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):
  # Allow only alphanumeric characters, spaces, and basic punctuation
  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
    
    if width > height:
        scaling_factor = maximum_size / width
    else:
        scaling_factor = maximum_size / 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)):
    #print("start process_images")
    progress(0, desc="Starting")


    def process_img2img(image,prompt="a person",strength=0.75,seed=0,num_inference_steps=4):
        #print("start  process_img2img")
        if image == None:
            print("empty input image returned")
            return None

        generators = []
        generator = torch.Generator(device).manual_seed(seed)
        generators.append(generator)
        fit_width,fit_height = convert_to_fit_size(image.size)
        #print(f"fit {width}x{height}")
        width,height = adjust_to_multiple_of_32(fit_width,fit_height)
        #print(f"multiple {width}x{height}")
        image = image.resize((width, height), Image.LANCZOS)
        #mask_image = mask_image.resize((width, height), Image.NEAREST)

        # more parameter see https://huggingface.co/docs/diffusers/api/pipelines/flux#diffusers.FluxInpaintPipeline
        #print(prompt)
        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)

        pil_image = Image.fromarray(output.images[0])
        new_width,new_height = pil_image.size

        # resize back multiple of 32
        if (new_width!=fit_width) or (new_height!=fit_height):
            resized_image= pil_image.resize(fit_width,fit_height,Image.LANCZOS)
            return np.array(resized_image)
        
        return np.array(pil_image)

    output = process_img2img(image,prompt,strength,seed,inference_step)
   
    #print("end process_images")
    return output
    

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

    return content


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

.image {
  width: 128px; 
  height: 128px; 
  object-fit: cover; 
}

.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"))
        gr.HTML(read_file("demo_tools.html"))
    with gr.Row():
                with gr.Column():
                    image = gr.Image(height=800,sources=['upload','clipboard'],image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
                    with gr.Row(elem_id="prompt-container",  equal_height=False):
                        with gr.Row():
                            prompt = gr.Textbox(label="Prompt",value="a women",placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt")
                            
                    btn = gr.Button("Img2Img", elem_id="run_button",variant="primary")
                    
                    with gr.Accordion(label="Advanced Settings", open=False):
                        with gr.Row( equal_height=True):
                            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_step")
                        id_input=gr.Text(label="Name", visible=False)
                            
                with gr.Column():
                    image_out = gr.Image(height=800,sources=[],label="Output", elem_id="output-img",format="jpg")
                   

                    
            

    gr.Examples(
               examples=[
                    ["examples/draw_input.jpg", "examples/draw_output.jpg","a women ,eyes closed,mouth opened"],
                    ["examples/draw-gimp_input.jpg", "examples/draw-gimp_output.jpg","a women ,eyes closed,mouth opened"],
                    ["examples/gimp_input.jpg", "examples/gimp_output.jpg","a women ,hand on neck"],
                    ["examples/inpaint_input.jpg", "examples/inpaint_output.jpg","a women ,hand on neck"]
                         ]
,
                inputs=[image,image_out,prompt],
    )
    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.launch()