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()