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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
text_gen=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion")
def get_prompts(prompt_text):
return text_gen(prompt_text)
proc1=gr.Interface.load("models/dreamlike-art/dreamlike-diffusion-1.0")
def restart_script_periodically():
while True:
time.sleep(600) # 10 minutes
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=0.07):
if noise_level == 0:
noise_level = 0.07
# Get the percentage of characters to add as noise
percentage_noise = noise_level * 5
# Get the number of characters to add as noise
num_noise_chars = int(len(prompt) * (percentage_noise/100))
# Get the indices of the characters to add noise to
noise_indices = random.sample(range(len(prompt)), num_noise_chars)
# Add noise to the selected characters
prompt_list = list(prompt)
noise_chars = string.ascii_letters + string.punctuation + ' '
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_it5(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)
#output5 = proc1(prompt_with_noise)
#return output5
#def send_it6(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)
#output6 = proc1(prompt_with_noise)
#return output6
#def send_it7(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)
#output7 = proc1(prompt_with_noise)
#return output7
#def send_it8(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)
#output8 = proc1(prompt_with_noise)
#return output8
css = '''
#col-container {max-width: 800px; margin-left: auto; margin-right: auto;}
a {
color: inherit;
text-decoration: underline;
}
'''
with gr.Blocks(css=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 Diffusion 1.0
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 96%">
Noise Level: Controls how much randomness is added to the input before it is sent to the model. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs,
<a href="https://twitter.com/DavidJohnstonxx/">created by Phenomenon1981</a>.
</p>
<p style="margin-bottom: 10px; font-size: 98%">
❤️ Press the Like Button if you enjoy my space! ❤️</a>.
</p>
</div>
"""
)
with gr.Column(elem_id="col-container"):
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'",
).style(
container=False,
)
see_prompts = gr.Button("✨ Magic Prompt ✨").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 Diffusion 1.0",show_label=False)
output2=gr.Image(label="Dreamlike Diffusion 1.0",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.Row():
gr.HTML(
"""
<div class="footer" style="margin-bottom: 45px; margin-top: 15px; text-align: center; border-bottom: 1px solid #e5e5e5;">
<p style="font-size: .8rem; display: inline-block; padding: 0 10px; transform: translateY(10px); background: #0b0f19;"> Demo for <a href="https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0">Dreamlike Diffusion 1.0</a> Stable Diffusion model
</p>
</div>
<div class="acknowledgments" style="margin: 1.25em 0 .25em 0; font-size: 115%;">
<p style="margin: 1.25em 0 .25em 0; font-size: 115%;"> 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>
"""
)
demo.queue(concurrency_count=8)
demo.launch(enable_queue=True, inline=True)