app.py

import gradio as gr
import requests
import time
import json
from contextlib import closing
from websocket import create_connection
from deep_translator import GoogleTranslator
from langdetect import detect
import os
from PIL import Image
import io
import base64
import re
from gradio_client import Client

def flip_text(prompt, negative_prompt, task, steps, sampler, cfg_scale, seed):
    result = {"prompt": prompt,"negative_prompt": negative_prompt,"task": task,"steps": steps,"sampler": sampler,"cfg_scale": cfg_scale,"seed": seed}
    print(result)
    try:
        language = detect(prompt)
        if language == 'ru':
            prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
            print(prompt)
    except:
        pass

    prompt = re.sub(r'[^a-zA-Zа-яА-Я\s]', '', prompt)
    
    cfg = int(cfg_scale)
    steps = int(steps)
    seed = int(seed)

    width = 1024
    height = 1024
    url_sd1 = os.getenv("url_sd1")
    url_sd2 = os.getenv("url_sd2")
    
    url_sd3 = os.getenv("url_sd3")
    url_sd4 = os.getenv("url_sd4")

    print("--3-->", url_sd3)
    print("--4-->", url_sd4)
    
    url_sd5 = os.getenv("url_sd5")
    url_sd6 = os.getenv("url_sd6")
    hf_token = os.getenv("hf_token")
    if task == "Playground v2":
        #playground = str(os.getenv("playground"))
        #client = Client(playground, hf_token=hf_token)
        #result = client.predict(prompt, "", False, 220, 1024, 1024, 3, True, api_name="/run")
        #return result[0][0]['image']
        client = Client("multimodalart/stable-cascade")
        result = client.predict(
            "cat",	# str  in 'Prompt' Textbox component
            "",	# str  in 'Negative prompt' Textbox component
            0,	# float (numeric value between 0 and 2147483647) in 'Seed' Slider component
            1024,	# float (numeric value between 1024 and 1536) in 'Width' Slider component
            1024,	# float (numeric value between 1024 and 1536) in 'Height' Slider component
            20,	# float (numeric value between 10 and 30) in 'Prior Inference Steps' Slider component
            4,	# float (numeric value between 0 and 20) in 'Prior Guidance Scale' Slider component
            10,	# float (numeric value between 4 and 12) in 'Decoder Inference Steps' Slider component
            0,	# float (numeric value between 0 and 0) in 'Decoder Guidance Scale' Slider component
            1,	# float (numeric value between 1 and 2) in 'Number of Images' Slider component
		api_name="/run")
        return result
        
    if task == "OpenDalle v1.1":
        opendalle = str(os.getenv("opendalle"))
        client = Client(opendalle, hf_token=hf_token)
        result = client.predict(prompt, "", "", "", False, False, False, 999, 1024, 1024, 5, 5, 25, 25, False, api_name="/run")
        return result
        
    try:
        with closing(create_connection(f"{url_sd3}", timeout=60)) as conn:
            conn.send('{"fn_index":3,"session_hash":""}')
            conn.send(f'{{"data":["{prompt}, 4k photo","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry",7.5,"(No style)"],"event_data":null,"fn_index":3,"session_hash":""}}')
            while True:
                status = json.loads(conn.recv())['msg']
                if status == 'estimation':
                    continue
                if status == 'process_starts':
                    break
            photo = json.loads(conn.recv())['output']['data'][0][0]
            photo = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
            photo = Image.open(io.BytesIO(base64.decodebytes(bytes(photo, "utf-8"))))
            return photo
            #data = {"inputs":f"{prompt}, 4k photo","options":{"negative_prompt":"[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","width":1024,"height":1024,"guidance_scale":7,"num_inference_steps":35}}
            #response = requests.post(f'{url_sd5}', json=data)
            #print(response.text)
            #print(response.json()['image']['file_name'])
            #file_name = response.json()['image']['file_name']
            #photo = f"{url_sd6}{file_name}.png"
            #return photo
    except:
       with closing(create_connection(f"{url_sd4}", timeout=60)) as conn:
            conn.send('{"fn_index":0,"session_hash":""}')
            conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","dreamshaperXL10_alpha2.safetensors [c8afe2ef]",30,"DPM++ 2M Karras",7,1024,1024,-1],"event_data":null,"fn_index":0,"session_hash":""}}')
            conn.recv()
            conn.recv()
            conn.recv()
            conn.recv()
            photo = json.loads(conn.recv())['output']['data'][0]
            photo = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
            photo = Image.open(io.BytesIO(base64.decodebytes(bytes(photo, "utf-8"))))
            return photo
    
#except:
#    try:
#        client = Client("https://prodia-sdxl-stable-diffusion-xl.hf.space")
#        result = client.predict(prompt,"[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","sd_xl_base_1.0.safetensors [be9edd61]",25,"DPM++ 2M Karras",7,1024,1024,-1,fn_index=0)
#        return result
#    except:
#        print("n_2")
#        print(url_sd4)
#        with closing(create_connection(f"{url_sd4}", timeout=60)) as conn:
#            conn.send('{"fn_index":0,"session_hash":""}')
#            conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","dreamshaperXL10_alpha2.safetensors [c8afe2ef]",30,"DPM++ 2M Karras",7,1024,1024,-1],"event_data":null,"fn_index":0,"session_hash":""}}')
#            conn.recv()
#            conn.recv()
#            conn.recv()
#            conn.recv()
#            photo = json.loads(conn.recv())['output']['data'][0]
#            photo = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
#            photo = Image.open(io.BytesIO(base64.decodebytes(bytes(photo, "utf-8"))))
#            return photo


def flipp():
    if task == 'Stable Diffusion XL 1.0':
        model = 'sd_xl_base_1.0'
    if task == 'Crystal Clear XL':
        model = '[3d] crystalClearXL_ccxl_97637'
    if task == 'Juggernaut XL':
        model = '[photorealistic] juggernautXL_version2_113240'
    if task == 'DreamShaper XL':
        model = '[base model] dreamshaperXL09Alpha_alpha2Xl10_91562'
    if task == 'SDXL Niji':
        model = '[midjourney] sdxlNijiV51_sdxlNijiV51_112807'
    if task == 'Cinemax SDXL':
        model = '[movie] cinemaxAlphaSDXLCinema_alpha1_107473'
    if task == 'NightVision XL':
        model = '[photorealistic] nightvisionXLPhotorealisticPortrait_beta0702Bakedvae_113098'
        
    print("n_3")
    negative = negative_prompt
    
    try:
        with closing(create_connection(f"{url_sd1}")) as conn:
            conn.send('{"fn_index":231,"session_hash":""}')
            conn.send(f'{{"data":["task()","{prompt}","{negative}",[],{steps},"{sampler}",false,false,1,1,{cfg},{seed},-1,0,0,0,false,{width},{height},false,0.7,2,"Lanczos",0,0,0,"Use same sampler","","",[],"None",true,"{model}","Automatic",null,null,null,false,false,"positive","comma",0,false,false,"","Seed","",[],"Nothing","",[],"Nothing","",[],true,false,false,false,0,null,null,false,null,null,false,null,null,false,50,[],"","",""],"event_data":null,"fn_index":231,"session_hash":""}}')
            print(conn.recv())
            print(conn.recv())
            print(conn.recv())
            print(conn.recv())
            photo = f"{url_sd2}" + str(json.loads(conn.recv())['output']['data'][0][0]["name"])
        return photo
    except:
        return None



def mirror(image_output, scale_by, method, gfpgan, codeformer):

    url_up = os.getenv("url_up")
    url_up_f = os.getenv("url_up_f")

    print(url_up)
    print(url_up_f)

    scale_by = int(scale_by)
    gfpgan = int(gfpgan)
    codeformer = int(codeformer)
    
    with open(image_output, "rb") as image_file:
        encoded_string2 = base64.b64encode(image_file.read())
        encoded_string2 = str(encoded_string2).replace("b'", '')

    encoded_string2 = "data:image/png;base64," + encoded_string2
    data = {"fn_index":81,"data":[0,0,encoded_string2,None,"","",True,gfpgan,codeformer,0,scale_by,512,512,None,method,"None",1,False,[],"",""],"session_hash":""}
    print(data)
    r = requests.post(f"{url_up}", json=data, timeout=100)
    print(r.text)
    ph = f"{url_up_f}" + str(r.json()['data'][0][0]['name'])
    return ph

css = """
#generate {
    width: 100%;
    background: #e253dd !important;
    border: none;
    border-radius: 50px;
    outline: none !important;
    color: white;
}
#generate:hover {
    background: #de6bda !important;
    outline: none !important;
    color: #fff;
    }
footer {visibility: hidden !important;}

#image_output {
height: 100% !important;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Tab("Базовые настройки"):
        with gr.Row():
            prompt = gr.Textbox(placeholder="Введите описание изображения...", show_label=True, label='Описание изображения:', lines=3)
        with gr.Row():
            task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Модель нейросети:", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL', 
                                                                                                              'Juggernaut XL', 'DreamShaper XL',
                                                                                                              'SDXL Niji', 'Cinemax SDXL', 'NightVision XL',
                                                                                                              'Playground v2', 'OpenDalle v1.1'])
    with gr.Tab("Расширенные настройки"):
        with gr.Row():
            negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=True, label='Negative Prompt:', lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry")
        with gr.Row():
            sampler = gr.Dropdown(value="DPM++ SDE Karras", show_label=True, label="Sampling Method:", choices=[
                "Euler", "Euler a", "Heun", "DPM++ 2M", "DPM++ SDE", "DPM++ 2M Karras", "DPM++ SDE Karras", "DDIM"])
        with gr.Row():
            steps = gr.Slider(show_label=True, label="Sampling Steps:", minimum=1, maximum=50, value=35, step=1)
        with gr.Row():
            cfg_scale = gr.Slider(show_label=True, label="CFG Scale:", minimum=1, maximum=20, value=7, step=1)
        with gr.Row():
            seed = gr.Number(show_label=True, label="Seed:", minimum=-1, maximum=1000000, value=-1, step=1)
    
    with gr.Tab("Настройки апскейлинга"):
        with gr.Column():
            with gr.Row():
                scale_by = gr.Number(show_label=True, label="Во сколько раз увеличить:", minimum=1, maximum=2, value=2, step=1)
            with gr.Row():
                method = gr.Dropdown(show_label=True, value="ESRGAN_4x", label="Алгоритм увеличения", choices=["ScuNET GAN", "SwinIR 4x", "ESRGAN_4x", "R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"])
        with gr.Column():
            with gr.Row():
                gfpgan = gr.Slider(show_label=True, label="Эффект GFPGAN (для улучшения лица)", minimum=0, maximum=1, value=0, step=0.1)
            with gr.Row():
                codeformer = gr.Slider(show_label=True, label="Эффект CodeFormer (для улучшения лица)", minimum=0, maximum=1, value=0, step=0.1)
    
    with gr.Column():
        text_button = gr.Button("Сгенерировать изображение", variant='primary', elem_id="generate")
    with gr.Column():
        image_output = gr.Image(show_download_button=True, interactive=False, label='Результат:', elem_id='image_output', type='filepath')
        text_button.click(flip_text, inputs=[prompt, negative_prompt, task, steps, sampler, cfg_scale, seed], outputs=image_output)
        
        img2img_b = gr.Button("Увеличить изображение", variant='secondary')
        image_i2i = gr.Image(show_label=True, label='Увеличенное изображение:')
        img2img_b.click(mirror, inputs=[image_output, scale_by, method, gfpgan, codeformer], outputs=image_i2i)
    
demo.queue(concurrency_count=12)
demo.launch()