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 from io import BytesIO import base64 import re from gradio_client import Client from fake_useragent import UserAgent import random from theme import theme from fastapi import FastAPI app = FastAPI() @app.get("/") 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 if task == "Playground v2": ua = UserAgent() headers = { 'user-agent': f'{ua.random}' } client = Client("https://ashrafb-arpr.hf.space/", headers=headers) result = client.predict(prompt, fn_index=0) return result if task == "Artigen v3": ua = UserAgent() headers = { 'user-agent': f'{ua.random}' } client = Client("https://ashrafb-arv3s.hf.space/", headers=headers) result = client.predict(prompt,0,"Cinematic", fn_index=0) return result try: with closing(create_connection("wss://google-sdxl.hf.space/queue/join")) 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 except: try: ua = UserAgent() headers = { 'authority': 'ehristoforu-dalle-3-xl-lora-v2.hf.space', 'accept': 'text/event-stream', 'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7', 'cache-control': 'no-cache', 'referer': 'https://ehristoforu-dalle-3-xl-lora-v2.hf.space/?__theme=light', 'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"Windows"', 'sec-fetch-dest': 'empty', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-origin', 'user-agent': f'{ua.random}' } client = Client("ehristoforu/dalle-3-xl-lora-v2", headers=headers) result = client.predict(prompt,"(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",True,0,1024,1024,6,True, api_name='/run') return result[0][0]['image'] except: try: ua = UserAgent() headers = { 'authority': 'nymbo-sd-xl.hf.space', 'accept': 'text/event-stream', 'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7', 'cache-control': 'no-cache', 'referer': 'https://nymbo-sd-xl.hf.space/?__theme=light', 'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"Windows"', 'sec-fetch-dest': 'empty', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-origin', 'user-agent': f'{ua.random}' } client = Client("Nymbo/SD-XL", headers=headers) result = client.predict(prompt,negative_prompt,"","",True,False,False,0,1024,1024,7,1,25,25,False,api_name="/run") return result except: try: ua = UserAgent() headers = { 'authority': 'radames-real-time-text-to-image-sdxl-lightning.hf.space', 'accept': 'text/event-stream', 'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7', 'cache-control': 'no-cache', 'referer': 'https://radames-real-time-text-to-image-sdxl-lightning.hf.space/?__theme=light', 'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"Windows"', 'sec-fetch-dest': 'empty', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-origin', 'user-agent': f'{ua.random}' } client = Client("radames/Real-Time-Text-to-Image-SDXL-Lightning", headers=headers) result = client.predict(prompt, [], 0, random.randint(1, 999999), fn_index=0) return result except: try: ua = UserAgent() headers = { 'user-agent': f'{ua.random}' } client = Client("https://ashrafb-arpr.hf.space/", headers=headers) result = client.predict(prompt, fn_index=0) return result except: ua = UserAgent() headers = { 'user-agent': f'{ua.random}' } client = Client("https://ashrafb-arv3s.hf.space/", headers=headers) result = client.predict(prompt,0,"Cinematic", fn_index=0) return result def mirror(image_output, scale_by, method, gfpgan, codeformer): url_up = "https://darkstorm2150-protogen-web-ui.hf.space/run/predict/" url_up_f = "https://darkstorm2150-protogen-web-ui.hf.space/file=" 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":""} r = requests.post(url_up, json=data, timeout=100) print(r.text) print(r.json()['data'][0][0]['name']) ph = "https://darkstorm2150-protogen-web-ui.hf.space/file=" + str(r.json()['data'][0][0]['name']) print(ph) response2 = requests.get(ph) img = Image.open(BytesIO(response2.content)) return img examples = [ "a beautiful woman with blonde hair and blue eyes", "a beautiful woman with brown hair and grey eyes", "a beautiful woman with black hair and brown eyes", ] # CSS to style the app css = """ .gradio-container {background-color: MediumAquaMarine} footer{display:none !important} #app-container { max-width: 930px; margin-left: auto; margin-right: auto; } """ with gr.Blocks(css=css, theme=theme, fill_width= False) as app: with gr.Tab("Basic Settings"): with gr.Row(): prompt = gr.Textbox(placeholder="Enter the image description...", show_label=True, label='Image Prompt ✍️', lines=3, scale=6, show_copy_button = True) with gr.Row(): task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Model of neural network:", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL', 'Juggernaut XL', 'DreamShaper XL', 'SDXL Niji', 'Cinemax SDXL', 'NightVision XL']) with gr.Row(): gr.Examples( examples = examples, inputs = [prompt], ) with gr.Tab("Extended settings"): 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++ S", show_label=True, label="Sampling Method:", choices=[ "DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"]) 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.Column(): text_button = gr.Button("Generate image", variant='primary', elem_id="generate") with gr.Column(): image_output = gr.Image(show_download_button=True, interactive=False, label='Generated Image 🌄', show_share_button=False, show_fullscreen_button=True, format="png", elem_id="gallery") text_button.click(flip_text, inputs=[prompt, negative_prompt, task, steps, sampler, cfg_scale, seed], outputs=image_output, concurrency_limit=48) clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button") clear_prompt.click(lambda: (None, None), None, [prompt, image_output], queue=False, show_api=False) app.queue(default_concurrency_limit=200, max_size=200) # <-- Sets up a queue with default parameters if __name__ == "__main__": app.launch(show_api=False, share=False)