
            <a href="https://github.com/wangzhiyaoo/SVFR">
                <img src='https://img.shields.io/badge/GitHub-Repo-blue'>
            </a> 
            <a href="https://wangzhiyaoo.github.io/SVFR/">
                <img src='https://img.shields.io/badge/Project-Page-green'>
            </a>
            <a href="https://arxiv.org/pdf/2501.01235">
                <img src='https://img.shields.io/badge/ArXiv-Paper-red'>
            </a>
            <a href="https://huggingface.co/spaces/fffiloni/SVFR-demo?duplicate=true">
                <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space">
            </a>
            <a href="https://huggingface.co/fffiloni">
                <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg" alt="Follow me on HF">
            </a>
        </div>
        """)
        with gr.Row():
            with gr.Column():
                input_seq = gr.Video(label="Video LQ")
                task_name = gr.Radio(
                    label="Task", 
                    choices=["BFR", "colorization", "BFR + colorization"], 
                    value="BFR"
                )
                submit_btn = gr.Button("Submit")
            with gr.Column():
                output_res = gr.Video(label="Restored")
                gr.Examples(
                    examples = [
                        ["./assert/lq/lq1.mp4", "BFR"],
                        ["./assert/lq/lq2.mp4", "BFR + colorization"],
                        ["./assert/lq/lq3.mp4", "colorization"]
                    ],
                    inputs = [input_seq, task_name]
                )
    
    submit_btn.click(
        fn = infer,
        inputs = [input_seq, task_name],
        outputs = [output_res]
    )

demo.queue().launch(show_api=False, show_error=True)