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import gradio as gr |
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import os |
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import sys |
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from pathlib import Path |
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import random |
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import string |
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import time |
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from queue import Queue |
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from threading import Thread |
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import emoji |
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text_gen=gr.Interface.load("spaces/Dao3/MagicPrompt-Stable-Diffusion") |
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def get_prompts(prompt_text): |
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if prompt_text: |
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return text_gen("openjourneyart, " + prompt_text) |
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else: |
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return text_gen("") |
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proc1=gr.Interface.load("models/prompthero/openjourney") |
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def restart_script_periodically(): |
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while True: |
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random_time = random.randint(540, 600) |
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time.sleep(random_time) |
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os.execl(sys.executable, sys.executable, *sys.argv) |
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restart_thread = Thread(target=restart_script_periodically, daemon=True) |
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restart_thread.start() |
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queue = Queue() |
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queue_threshold = 100 |
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def add_random_noise(prompt, noise_level=0.00): |
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if noise_level == 0: |
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noise_level = 0.00 |
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percentage_noise = noise_level * 5 |
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num_noise_chars = int(len(prompt) * (percentage_noise/100)) |
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noise_indices = random.sample(range(len(prompt)), num_noise_chars) |
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prompt_list = list(prompt) |
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noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits) |
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noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈']) |
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for index in noise_indices: |
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prompt_list[index] = random.choice(noise_chars) |
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return "".join(prompt_list) |
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def send_it1(inputs, noise_level, proc1=proc1): |
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prompt_with_noise = add_random_noise(inputs, noise_level) |
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while queue.qsize() >= queue_threshold: |
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time.sleep(2) |
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queue.put(prompt_with_noise) |
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output1 = proc1(prompt_with_noise) |
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return output1 |
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def send_it2(inputs, noise_level, proc1=proc1): |
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prompt_with_noise = add_random_noise(inputs, noise_level) |
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while queue.qsize() >= queue_threshold: |
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time.sleep(2) |
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queue.put(prompt_with_noise) |
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output2 = proc1(prompt_with_noise) |
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return output2 |
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with gr.Blocks(css='style.css') as demo: |
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gr.HTML( |
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""" |
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<div style="text-align: center; max-width: 650px; margin: 0 auto;"> |
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<div> |
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<h1 style="font-weight: 900; font-size: 3rem; margin-bottom:20px;"> |
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OpenART |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 96%"> |
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差异程度: 用数值调节两张图的差异程度。数值越大，两张图的差异越大，反之越小。 |
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</p> |
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<p style="margin-bottom: 10px; font-size: 98%"> |
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❤️ 喜欢的话，就点上面的❤️吧~❤️</a> |
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</p> |
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</div> |
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""" |
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) |
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with gr.Column(elem_id="col-container"): |
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with gr.Row(variant="compact"): |
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input_text = gr.Textbox( |
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label="Short Prompt", |
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show_label=False, |
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max_lines=2, |
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placeholder="输入你的想象（英文词汇），然后按右边按钮。没灵感？直接按！", |
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).style( |
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container=False, |
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) |
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see_prompts = gr.Button("✨ 咒语显现 ✨").style(full_width=False) |
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with gr.Row(variant="compact"): |
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prompt = gr.Textbox( |
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label="Enter your prompt", |
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show_label=False, |
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max_lines=2, |
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placeholder="可输入完整描述词，或者用咒语显现按钮生成", |
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).style( |
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container=False, |
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) |
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run = gr.Button("✨幻梦显形✨").style(full_width=False) |
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with gr.Row(): |
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with gr.Row(): |
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noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="差异程度") |
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with gr.Row(): |
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with gr.Row(): |
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output1=gr.Image(label="Dreamlike Diffusion 1.0",show_label=False) |
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output2=gr.Image(label="Dreamlike Diffusion 1.0",show_label=False) |
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see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False) |
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run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1]) |
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run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2]) |
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with gr.Row(): |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<div class="acknowledgments" style="font-size: 115%"> |
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<p> |
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安利：一个汉化项目：<a href="https://tiwenti.chat/">TiwenTi.chat</a>，这是一个ChatGPT的中文案例库，按照工具用途和角色扮演用途做了分类，欢迎去看去分享~ </p> |
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</p> |
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</div> |
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""" |
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) |
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demo.launch(enable_queue=True, inline=True) |
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block.queue(concurrency_count=100) |
