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'] 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()