from transformers import pipeline import gradio as gr import random import string import paddlehub as hub import torch from transformers import AutoModelForCausalLM, AutoTokenizer from loguru import logger language_translation_model = hub.Module(directory=f'./baidu_translate') def getTextTrans(text, source='zh', target='en'): def is_chinese(string): for ch in string: if u'\u4e00' <= ch <= u'\u9fff': return True return False if not is_chinese(text) and target == 'en': return text try: text_translation = language_translation_model.translate(text, source, target) return text_translation except Exception as e: return text space_ids = { "spaces/stabilityai/stable-diffusion": "SD 2.1", "spaces/runwayml/stable-diffusion-v1-5": "SD 1.5", "spaces/stabilityai/stable-diffusion-1": "SD 1.0", "dalle_mini_tab": "Dalle mini", "spaces/IDEA-CCNL/Taiyi-Stable-Diffusion-Chinese": "Taiyi(太乙)", } tab_actions = [] tab_titles = [] extend_prompt_1 = True extend_prompt_2 = True extend_prompt_3 = True thanks_info = "Thanks: " if extend_prompt_1: extend_prompt_pipe = pipeline('text-generation', model='yizhangliu/prompt-extend', max_length=77, pad_token_id=0) thanks_info += "[prompt-extend(1)]" if extend_prompt_2: def load_prompter(): prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist") tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" return prompter_model, tokenizer prompter_model, prompter_tokenizer = load_prompter() def extend_prompt_microsoft(in_text): input_ids = prompter_tokenizer(in_text.strip()+" Rephrase:", return_tensors="pt").input_ids eos_id = prompter_tokenizer.eos_token_id outputs = prompter_model.generate(input_ids, do_sample=False, max_new_tokens=75, num_beams=8, num_return_sequences=8, eos_token_id=eos_id, pad_token_id=eos_id, length_penalty=-1.0) output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True) res = output_texts[0].replace(in_text+" Rephrase:", "").strip() return res thanks_info += "[Promptist(2)]" if extend_prompt_3: MagicPrompt = gr.Interface.load("spaces/Gustavosta/MagicPrompt-Stable-Diffusion") thanks_info += "[MagicPrompt(3)]" do_dreamlike_photoreal = False if do_dreamlike_photoreal: def add_random_noise(prompt, noise_level=0.1): # 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) for index in noise_indices: prompt_list[index] = random.choice(string.ascii_letters + string.punctuation) new_prompt = "".join(prompt_list) return new_prompt dreamlike_photoreal_2_0 = gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0") dreamlike_image = gr.Image(label="Dreamlike Photoreal 2.0") tab_actions.append(dreamlike_image) tab_titles.append("Dreamlike_2.0") thanks_info += "[dreamlike-photoreal-2.0]" for space_id in space_ids.keys(): print(space_id, space_ids[space_id]) try: tab_title = space_ids[space_id] tab_titles.append(tab_title) if (tab_title == 'Dalle mini'): tab_content = gr.Blocks(elem_id='dalle_mini') tab_actions.append(tab_content) else: tab_content = gr.Interface.load(space_id) tab_actions.append(tab_content) thanks_info += f"[{tab_title}]" except Exception as e: logger.info(f"load_fail__{space_id}_{e}") start_work = """async() => { function isMobile() { try { document.createEvent("TouchEvent"); return true; } catch(e) { return false; } } function getClientHeight() { var clientHeight=0; if(document.body.clientHeight&&document.documentElement.clientHeight) { var clientHeight = (document.body.clientHeightdocument.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight; } return clientHeight; } function setNativeValue(element, value) { const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set; const prototype = Object.getPrototypeOf(element); const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set; if (valueSetter && valueSetter !== prototypeValueSetter) { prototypeValueSetter.call(element, value); } else { valueSetter.call(element, value); } } window['tab_advanced'] = 0; var gradioEl = document.querySelector('body > gradio-app').shadowRoot; if (!gradioEl) { gradioEl = document.querySelector('body > gradio-app'); } if (typeof window['gradioEl'] === 'undefined') { window['gradioEl'] = gradioEl; tabitems = window['gradioEl'].querySelectorAll('.tabitem'); tabitems_title = window['gradioEl'].querySelectorAll('#tab_demo')[0].children[0].children[0].children; window['dalle_mini_block'] = null; window['dalle_mini_iframe'] = null; for (var i = 0; i < tabitems.length; i++) { if (tabitems_title[i].innerText.indexOf('SD') >= 0) { tabitems[i].childNodes[0].children[0].style.display='none'; for (var j = 0; j < tabitems[i].childNodes[0].children[1].children.length; j++) { if (j != 1) { tabitems[i].childNodes[0].children[1].children[j].style.display='none'; } } if (tabitems_title[i].innerText.indexOf('SD 1') >= 0) { for (var j = 0; j < 4; j++) { tabitems[i].childNodes[0].children[1].children[3].children[1].children[j].children[2].removeAttribute("disabled"); } } else if (tabitems_title[i].innerText.indexOf('SD 2') >= 0) { tabitems[i].children[0].children[1].children[3].children[0].click(); } } else if (tabitems_title[i].innerText.indexOf('Taiyi') >= 0) { tabitems[i].children[0].children[0].children[1].style.display='none'; tabitems[i].children[0].children[0].children[0].children[0].children[1].style.display='none'; } else if (tabitems_title[i].innerText.indexOf('Dreamlike') >= 0) { tabitems[i].childNodes[0].children[0].children[1].style.display='none'; } else if (tabitems_title[i].innerText.indexOf('Dalle mini') >= 0) { window['dalle_mini_block']= tabitems[i]; } } tab_demo = window['gradioEl'].querySelectorAll('#tab_demo')[0]; tab_demo.style.display = "block"; tab_demo.setAttribute('style', 'height: 100%;'); const page1 = window['gradioEl'].querySelectorAll('#page_1')[0]; const page2 = window['gradioEl'].querySelectorAll('#page_2')[0]; btns_1 = window['gradioEl'].querySelector('#input_col1_row3').children; btns_1_split = 100 / btns_1.length; for (var i = 0; i < btns_1.length; i++) { btns_1[i].setAttribute('style', 'min-width:0px;width:' + btns_1_split + '%;'); } page1.style.display = "none"; page2.style.display = "block"; prompt_work = window['gradioEl'].querySelectorAll('#prompt_work'); for (var i = 0; i < prompt_work.length; i++) { prompt_work[i].style.display='none'; } window['prevPrompt'] = ''; window['doCheckPrompt'] = 0; window['checkPrompt'] = function checkPrompt() { try { prompt_work = window['gradioEl'].querySelectorAll('#prompt_work'); if (prompt_work.length > 0 && prompt_work[0].children.length > 1) { prompt_work[0].children[1].style.display='none'; prompt_work[0].style.display='block'; } text_value = window['gradioEl'].querySelectorAll('#prompt_work')[0].querySelectorAll('textarea')[0].value; progress_bar = window['gradioEl'].querySelectorAll('.progress-bar'); if (window['doCheckPrompt'] === 0 && window['prevPrompt'] !== text_value && progress_bar.length == 0) { console.log('_____new prompt___[' + text_value + ']_'); window['doCheckPrompt'] = 1; window['prevPrompt'] = text_value; tabitems = window['gradioEl'].querySelectorAll('.tabitem'); for (var i = 0; i < tabitems.length; i++) { if (tabitems_title[i].innerText.indexOf('Dalle mini') >= 0) { if (window['dalle_mini_block']) { if (window['dalle_mini_iframe'] === null) { window['dalle_mini_iframe'] = document.createElement('iframe'); window['dalle_mini_iframe'].height = 1000; window['dalle_mini_iframe'].width = '100%'; window['dalle_mini_iframe'].id = 'dalle_iframe'; window['dalle_mini_block'].appendChild(window['dalle_mini_iframe']); } window['dalle_mini_iframe'].src = 'https://yizhangliu-dalleclone.hf.space/index.html?prompt=' + encodeURI(text_value); console.log('dalle_mini'); } continue; } inputText = null; if (tabitems_title[i].innerText.indexOf('SD') >= 0) { text_value = window['gradioEl'].querySelectorAll('#prompt_work')[0].querySelectorAll('textarea')[0].value; inputText = tabitems[i].children[0].children[1].children[0].querySelectorAll('.gr-text-input')[0]; } else if (tabitems_title[i].innerText.indexOf('Taiyi') >= 0) { text_value = window['gradioEl'].querySelectorAll('#prompt_work_zh')[0].querySelectorAll('textarea')[0].value; inputText = tabitems[i].children[0].children[0].children[1].querySelectorAll('.gr-text-input')[0]; } if (inputText) { setNativeValue(inputText, text_value); inputText.dispatchEvent(new Event('input', { bubbles: true })); } } setTimeout(function() { btns = window['gradioEl'].querySelectorAll('button'); for (var i = 0; i < btns.length; i++) { if (['Generate image','Run', '生成图像(Generate)'].includes(btns[i].innerText)) { btns[i].click(); } } window['doCheckPrompt'] = 0; }, 10); } } catch(e) { } } window['checkPrompt_interval'] = window.setInterval("window.checkPrompt()", 100); } return false; }""" switch_tab_advanced = """async() => { window['tab_advanced'] = 1 - window['tab_advanced']; if (window['tab_advanced']==0) { action = 'none'; } else { action = 'block'; } tabitems = window['gradioEl'].querySelectorAll('.tabitem'); tabitems_title = window['gradioEl'].querySelectorAll('#tab_demo')[0].children[0].children[0].children; for (var i = 0; i < tabitems.length; i++) { if (tabitems_title[i].innerText.indexOf('SD') >= 0) { //tabitems[i].childNodes[0].children[1].children[0].style.display=action; //tabitems[i].childNodes[0].children[1].children[4].style.display=action; for (var j = 0; j < tabitems[i].childNodes[0].children[1].children.length; j++) { if (j != 1) { tabitems[i].childNodes[0].children[1].children[j].style.display=action; } } } else if (tabitems_title[i].innerText.indexOf('Taiyi') >= 0) { tabitems[i].children[0].children[0].children[1].style.display=action; } } return false; }""" def prompt_extend(prompt, PM): prompt_en = getTextTrans(prompt, source='zh', target='en') if PM == 1: extend_prompt_en = extend_prompt_pipe(prompt_en+',', num_return_sequences=1)[0]["generated_text"] elif PM == 2: extend_prompt_en = extend_prompt_microsoft(prompt_en) elif PM == 3: extend_prompt_en = MagicPrompt(prompt_en) if (prompt != prompt_en): logger.info(f"extend_prompt__1_PM=[{PM}]_") extend_prompt_out = getTextTrans(extend_prompt_en, source='en', target='zh') else: logger.info(f"extend_prompt__2_PM=[{PM}]_") extend_prompt_out = extend_prompt_en return extend_prompt_out def prompt_extend_1(prompt): extend_prompt_out = prompt_extend(prompt, 1) return extend_prompt_out def prompt_extend_2(prompt): extend_prompt_out = prompt_extend(prompt, 2) return extend_prompt_out def prompt_extend_3(prompt): extend_prompt_out = prompt_extend(prompt, 3) return extend_prompt_out def prompt_draw_1(prompt, noise_level): prompt_en = getTextTrans(prompt, source='zh', target='en') if (prompt != prompt_en): logger.info(f"draw_prompt______1__") prompt_zh = prompt else: logger.info(f"draw_prompt______2__") prompt_zh = getTextTrans(prompt, source='en', target='zh') prompt_with_noise = add_random_noise(prompt_en, noise_level) dreamlike_output = dreamlike_photoreal_2_0(prompt_with_noise) return prompt_en, prompt_zh, dreamlike_output def prompt_draw_2(prompt): prompt_en = getTextTrans(prompt, source='zh', target='en') if (prompt != prompt_en): logger.info(f"draw_prompt______1__") prompt_zh = prompt else: logger.info(f"draw_prompt______2__") prompt_zh = getTextTrans(prompt, source='en', target='zh') return prompt_en, prompt_zh with gr.Blocks(title='Text-to-Image') as demo: with gr.Group(elem_id="page_1", visible=True) as page_1: with gr.Box(): with gr.Row(): start_button = gr.Button("Let's GO!", elem_id="start-btn", visible=True) start_button.click(fn=None, inputs=[], outputs=[], _js=start_work) with gr.Group(elem_id="page_2", visible=False) as page_2: with gr.Row(elem_id="prompt_row0"): with gr.Column(id="input_col1"): with gr.Row(elem_id="input_col1_row1"): prompt_input0 = gr.Textbox(lines=2, label="Original prompt", visible=True) with gr.Row(elem_id="input_col1_row2"): prompt_work = gr.Textbox(lines=1, label="prompt_work", elem_id="prompt_work", visible=True) with gr.Row(elem_id="input_col1_row3"): with gr.Column(elem_id="input_col1_row2_col0"): draw_btn_0 = gr.Button(value = "Generate(original)", elem_id="draw-btn-0") if extend_prompt_1: with gr.Column(elem_id="input_col1_row2_col1"): extend_btn_1 = gr.Button(value = "Extend_1",elem_id="extend-btn-1") if extend_prompt_2: with gr.Column(elem_id="input_col1_row2_col2"): extend_btn_2 = gr.Button(value = "Extend_2",elem_id="extend-btn-2") if extend_prompt_3: with gr.Column(elem_id="input_col1_row2_col3"): extend_btn_3 = gr.Button(value = "Extend_3",elem_id="extend-btn-3") with gr.Column(id="input_col2"): prompt_input1 = gr.Textbox(lines=2, label="Extend prompt", visible=True) draw_btn_1 = gr.Button(value = "Generate(extend)", elem_id="draw-btn-1") with gr.Row(elem_id="prompt_row1"): with gr.Column(id="input_col3"): with gr.Row(elem_id="input_col3_row2"): prompt_work_zh = gr.Textbox(lines=1, label="prompt_work_zh", elem_id="prompt_work_zh", visible=False) with gr.Row(elem_id='tab_demo', visible=True).style(height=200): tab_demo = gr.TabbedInterface(tab_actions, tab_titles) if do_dreamlike_photoreal: with gr.Row(): noise_level=gr.Slider(minimum=0.1, maximum=3, step=0.1, label="Dreamlike noise Level: [Higher noise level produces more diverse outputs, while lower noise level produces similar outputs.]") with gr.Row(): switch_tab_advanced_btn = gr.Button(value = "Switch_tab_advanced", elem_id="switch_tab_advanced_btn") switch_tab_advanced_btn.click(fn=None, inputs=[], outputs=[], _js=switch_tab_advanced) with gr.Row(): gr.HTML(f"

{thanks_info}

") if extend_prompt_1: extend_btn_1.click(fn=prompt_extend_1, inputs=[prompt_input0], outputs=[prompt_input1]) if extend_prompt_2: extend_btn_2.click(fn=prompt_extend_2, inputs=[prompt_input0], outputs=[prompt_input1]) if extend_prompt_3: extend_btn_3.click(fn=prompt_extend_3, inputs=[prompt_input0], outputs=[prompt_input1]) if do_dreamlike_photoreal: draw_btn_0.click(fn=prompt_draw_1, inputs=[prompt_input0, noise_level], outputs=[prompt_work, prompt_work_zh, dreamlike_image]) draw_btn_1.click(fn=prompt_draw_1, inputs=[prompt_input1, noise_level], outputs=[prompt_work, prompt_work_zh, dreamlike_image]) else: draw_btn_0.click(fn=prompt_draw_2, inputs=[prompt_input0], outputs=[prompt_work, prompt_work_zh]) draw_btn_1.click(fn=prompt_draw_2, inputs=[prompt_input1], outputs=[prompt_work, prompt_work_zh]) demo.queue() demo.launch()