app.py

from typing import Tuple

import requests
import random
import numpy as np
import gradio as gr
import spaces
import torch
from PIL import Image
from diffusers import FluxInpaintPipeline
from diffusers import FluxImg2ImgPipeline

MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"


def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
    image = image.convert("RGBA")
    data = image.getdata()
    new_data = []
    for item in data:
        avg = sum(item[:3]) / 3
        if avg < threshold:
            new_data.append((0, 0, 0, 0))
        else:
            new_data.append(item)

    image.putdata(new_data)
    return image


#pipe = FluxInpaintPipeline.from_pretrained(
   # "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
pipe2 = FluxImg2ImgPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)

def resize_image_dimensions(
    original_resolution_wh: Tuple[int, int],
    maximum_dimension: int = IMAGE_SIZE
) -> Tuple[int, int]:
    width, height = original_resolution_wh

    # if width <= maximum_dimension and height <= maximum_dimension:
    #     width = width - (width % 32)
    #     height = height - (height % 32)
    #     return width, height

    if width > height:
        scaling_factor = maximum_dimension / width
    else:
        scaling_factor = maximum_dimension / height

    new_width = int(width * scaling_factor)
    new_height = int(height * scaling_factor)

    new_width = new_width - (new_width % 32)
    new_height = new_height - (new_height % 32)

    return new_width, new_height


@spaces.GPU(duration=80)
def process(
    input_image_editor: dict,
    input_text: str,
    seed_slicer: int,
    randomize_seed_checkbox: bool,
    strength_slider: float,
    num_inference_steps_slider: int,
    progress=gr.Progress(track_tqdm=True)
):
    if not input_text:
        gr.Info("Please enter a text prompt.")
        return None, None, None, None
    input_text = "A military COR3 "+input_text
    image = input_image_editor['background']
    mask = input_image_editor['layers'][0]

    if not image:
        gr.Info("Please upload an image.")
        return None, None, None, None
        
    width, height = resize_image_dimensions(original_resolution_wh=image.size)
    resized_image = image.resize((width, height), Image.LANCZOS)
    if randomize_seed_checkbox:
       seed_slicer = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed_slicer)
 
    # if not mask:
       # gr.Info("Please draw a mask on the image.")
    pipe2.load_lora_weights("SIGMitch/KIT")
    result = pipe2(
        prompt=input_text,
        image=resized_image,
        width=width,
        height=height,
        num_images_per_prompt =4,
        strength=strength_slider,
        generator=generator,
        joint_attention_kwargs={"scale": 1.2},
        num_inference_steps=num_inference_steps_slider
        )
    print('INFERENCE DONE')
    return result.images[0], result.images[1], result.images[2], result.images[3]

     #resized_mask = mask.resize((width, height), Image.LANCZOS)

   #pipe.load_lora_weights("SIGMitch/KIT")
    #result = pipe(
     #   prompt=input_text,
     #   image=resized_image,
     #   mask_image=resized_mask,
     #   width=width,
     #   height=height,
     #   strength=strength_slider,
     #   generator=generator,
     #   joint_attention_kwargs={"scale": 1.2},
     #   num_inference_steps=num_inference_steps_slider
    #).images[0]
    #print('INFERENCE DONE')
    # return result, resized_mask


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            input_image_editor_component = gr.ImageEditor(
                label='Image',
                type='pil',
                sources=["upload"],
                image_mode='RGB',
                layers=False

            with gr.Row():
                input_text_component = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    container=False,
                )
                submit_button_component = gr.Button(
                    value='Submit', variant='primary', scale=0)

            with gr.Accordion("Advanced Settings", open=False):
                seed_slicer_component = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=42,
                )

                randomize_seed_checkbox_component = gr.Checkbox(
                    label="Randomize seed", value=True)

                with gr.Row():
                    strength_slider_component = gr.Slider(
                        label="Strength",
                        info="Indicates extent to transform the reference `image`. "
                             "Must be between 0 and 1. `image` is used as a starting "
                             "point and more noise is added the higher the `strength`.",
                        minimum=0,
                        maximum=1,
                        step=0.01,
                        value=0.85,
                    )

                    num_inference_steps_slider_component = gr.Slider(
                        label="Number of inference steps",
                        info="The number of denoising steps. More denoising steps "
                             "usually lead to a higher quality image at the",
                        minimum=1,
                        maximum=50,
                        step=1,
                        value=20,
                    )
        with gr.Column():
            output_image_component = gr.Image(
                type='pil', image_mode='RGB', label='Generated image', format="png")
             output_image_component2 = gr.Image(
                type='pil', image_mode='RGB', label='Generated image', format="png")
             output_image_component3 = gr.Image(
                type='pil', image_mode='RGB', label='Generated image', format="png")
             output_image_component4 = gr.Image(
                type='pil', image_mode='RGB', label='Generated image', format="png")


    submit_button_component.click(
        fn=process,
        inputs=[
            input_image_editor_component,
            input_text_component,
            seed_slicer_component,
            randomize_seed_checkbox_component,
            strength_slider_component,
            num_inference_steps_slider_component
        ],
        outputs=[
            output_image_component,
            output_image_component2,
            output_image_component3,
            output_image_component4
        ]
    )

demo.launch(debug=False, show_error=True)