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 query(prompt, negative_prompt, task, steps=35, sampler="DPM++ 2M Karras", cfg_scale=7, seed=-1, width=896, height=1152): result = {prompt:"prompt", negative_prompt:"negative_prompt", task:"task", steps:"steps", sampler:"sampler", cfg_scale:"cfg_scale", seed:"seed" , width:"width" , height:"height" } 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 = int(width) height = int(height) progress=gr.Progress(track_tqdm=True) 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,0,896,1152,7,1,25,25,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,896,1152,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 ##### style_list = [ { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "Photo", "prompt": "cinematic photo {prompt}. 35mm photograph, film, bokeh, professional, 4k, highly detailed", "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", }, { "name": "Cinematic", "prompt": "cinematic still {prompt}. emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", }, { "name": "Anime", "prompt": "anime artwork {prompt}. anime style, key visual, vibrant, studio anime, highly detailed", "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", }, { "name": "3D Model", "prompt": "professional 3d model {prompt}. octane render, highly detailed, volumetric, dramatic lighting", "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", }, { "name": "(No style)", "prompt": "{prompt}", "negative_prompt": "", }, ] styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "2560 x 1440" def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) if not negative: negative = "" return p.replace("{prompt}", positive), n + negative 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 { visibility: hidden; } """ with gr.Blocks(css=css, theme=theme, fill_width= False) as app: with gr.Tab("Basic Settings"): with gr.Column(scale=1): 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']) gr.Examples( examples = examples, inputs = [prompt], ) with gr.Tab("Extended settings"): with gr.Column(scale=3): with gr.Row(): negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=True, label='Negative Prompt:', scale=6, lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry") with gr.Group(): 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(visible=True): style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Image Style", ) 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.Row(): width = gr.Slider(label="Width", minimum=512, maximum=2048, step=8, value=896, interactive=True,) with gr.Row(): height = gr.Slider(label="Height", minimum=512, maximum=2048, step=8, value=1152,interactive=True,) with gr.Column(scale=3): text_button = gr.Button("Generate image", variant="primary", interactive=True, elem_id="generate") with gr.Column(scale=0): 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(query, inputs=[prompt, negative_prompt, task, steps, sampler, cfg_scale, seed, width, height], 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()