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| 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")) | |
| with closing(create_connection("wss://ashrafb-arpr.hf.space/queue/join", timeout=60)) as conn: | |
| conn.send('{"fn_index":0,"session_hash":""}') | |
| conn.send(f'{{"fn_index":0,"data":["{prompt}"],"session_hash":""}}') | |
| conn.recv() | |
| conn.recv() | |
| conn.recv() | |
| conn.recv() | |
| a = conn.recv() | |
| print(">> A:", a) | |
| photo = json.loads(a)['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 | |
| if task == "Artigen v3": | |
| artigen = str(os.getenv("artigen")) | |
| with closing(create_connection("wss://ashrafb-arv3s.hf.space/queue/join", timeout=60)) as conn: | |
| conn.send('{"fn_index":0,"session_hash":""}') | |
| conn.send(f'{{"fn_index":0,"data":["{prompt}", 0, "No style"],"session_hash":""}}') | |
| conn.recv() | |
| conn.recv() | |
| conn.recv() | |
| conn.recv() | |
| a = conn.recv() | |
| print(">> A:", a) | |
| photo = json.loads(a)['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 | |
| 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":""}}') | |
| c = 0 | |
| while c < 60: | |
| status = json.loads(conn.recv())['msg'] | |
| if status == 'estimation': | |
| c += 1 | |
| time.sleep(1) | |
| 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("~~ up", url_up) | |
| print("~~ f", 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', 'Artigen v3']) | |
| 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=24) | |
| demo.launch() |