File size: 5,750 Bytes
63e20c3 2fe7cec 63e20c3 2fe7cec 63e20c3 1e87ec4 63e20c3 2fe7cec 63e20c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import gradio as gr
import os
import sys
import random
import string
import time
from queue import Queue
from threading import Thread
import requests
import io
from PIL import Image
app = FastAPI()
API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic"
API_TOKEN = os.getenv("HF_READ_TOKEN") # it is free
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def generate_image(prompt):
payload = {"inputs": prompt}
response = requests.post(API_URL, headers=headers, json=payload)
image_bytes = response.content
image = Image.open(io.BytesIO(image_bytes))
return image
text_gen = gr.Interface.load("models/Gustavosta/MagicPrompt-Stable-Diffusion")
proc1 = gr.Interface.load("models/playgroundai/playground-v2-1024px-aesthetic")
queue = Queue()
queue_threshold = 100
def add_random_noise(prompt, noise_level=0.00):
if noise_level == 0:
noise_level = 0.00
percentage_noise = noise_level * 5
num_noise_chars = int(len(prompt) * (percentage_noise / 100))
noise_indices = random.sample(range(len(prompt)), num_noise_chars)
prompt_list = list(prompt)
noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
noise_chars.extend(['๐', '๐ฉ', '๐', '๐ค', '๐', '๐ค', '๐ญ', '๐', '๐ท', '๐คฏ', '๐คซ', '๐ฅด', '๐ด', '๐คฉ', '๐ฅณ', '๐', '๐ฉ', '๐คช', '๐', '๐คข', '๐', '๐น', '๐ป', '๐ค', '๐ฝ', '๐', '๐', '๐
', '๐', '๐', '๐', '๐', '๐', '๐', '๐ฎ', 'โค๏ธ', '๐', '๐', '๐', '๐', '๐ถ', '๐ฑ', '๐ญ', '๐น', '๐ฆ', '๐ป', '๐จ', '๐ฏ', '๐ฆ', '๐', '๐ฅ', '๐ง๏ธ', '๐', '๐', '๐ฅ', '๐ด', '๐', '๐บ', '๐ป', '๐ธ', '๐จ', '๐
', '๐', 'โ๏ธ', 'โ๏ธ', 'โ๏ธ', 'โ๏ธ', '๐ค๏ธ', 'โ
๏ธ', '๐ฅ๏ธ', '๐ฆ๏ธ', '๐ง๏ธ', '๐ฉ๏ธ', '๐จ๏ธ', '๐ซ๏ธ', 'โ๏ธ', '๐ฌ๏ธ', '๐จ', '๐ช๏ธ', '๐'])
for index in noise_indices:
prompt_list[index] = random.choice(noise_chars)
return "".join(prompt_list)
# Existing code...
import uuid # Import the UUID library
# Existing code...
# Existing code...
request_counter = 0 # Global counter to track requests
def send_it1(inputs, noise_level, proc=proc1):
global request_counter
request_counter += 1
timestamp = f"{time.time()}_{request_counter}"
prompt_with_noise = add_random_noise(inputs, noise_level) + f" - {timestamp}"
while queue.qsize() >= queue_threshold:
time.sleep(2)
queue.put(prompt_with_noise)
output = proc(prompt_with_noise)
return output
def get_prompts(prompt_text):
if not prompt_text:
return "Please enter text before generating prompts.ุฑุฌุงุก ุงุฏุฎู ุงููุต ุงููุง"
raise gr.Error("Please enter text before generating prompts.ุฑุฌุงุก ุงุฏุฎู ุงููุต ุงููุง")
else:
global request_counter
request_counter += 1
timestamp = f"{time.time()}_{request_counter}"
options = [
"Cyberpunk android",
"2060",
"newyork",
"style of laurie greasley" , "studio ghibli" , "akira toriyama" , "james gilleard" , "genshin impact" , "trending pixiv fanbox" , "acrylic palette knife, 4k, vibrant colors, devinart, trending on artstation, low details"
"Editorial Photography, Shot on 70mm lens, Depth of Field, Bokeh, DOF, Tilt Blur, Shutter Speed 1/1000, F/22, 32k, Super-Resolution, award winning,",
"high detail, warm lighting, godrays, vivid, beautiful, trending on artstation, by jordan grimmer, huge scene, grass, art greg rutkowski ",
"highly detailed, digital painting, artstation, illustration, art by artgerm and greg rutkowski and alphonse mucha.",
"Charlie Bowater, stanley artgerm lau, a character portrait, sots art, sharp focus, smooth, aesthetic, extremely detailed, octane render,solo, dark industrial background, rtx, rock clothes, cinematic light, intricate detail, highly detailed, high res, detailed facial features",
"portrait photograph" , "realistic" , "concept art" , "elegant, highly detailed" , "intricate, sharp focus, depth of field, f/1. 8, 85mm, medium shot, mid shot, (((professionally color graded)))" ," sharp focus, bright soft diffused light" , "(volumetric fog),",
"Cinematic film still" ," (dark city street:1.2)" , "(cold colors), damp, moist, intricate details" ,"shallow depth of field, [volumetric fog]" , "cinematic lighting, reflections, photographed on a Canon EOS R5, 50mm lens, F/2.8, HDR, 8k resolution" , "cinematic film still from cyberpunk movie" , "volumetric fog, (RAW, analog, masterpiece, best quality, soft particles, 8k, flawless perfect face, intricate details" , "trending on artstation, trending on cgsociety, dlsr, ultra sharp, hdr, rtx, antialiasing, canon 5d foto))" , "((skin details, high detailed skin texture))" , "(((perfect face))), (perfect eyes)))",
# Add other prompt options here...
]
if prompt_text:
chosen_option = random.choice(options)
return text_gen(f"{prompt_text}, {chosen_option} - {timestamp}")
else:
return text_gen("", timestamp)
@app.get("/generate_prompts")
def generate_prompts(prompt_text: str):
return get_prompts(prompt_text)
@app.get("/send_inputs")
def send_inputs(inputs: str, noise_level: float):
generated_image = generate_image(inputs)
return generated_image
app.mount("/", StaticFiles(directory="static", html=True), name="static")
@app.get("/")
def index() -> FileResponse:
return FileResponse(path="/app/static/index.html", media_type="text/html")
|