File size: 6,747 Bytes
b930f26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e6d3313
28289d7
b930f26
 
8d8d8eb
 
b930f26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8958ee7
05d90a1
a9629e7
 
 
 
 
b930f26
 
 
 
 
 
 
 
 
 
 
 
 
445d108
b930f26
 
 
 
99d7d37
b930f26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9c0c943
ce6959b
 
 
 
 
2c5b89b
ce6959b
b930f26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0f6479b
b930f26
 
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
163
164
165
166
167
168
169
170
171
172
173
import gradio as gr
import os
import sys
from pathlib import Path
import random
import string
import time
from queue import Queue
from threading import Thread
import emoji


text_gen=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion")
def get_prompts(prompt_text):
    if prompt_text:
        return text_gen("photo, " + prompt_text)
    else:
        return text_gen("")
proc1=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0")

def restart_script_periodically():
    while True:
        random_time = random.randint(540, 600)
        time.sleep(random_time)
        os.execl(sys.executable, sys.executable, *sys.argv)


restart_thread = Thread(target=restart_script_periodically, daemon=True)
restart_thread.start()


queue = Queue()
queue_threshold = 100

def add_random_noise(prompt, noise_level=1.00):
    print(queue.qsize())
    if noise_level == 0:
        noise_level = 0.00
    if noise_level == None:
        noise_level = 1.00
    percentage_noise = noise_level * 5
    num_noise_chars = int(len(prompt) * (percentage_noise/100))
    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
    prompt_list = list(prompt)
    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
    noise_chars.extend(['๐Ÿ˜', '๐Ÿ’ฉ', '๐Ÿ˜‚', '๐Ÿค”', '๐Ÿ˜Š', '๐Ÿค—', '๐Ÿ˜ญ', '๐Ÿ™„', '๐Ÿ˜ท', '๐Ÿคฏ', '๐Ÿคซ', '๐Ÿฅด', '๐Ÿ˜ด', '๐Ÿคฉ', '๐Ÿฅณ', '๐Ÿ˜”', '๐Ÿ˜ฉ', '๐Ÿคช', '๐Ÿ˜‡', '๐Ÿคข', '๐Ÿ˜ˆ', '๐Ÿ‘น', '๐Ÿ‘ป', '๐Ÿค–', '๐Ÿ‘ฝ', '๐Ÿ’€', '๐ŸŽƒ', '๐ŸŽ…', '๐ŸŽ„', '๐ŸŽ', '๐ŸŽ‚', '๐ŸŽ‰', '๐ŸŽˆ', '๐ŸŽŠ', '๐ŸŽฎ', 'โค๏ธ', '๐Ÿ’”', '๐Ÿ’•', '๐Ÿ’–', '๐Ÿ’—', '๐Ÿถ', '๐Ÿฑ', '๐Ÿญ', '๐Ÿน', '๐ŸฆŠ', '๐Ÿป', '๐Ÿจ', '๐Ÿฏ', '๐Ÿฆ', '๐Ÿ˜', '๐Ÿ”ฅ', '๐ŸŒง๏ธ', '๐ŸŒž', '๐ŸŒˆ', '๐Ÿ’ฅ', '๐ŸŒด', '๐ŸŒŠ', '๐ŸŒบ', '๐ŸŒป', '๐ŸŒธ', '๐ŸŽจ', '๐ŸŒ…', '๐ŸŒŒ', 'โ˜๏ธ', 'โ›ˆ๏ธ', 'โ„๏ธ', 'โ˜€๏ธ', '๐ŸŒค๏ธ', 'โ›…๏ธ', '๐ŸŒฅ๏ธ', '๐ŸŒฆ๏ธ', '๐ŸŒง๏ธ', '๐ŸŒฉ๏ธ', '๐ŸŒจ๏ธ', '๐ŸŒซ๏ธ', 'โ˜”๏ธ', '๐ŸŒฌ๏ธ', '๐Ÿ’จ', '๐ŸŒช๏ธ', '๐ŸŒˆ'])
    for index in noise_indices:
        prompt_list[index] = random.choice(noise_chars)
    return "".join(prompt_list)


def send_it1(inputs, noise_level, proc1=proc1):
    prompt_with_noise = add_random_noise(inputs, noise_level)
    while queue.qsize() >= queue_threshold:
        time.sleep(2)
    queue.put(prompt_with_noise)
    output1 = proc1(prompt_with_noise)
    return output1

def send_it2(inputs, noise_level, proc1=proc1):
    prompt_with_noise = add_random_noise(inputs, noise_level)
    while queue.qsize() >= queue_threshold:
        time.sleep(2)
    queue.put(prompt_with_noise)
    output2 = proc1(prompt_with_noise)
    return output2

#def send_it3(inputs, noise_level, proc1=proc1):
    #prompt_with_noise = add_random_noise(inputs, noise_level)
    #while queue.qsize() >= queue_threshold:
        #time.sleep(2)
    #queue.put(prompt_with_noise)
    #output3 = proc1(prompt_with_noise)
    #return output3

#def send_it4(inputs, noise_level, proc1=proc1):
    #prompt_with_noise = add_random_noise(inputs, noise_level)
    #while queue.qsize() >= queue_threshold:
        #time.sleep(2)
    #queue.put(prompt_with_noise)
    #output4 = proc1(prompt_with_noise)
    #return output4

def send_it_all(inputs, proc1=proc1):
    print(queue.qsize())
    noise_level = 1.00
    output1 = send_it1(inputs, noise_level, proc1)
    output2 = send_it2(inputs, noise_level, proc1)
    return output1, output2


with gr.Blocks(css='style.css') as demo:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div>
                <h1 style="font-weight: 900; font-size: 3rem; margin-bottom:20px;">
                  Dreamlike Photoreal 2.0
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 96%">
              </p>
              <p style="margin-bottom: 10px; font-size: 98%">
              Api Only</a>
              </p>
            </div>
        """
    )
    with gr.Column(elem_id="col-container") as hide:
        with gr.Row(variant="compact"):
            input_text = gr.Textbox(
                label="Short Prompt",
                show_label=False,
                max_lines=2,
                placeholder="Enter a basic idea and click 'Magic Prompt'. Got no ideas? No problem, Simply just hit the magic button!",
            ).style(
                container=False,
            )
            see_prompts = gr.Button("โœจ Magic Prompt โœจ").style(full_width=False)

        
        with gr.Row(variant="compact"):
            prompt = gr.Textbox(
                label="Enter your prompt",
                show_label=False,
                max_lines=2,
                placeholder="Full Prompt",
            ).style(
                container=False,
            )
            run = gr.Button("Generate Images").style(full_width=False)
        
        with gr.Row():
            with gr.Row():
                noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level")
        with gr.Row():
            with gr.Row():
                output1=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False)
                output2=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False)
        
    #with gr.Row():
        #output1=gr.Image()

        see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
        run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
        run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])

        with gr.Column() as teste:
            input_txt = gr.Text()
            output_text = gr.Text()
            submit_text = gr.Button()
            submit_text.click(send_it_all, inputs=[input_txt] , outputs=[output1, output2], api_name="text", show_progress=True)

        teste.unrender()


        with gr.Row():
                gr.HTML(
    """
        <div class="footer">
        <p> Demo for <a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0">Dreamlike Photoreal 2.0</a> Stable Diffusion model
</p>
</div>
        <div class="acknowledgments" style="font-size: 115%">
            <p> Unleash your creative side and generate mesmerizing images with just a few clicks! Enter a spark of inspiration in the "Basic Idea" text box and click the "Magic Prompt" button to elevate it to a polished masterpiece. Make any final tweaks in the "Full Prompt" box and hit the "Generate Images" button to watch your vision come to life. Experiment with the "Noise Level" for a diverse range of outputs, from similar to wildly unique. Let the fun begin!
            </p>
        </div>
    """
)

    hide.unrender()
    demo.launch(enable_queue=True, inline=True)
    block.queue(concurrency_count=100)