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("dreamlikeart, " + prompt_text) else: return text_gen("") proc1=gr.Interface.load("models/dreamlike-art/dreamlike-diffusion-1.0") 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=0.00): if noise_level == 0: noise_level = 0.00 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(['😍', 'ðŸ’Đ', '😂', 'ðŸĪ”', '😊', 'ðŸĪ—', '😭', '🙄', '😷', 'ðŸĪŊ', 'ðŸĪŦ', 'ðŸĨī', 'ðŸ˜ī', 'ðŸĪĐ', 'ðŸĨģ', '😔', 'ðŸ˜Đ', 'ðŸĪŠ', '😇', 'ðŸĪĒ', '😈', 'ðŸ‘đ', 'ðŸ‘ŧ', 'ðŸĪ–', 'ðŸ‘―', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', 'ðŸŽŪ', 'âĪïļ', '💔', '💕', '💖', '💗', 'ðŸķ', 'ðŸą', '🐭', 'ðŸđ', 'ðŸĶŠ', 'ðŸŧ', 'ðŸĻ', 'ðŸŊ', 'ðŸĶ', '🐘', 'ðŸ”Ĩ', '🌧ïļ', '🌞', '🌈', 'ðŸ’Ĩ', 'ðŸŒī', '🌊', '🌚', 'ðŸŒŧ', 'ðŸŒļ', 'ðŸŽĻ', '🌅', '🌌', '☁ïļ', '⛈ïļ', '❄ïļ', '☀ïļ', 'ðŸŒĪïļ', '⛅ïļ', 'ðŸŒĨïļ', 'ðŸŒĶïļ', '🌧ïļ', 'ðŸŒĐïļ', 'ðŸŒĻïļ', 'ðŸŒŦïļ', '☔ïļ', '🌎ïļ', 'ðŸ’Ļ', '🌊ïļ', '🌈']) 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 with gr.Blocks(css='style.css') as demo: gr.HTML( """

Image Creation Demo

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.

""" ) 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'. Got no ideas? No problem, Simply just hit the magic button!", ).style( container=False, ) see_prompts = gr.Button("âœĻ Magic Prompt âœĻ").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) 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( """ """ ) demo.launch(enable_queue=True, inline=True) block.queue(concurrency_count=100)