app.py

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

from datetime import datetime
from fastapi import FastAPI

app = FastAPI()

#----------Start of theme----------
theme = gr.themes.Ocean(
    primary_hue="zinc",
    secondary_hue="slate",
    neutral_hue="neutral",
    font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'],
    font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'],
).set(
    #Body Settings
    body_background_fill='linear-gradient(10deg, *primary_200, *secondary_50)',
    body_text_color='secondary_600',
    body_text_color_subdued='*primary_500',
    body_text_weight='500',

    #Background Settings
    background_fill_primary='*primary_100',
    background_fill_secondary='*secondary_200',
    
    color_accent='*primary_300',

    #Border Settings
    border_color_accent_subdued='*primary_400',
    border_color_primary='*primary_400',
    
    #Block Settings
    block_radius='*radius_md',
    block_background_fill='*primary_200',
    block_border_color='*primary_500',
    block_border_width='*panel_border_width',
    block_info_text_color='*primary_700',
    block_info_text_size='*text_md',
    
    container_radius='*radius_xl',
    panel_background_fill='*primary_200',
    accordion_text_color='*primary_600',
    checkbox_border_radius='*radius_xl',
    slider_color='*primary_500',
    table_text_color='*primary_600',
    input_background_fill='*primary_50',
    input_background_fill_focus='*primary_100',

    #Button Settings
    button_border_width='1px',
    button_transform_hover='scale(1.01)',
    button_transition='all 0.1s ease-in-out',
    button_transform_active='Scale(0.9)',
    button_large_radius='*radius_xl',
    button_medium_radius='*radius_xl',
    button_small_radius='*radius_xl',
    button_primary_border_color='*primary_500',
    button_secondary_border_color='*primary_400',
    button_primary_background_fill_hover='linear-gradient(90deg, *primary_400, *secondary_200, *primary_400)',
    button_primary_background_fill='linear-gradient(90deg,*secondary_300 , *primary_500, *secondary_300)',
    button_primary_text_color='*primary_100',
    button_primary_text_color_hover='*primary_700',
    button_cancel_background_fill='*primary_500',
    button_cancel_background_fill_hover='*primary_400'
)
#----------End of theme----------

# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3.5-large"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

# Function to clear input and output
def clear():
    return None 

# 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=896, height=1152):
    if prompt == "" or prompt is None:
        return None

    key = random.randint(0, 999)
    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    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

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 = """
#app-container {
    max-width: 930px;
    margin-left: auto;
    margin-right: auto;
    background-image: url("https://drive.google.com/file/d/1Kz2pi93EfsEHw90fil6XJBoSq9f-BlkJ"); repeat 0 0;}')
}
".gradio-container {background: url('file/abstract.png')"
   
"""

# Build the Gradio UI with Blocks
with gr.Blocks(theme=theme, css=css) as app:
    # Add a title to the app
    gr.HTML("<center><h1>🎨 Stable Diffusion 3.5 🇬🇧</h1></center>")
    
    # 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="Image Prompt", placeholder="Enter a prompt here", lines=2, show_copy_button = True, 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="((visible hand:1.3), (ugly:1.3), (duplicate:1.2), (morbid:1.1), (mutilated:1.1), out of frame, bad face, extra fingers, mutated hands, (poorly drawn hands:1.1), (poorly drawn face:1.3), (mutation:1.3), (deformed:1.3), blurry, (bad anatomy:1.1), (bad proportions:1.2), (extra limbs:1.1), cloned face, (disfigured:1.2), gross proportions, malformed limbs, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), fused fingers, too many fingers, (long neck:1.2), sketched by bad-artist, (bad-image-v2-39000:1.3)", lines=5, elem_id="negative-prompt-text-input")
                        with gr.Row():
                            width = gr.Slider(label="ImageWidth", value=896, minimum=64, maximum=1216, step=32)
                            height = gr.Slider(label="Image Height", value=1152, minimum=64, maximum=1216, step=32)
                        steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
                        cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=1)
                        strength = gr.Slider(label="PromptStrength", value=100, minimum=0, maximum=100, step=1)
                        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", "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", "PLMS", "UniPC", "UniPC BH2"])

        # Add a button to trigger the image generation
        with gr.Row():
            text_button = gr.Button("Generate Image", variant='primary', elem_id="gen-button")
            clr_button =gr.Button("Clear Prompt",variant="primary", elem_id="clear_button")
            clr_button.click(lambda: gr.Textbox(value=""), None, text_prompt)
            
        # Image output area to display the generated image
        with gr.Row():
            image_output1 = gr.Image(type="pil", label="Image Output 1", format="png", elem_id="gallery")
            image_output2 = gr.Image(type="pil", label="Image Output 2", format="png", elem_id="gallery")			
            
        with gr.Row():
            clear_btn = gr.Button(value="Clear Image", variant="primary", elem_id="clear_button")
            clear_btn.click(clear, inputs=[], outputs=[image_output])
            
        gr.Examples(
            examples = examples,    
            inputs = [text_prompt],    
        )    
            
        # 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_output1, image_output2])

        if __name__ == "__main__":
            
            # Launch the Gradio app
            app.launch(show_api=False, share=False)