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import gradio as gr
import requests
import io
import random
import os
from PIL import Image
List of available models
list_models = [
"SDXL-1.0",
"SD-1.5",
"OpenJourney-V4",
"Anything-V4",
"Disney-Pixar-Cartoon",
"Pixel-Art-XL",
"Dalle-3-XL",
"Midjourney-V4-XL",
]
Function to generate image from text prompt using selected model and style
def generate_txt2img(current_model, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7,
seed=None):
# API URLs for different models
if current_model == "SD-1.5":
API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
elif current_model == "SDXL-1.0":
API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
elif current_model == "OpenJourney-V4":
API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney"
elif current_model == "Anything-V4":
API_URL = "https://api-inference.huggingface.co/models/xyn-ai/anything-v4.0"
elif current_model == "Disney-Pixar-Cartoon":
API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/disney-pixar-cartoon"
elif current_model == "Pixel-Art-XL":
API_URL = "https://api-inference.huggingface.co/models/nerijs/pixel-art-xl"
elif current_model == "Dalle-3-XL":
API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
elif current_model == "Midjourney-V4-XL":
API_URL = "https://api-inference.huggingface.co/models/openskyml/midjourney-v4-xl"
API_TOKEN = os.environ.get("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
# Construct payload based on selected style and options
if image_style == "None style":
payload = {
"inputs": prompt + ", 8k",
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Cinematic":
payload = {
"inputs": prompt + ", realistic, detailed, textured, skin, hair, eyes, by Alex Huguet, Mike Hill, Ian Spriggs, JaeCheol Park, Marek Denko",
"is_negative": is_negative + ", abstract, cartoon, stylized",
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Digital Art":
payload = {
"inputs": prompt + ", faded , vintage , nostalgic , by Jose Villa , Elizabeth Messina , Ryan Brenizer , Jonas Peterson , Jasmine Star",
"is_negative": is_negative + ", sharp , modern , bright",
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
elif image_style == "Portrait":
payload = {
"inputs": prompt + ", soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)",
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed is not None else random.randint(-1, 2147483647)
}
# Send request to API and retrieve image
image_bytes = requests.post(API_URL, headers=headers, json=payload).content
image = Image.open(io.BytesIO(image_bytes))
return image
CSS styles for the app
css = """
/* General Container Styles */
.gradio-container {
max-width: 800px !important;
margin: auto;
padding-top: 1.5rem;
}
/* Button Styles */
.gr-button {
color: white;
border-color: black;
background: black;
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
/* Footer Styles */
.footer, .dark .footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer > p, .dark .footer > p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer > p {
background: #0b0f19;
}
/* Share Button Styles */
#share-btn-container {
padding: 0 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
max-width: 13rem;
margin-left: auto;
}
#share-btn-container:hover {
background-color: #060606;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor: pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding: 0.5rem !important;
right: 0;
}
/* Animation Styles */
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from { transform: rotate(0deg); }
to { transform: rotate(360deg); }
}
/* Other Styles */
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
"""
Create the app interface
with gr.Interface(generate_txt2img,
title="AI Diffusion",
description="Generate images from text prompts using different AI models and styles",
examples=[
["a cute dog"],
["a beautiful sunset"],
["a fantasy world"]
],
layout="vertical",
css=css) as demo:
# Add model selection dropdown
demo.inputs[0].label = "Current Model"
# Add prompt text input and generate button
demo.inputs[1].label = "Prompt"
demo.inputs[1].placeholder = "Enter your text prompt here"
demo.inputs[1].lines = 1
demo.inputs[2].visibility = "hidden" # Hide negative prompt textbox initially
@demo.inputs[3].callback
def style_selected(value):
# Show or hide negative prompt textbox based on the selected style
if value == "None style":
demo.inputs[2].visibility = "hidden"
else:
demo.inputs[2].visibility = "visible"
# Add image output
demo.outputs[0].label = "Generated Image"
# Run the app
demo.launch() |