app.py

import spaces
import gradio as gr
import re
from PIL import Image
import os
import numpy as np
import torch
from diffusers import FluxImg2ImgPipeline

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device)

def sanitize_prompt(prompt):
    allowed_chars = re.compile(r"[^a-zA-Z0-9\s.,!?-]")
    sanitized_prompt = allowed_chars.sub("", prompt)
    return sanitized_prompt

def convert_to_fit_size(original_width_and_height, maximum_size=2048):
    width, height = original_width_and_height
    if width <= maximum_size and height <= maximum_size:
        return width, height
    scaling_factor = maximum_size / max(width, height)
    new_width = int(width * scaling_factor)
    new_height = int(height * scaling_factor)
    return new_width, new_height

def adjust_to_multiple_of_32(width: int, height: int):
    width = width - (width % 32)
    height = height - (height % 32)
    return width, height

@spaces.GPU(duration=120)
def process_images(image, prompt="a girl", strength=0.75, seed=0, inference_step=4, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting")
    def process_img2img(image, prompt="a person", strength=0.75, seed=0, num_inference_steps=4):
        if image is None:
            return None
        generator = torch.Generator(device).manual_seed(seed)
        width, height = convert_to_fit_size(image.size)
        width, height = adjust_to_multiple_of_32(width, height)
        image = image.resize((width, height), Image.LANCZOS)
        output = pipe(prompt=prompt, image=image, generator=generator, strength=strength, width=width, height=height, guidance_scale=0, num_inference_steps=num_inference_steps, max_sequence_length=256)
        return output.images[0]
    output = process_img2img(image, prompt, strength, seed, inference_step)
    return output

def read_file(path: str) -> str:
    with open(path, 'r', encoding='utf-8') as f:
        content = f.read()
    return content

css = """
#demo-container {
    border: 4px solid black;
    border-radius: 8px;
    padding: 20px;
    margin: 20px auto;
    max-width: 800px;
}

#image_upload, #output-img {
    border: 4px solid black;
    border-radius: 8px;
    width: 256px;
    height: 256px;
    object-fit: cover;
}

#run_button {
    font-weight: bold;
    border: 4px solid black;
    border-radius: 8px;
    padding: 10px 20px;
    width: 100%
}

#col-left, #col-right {
    max-width: 640px;
    margin: 0 auto;
}
.grid-container {
    display: flex;
    align-items: center;
    justify-content: center;
    gap: 10px;
}
.text {
    font-size: 16px;
}
"""

with gr.Blocks(css=css, elem_id="demo-container") as demo:
    with gr.Column():
        gr.HTML(read_file("demo_header.html"))
        # Removed or commented out the demo_tools.html line
        # gr.HTML(read_file("demo_tools.html"))
    with gr.Row():
        with gr.Column():
            image = gr.Image(width=256, height=256, sources=['upload', 'clipboard'], image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
            prompt = gr.Textbox(label="Prompt", value="", placeholder="Your prompt", elem_id="prompt")
            btn = gr.Button("Generate", elem_id="run_button", variant="primary")
            with gr.Accordion(label="Advanced Settings", open=False):
                strength = gr.Number(value=0.75, minimum=0, maximum=0.75, step=0.01, label="Strength")
                seed = gr.Number(value=100, minimum=0, step=1, label="Seed")
                inference_step = gr.Number(value=4, minimum=1, step=4, label="Inference Steps")
        with gr.Column():
            image_out = gr.Image(width=256, height=256, label="Output", elem_id="output-img", format="jpg")

    gr.HTML(gr.HTML(read_file("demo_footer.html")))
    
    gr.on(
        triggers=[btn.click, prompt.submit],
        fn=process_images,
        inputs=[image, prompt, strength, seed, inference_step],
        outputs=[image_out]
    )

if __name__ == "__main__":
    demo.queue().launch()