app.py

import spaces
import gradio as gr
import torch
from diffusers import FluxPipeline
from huggingface_hub import hf_hub_download
from PIL import Image
import requests
from translatepy import Translator

translator = Translator()

# Constants
model = "Shakker-Labs/AWPortrait-FL"

CSS = """
.gradio-container {
  max-width: 690px !important;
}
footer {
    visibility: hidden;
}
"""

JS = """function () {
  gradioURL = window.location.href
  if (!gradioURL.endsWith('?__theme=dark')) {
    window.location.replace(gradioURL + '?__theme=dark');
  }
}"""


# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
    pipe = FluxPipeline.from_pretrained(model, torch_dtype=torch.bfloat16)
    pipe.to("cuda")



# Function 
@spaces.GPU()
def generate_image(
    prompt,
    negative="low quality",
    width=768,
    height=1024,
    scale=3.5,
    steps=24):
    
    prompt = str(translator.translate(prompt, 'English'))
    negative_prompt = str(translator.translate(negative, 'English'))

    print(f'prompt:{prompt}')
    
    image = pipe(
        prompt, 
        negative_prompt=negative, 
        width=width,
        height=height,
        guidance_scale=scale,
        num_inference_steps=steps,
    )
    print(image.images[0])
    return image.images[0]


examples = [
    "close up portrait, Amidst the interplay of light and shadows in a photography studio,a soft spotlight traces the contours of a face,highlighting a figure clad in a sleek black turtleneck. The garment,hugging the skin with subtle luxury,complements the Caucasian model's understated makeup,embodying minimalist elegance. Behind,a pale gray backdrop extends,its fine texture shimmering subtly in the dim light,artfully balancing the composition and focusing attention on the subject. In a palette of black,gray,and skin tones,simplicity intertwines with profundity,as every detail whispers untold stories.",
    "upper body portrait of 1girl wear a black color turtleneck sweater,A proud and confident expression,long hair,look at viewers,studio fashion portrait,studio light,pure white background",
    "upper body portrait of 1girl wear (red color turtleneck sweater:1),A proud and confident smile expression,long hair,look at viewers,studio fashion portrait,studio light,pure white background",
    "upper body portrait of 1girl wear suit with tie,A proud and confident smile expression,long hair,look at viewers,studio fashion portrait,studio light,pure white background"
]


# Gradio Interface

with gr.Blocks(css=CSS, js=JS, theme="soft") as demo:
    gr.HTML("<h1><center>Flux</center></h1>")
    gr.HTML("<p><center><a href='https://huggingface.co/Shakker-Labs/AWPortrait-FL'>Shakker-Labs/AWPortrait-FL</a></center></p>")
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Enter Your Prompt(multilingual)', scale=6)
            submit = gr.Button(scale=1, variant='primary')
    img = gr.Image(label='Flux Generated Image')
    with gr.Accordion("Advanced Options", open=False):
        with gr.Row():
            negative = gr.Textbox(label="Negative prompt", value="low quality")
        with gr.Row():
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=1280,
                step=8,
                value=768,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=1280,
                step=8,
                value=1024,
            )
        with gr.Row():
            scale = gr.Slider(
                label="Guidance Scale",
                minimum=0,
                maximum=50,
                step=0.1,
                value=3.5,
            )
            steps = gr.Slider(
                label="Steps",
                minimum=1,
                maximum=50,
                step=1,
                value=24,
            )            
    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=img,
        fn=generate_image,
        cache_examples="lazy",
    )

    prompt.submit(fn=generate_image,
                 inputs=[prompt, negative, width, height, scale, steps],
                 outputs=img,
                 )
    submit.click(fn=generate_image,
                 inputs=[prompt, negative, width, height, scale, steps],
                 outputs=img,
                 )
    
demo.queue().launch()