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import gradio as gr | |
import requests | |
import io | |
import random | |
import os | |
import time | |
from PIL import Image | |
from deep_translator import GoogleTranslator | |
import json | |
# Project by Nymbo | |
API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell" | |
API_TOKEN = os.getenv("HF_READ_TOKEN") | |
headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
timeout = 100 | |
# Function to query the API and return the generated image | |
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024): | |
if prompt == "" or prompt is None: | |
return None | |
key = random.randint(0, 999) | |
headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
# Translate the prompt from Russian to English if necessary | |
prompt = GoogleTranslator(source='ru', target='en').translate(prompt) | |
print(f'\033[1mGeneration {key} translation:\033[0m {prompt}') | |
# Add some extra flair to the prompt | |
prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." | |
print(f'\033[1mGeneration {key}:\033[0m {prompt}') | |
# Prepare the payload for the API call, including width and height | |
payload = { | |
"inputs": prompt, | |
"is_negative": is_negative, | |
"steps": steps, | |
"cfg_scale": cfg_scale, | |
"seed": seed if seed != -1 else random.randint(1, 1000000000), | |
"strength": strength, | |
"parameters": { | |
"width": width, # Pass the width to the API | |
"height": height # Pass the height to the API | |
} | |
} | |
# Send the request to the API and handle the response | |
response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout) | |
if response.status_code != 200: | |
print(f"Error: Failed to get image. Response status: {response.status_code}") | |
print(f"Response content: {response.text}") | |
if response.status_code == 503: | |
raise gr.Error(f"{response.status_code} : The model is being loaded") | |
raise gr.Error(f"{response.status_code}") | |
try: | |
# Convert the response content into an image | |
image_bytes = response.content | |
image = Image.open(io.BytesIO(image_bytes)) | |
print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})') | |
return image | |
except Exception as e: | |
print(f"Error when trying to open the image: {e}") | |
return None | |
# CSS to style the app | |
css = """ | |
body { | |
background-color: #000; | |
margin: 0; | |
height: 100vh; | |
display: flex; | |
justify-content: center; | |
align-items: center; | |
} | |
#neon-square { | |
width: 300px; | |
height: 300px; | |
position: relative; | |
} | |
#neon-square::before { | |
content: ''; | |
position: absolute; | |
top: -20px; | |
left: -20px; | |
right: -20px; | |
bottom: -20px; | |
z-index: -1; | |
filter: blur(20px); | |
} | |
#app { | |
max-width: 1400px; | |
position: relative; /* Allow positioning for neon effect */ | |
} | |
.neon-cursor { | |
position: absolute; | |
top: 0; | |
left: 0; | |
width: 100%; | |
height: 100%; | |
pointer-events: none; /* Allow clicks through the neon effect */ | |
} | |
#app-container { | |
max-width: 1400px; | |
margin-left: auto; | |
margin-right: auto; | |
overflow: hidden; | |
touch-action: pan-up; | |
color: #ff000f; | |
font-family: 'Montserrat', sans-serif; | |
text-align: center; | |
text-shadow: 0 0 5px #1121, 0 0 20px #000, 0 0 30px #000; | |
} | |
.gradio-container { | |
background: url(https://huggingface.co/spaces/K00B404/FLUX.1-Dev-Serverless-darn-enhanced-prompt/resolve/main/edge.png); | |
background-size: 900px 880px; | |
background-repeat: no-repeat; | |
background-position: center; | |
color:#000; | |
} | |
.dark\:bg-gray-950:is(.dark *) { | |
--tw-bg-opacity: 1; | |
background-color: rgb(157, 17, 142); | |
} | |
.gradio-container-4-41-0 .prose :last-child { | |
margin-top: 8px !important; | |
} | |
.gradio-container-4-41-0 .prose :last-child { | |
margin-bottom: -7px !important; | |
} | |
.dark { | |
--button-primary-background-fill: #09e60d70; | |
--button-primary-background-fill-hover: #00000070; | |
--background-fill-primary: #000; | |
--background-fill-secondary: #000; | |
} | |
.hide-container { | |
margin-top;-2px; | |
} | |
#app-container3 { | |
background-color: rgba(255, 255, 255, 0.001); /* Corrected to make semi-transparent */ | |
max-width: 600px; | |
margin-left: auto; | |
margin-right: auto; | |
margin-bottom: 10px; | |
border-radius: 125px; | |
box-shadow: 0 0 10px rgba(0,0,0,0.1); /* Adjusted shadow opacity */ | |
} | |
#app-container { | |
background-color: rgba(255, 255, 255, 0.001); /* Semi-transparent background */ | |
max-width: 600px; | |
margin: 0 auto; /* Center horizontally */ | |
padding-bottom: 10px; | |
border-radius: 25px; | |
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); /* Adjusted shadow opacity */ | |
} | |
#title-container { | |
display: flex; | |
align-items: center | |
margin-bottom:10px; | |
justify-content: center; | |
} | |
#title-icon { | |
width: 32px; | |
height: auto; | |
margin-right: 10px; | |
} | |
#title-text { | |
font-size: 30px; | |
font-weight: bold; | |
color: #000; | |
} | |
body, html, #app { | |
margin: 0; | |
width: 100%; | |
height: 100%; | |
} | |
#app { | |
overflow: hidden; | |
touch-action: pan-up; | |
color: #ff000f; | |
font-family: 'Montserrat', sans-serif; | |
text-align: center; | |
text-shadow: 0 0 5px #1121, 0 0 20px #000, 0 0 30px #000; | |
} | |
#app h1 { | |
--fontSize: 60px; | |
--lineHeight: 80px; | |
width: auto; | |
height: calc(2 * var(--lineHeight)); | |
line-height: var(--lineHeight); | |
margin: calc(50vh - var(--lineHeight)) auto 0; | |
font-size: var(--fontSize); | |
text-transform: uppercase; | |
} | |
#app a { | |
margin-top: 10px; | |
display: inline-block; | |
text-decoration: none; | |
color: #fff; | |
} | |
#app canvas { | |
display: block; | |
position: fixed; | |
z-index: -1; | |
top: 0; | |
} | |
""" | |
js2='''const neonSquare = document.getElementById('neon-square'); | |
let time = 0; | |
function hslToRgb(h, s, l) { | |
let r, g, b; | |
if (s === 0) { | |
r = g = b = l; | |
} else { | |
const hue2rgb = (p, q, t) => { | |
if (t < 0) t += 1; | |
if (t > 1) t -= 1; | |
if (t < 1/6) return p + (q - p) * 6 * t; | |
if (t < 1/2) return q; | |
if (t < 2/3) return p + (q - p) * (2/3 - t) * 6; | |
return p; | |
}; | |
const q = l < 0.5 ? l * (1 + s) : l + s - l * s; | |
const p = 2 * l - q; | |
r = hue2rgb(p, q, h + 1/3); | |
g = hue2rgb(p, q, h); | |
b = hue2rgb(p, q, h - 1/3); | |
} | |
return [Math.round(r * 255), Math.round(g * 255), Math.round(b * 255)]; | |
} | |
function updateColors() { | |
const noise1 = noise.simplex2(time * 0.5, 0) * 0.5 + 0.5; | |
const noise2 = noise.simplex2(time * 0.5, 1000) * 0.5 + 0.5; | |
const noise3 = noise.simplex2(time * 0.5, 2000) * 0.5 + 0.5; | |
const noise4 = noise.simplex2(time * 0.5, 3000) * 0.5 + 0.5; | |
const [r1, g1, b1] = hslToRgb(noise1, 1, 0.5); | |
const [r2, g2, b2] = hslToRgb(noise2, 1, 0.5); | |
const [r3, g3, b3] = hslToRgb(noise3, 1, 0.5); | |
const [r4, g4, b4] = hslToRgb(noise4, 1, 0.5); | |
const gradient = `linear-gradient(45deg, | |
rgb(${r1},${g1},${b1}), | |
rgb(${r2},${g2},${b2}), | |
rgb(${r3},${g3},${b3}), | |
rgb(${r4},${g4},${b4}))`; | |
neonSquare.style.background = gradient; | |
neonSquare.style.boxShadow = ` | |
0 0 20px rgb(${r1},${g1},${b1}), | |
0 0 40px rgb(${r2},${g2},${b2}), | |
0 0 60px rgb(${r3},${g3},${b3}), | |
0 0 80px rgb(${r4},${g4},${b4}) | |
`; | |
neonSquare.style.setProperty('--glow', gradient); | |
time += 0.01; | |
} | |
// Update colors every frame | |
function animate() { | |
updateColors(); | |
requestAnimationFrame(animate); | |
} | |
animate();''' | |
js_script=""" | |
import { neonCursor } from 'https://unpkg.com/[email protected]/build/threejs-toys.module.cdn.min.js'; | |
let isNeonActive = false; // Toggle state for neon effect | |
let isFixedPathMode = false; // Toggle between edge-following and fixed path modes | |
let neonAnimationId = null; // To store the requestAnimationFrame ID | |
function moveNeonCursorAlongEdges() { | |
const panel = document.getElementById('app'); | |
const rect = panel.getBoundingClientRect(); | |
const { top, left, right, bottom, width, height } = rect; | |
let x, y; | |
let time = performance.now() / 1000; // Time for smooth movement along edges | |
let edgePosition = (time * 100) % (2 * width + 2 * height); // Total perimeter of the panel | |
if (edgePosition < width) { // Top edge | |
x = left + edgePosition; | |
y = top; | |
} else if (edgePosition < width + height) { // Right edge | |
x = right; | |
y = top + (edgePosition - width); | |
} else if (edgePosition < 2 * width + height) { // Bottom edge | |
x = right - (edgePosition - width - height); | |
y = bottom; | |
} else { // Left edge | |
x = left; | |
y = bottom - (edgePosition - 2 * width - height); | |
} | |
neonCursor({ | |
el: document.getElementById('neon-cursor'), | |
shaderPoints: 16, | |
curvePoints: 80, | |
curveLerp: 0.5, | |
radius1: 45, | |
radius2: 30, | |
velocityTreshold: 10, | |
sleepRadiusX: 100, | |
sleepRadiusY: 100, | |
sleepTimeCoefX: 0.0025, | |
sleepTimeCoefY: 0.0025, | |
x, y // Position the neon cursor at the calculated x, y coordinates | |
}); | |
if (isNeonActive && !isFixedPathMode) { | |
neonAnimationId = requestAnimationFrame(moveNeonCursorAlongEdges); | |
} | |
} | |
function toggleNeonEffect(event) { | |
if (event.button === 2) { // Right-click | |
isNeonActive = !isNeonActive; | |
if (isNeonActive) { | |
isFixedPathMode = !isFixedPathMode; | |
if (isFixedPathMode) { | |
moveNeonCursorInFixedPath(); // Start fixed path mode | |
} else { | |
moveNeonCursorAlongEdges(); // Start edge-following mode | |
} | |
} else { | |
cancelAnimationFrame(neonAnimationId); // Stop the neon effect | |
} | |
} | |
} | |
window.addEventListener('contextmenu', (event) => { | |
event.preventDefault(); | |
toggleNeonEffect(event); | |
}); | |
window.addEventListener('resize', () => { | |
if (isNeonActive) { | |
cancelAnimationFrame(neonAnimationId); | |
moveNeonCursorAlongEdges(); // Restart edge-following mode | |
} | |
}); | |
""" | |
title="""<center><h1>FluxCapacitor</h1></center>""" | |
# Build the Gradio UI with Blocks | |
with gr.Blocks( css=css, js=js2) as app: #theme='Nymbo/Nymbo_Theme', | |
# Add a title to the app | |
gr.HTML(title) | |
# Add the neon cursor container | |
with gr.Row(): | |
gr.HTML('<div id="neon-cursor" class="neon-cursor"></div>') | |
# Container for all the UI elements | |
with gr.Column(elem_id="app-container"): | |
# Add a text input for the main prompt | |
with gr.Row(): | |
with gr.Column(elem_id="prompt-container"): | |
with gr.Row(): | |
text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input") | |
# Accordion for advanced settings | |
with gr.Row(): | |
with gr.Accordion("Advanced Settings", open=False): | |
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input") | |
with gr.Row(): | |
width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32) | |
height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32) | |
steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1) | |
cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1) | |
strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001) | |
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random | |
method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]) | |
# Add a button to trigger the image generation | |
with gr.Row(): | |
text_button = gr.Button("Run", variant='primary', elem_id="gen-button") | |
# Image output area to display the generated image | |
with gr.Row(): | |
image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") | |
# Bind the button to the query function with the added width and height inputs | |
text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output) | |
# Launch the Gradio app | |
app.launch(show_api=False, share=False) |