app.py

import gradio as gr
from PIL import Image
import torch
#from diffusers import FluxControlNetModel
#from diffusers.pipelines import FluxControlNetPipeline

from diffusers import DiffusionPipeline

#from diffusers import FluxControlNetPipeline
#from diffusers import FluxControlNetModel #, FluxMultiControlNetModel

"""
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipe.load_lora_weights("enhanceaiteam/Flux-Uncensored-V2")

prompt = "nsfw nude woman on beach, sunset, long flowing hair, sensual pose"
image = pipe(prompt).images[0]
"""

#import torch.nn.functional as F
#import torchvision
#import torchvision.transforms as T
#import cv2

from diffusers import StableDiffusionInpaintPipeline

import numpy as np
import os
import shutil
from gradio_client import Client, handle_file

# Load the model once globally to avoid repeated loading
def load_inpainting_model():
    # Load pipeline
    model_path = "uberRealisticPornMerge_urpmv13Inpainting.safetensors"
    #model_path = "uberRealisticPornMerge_v23Inpainting.safetensors"
    #model_path = "pornmasterFantasy_v4-inpainting.safetensors"
    #model_path = "pornmasterAmateur_v6Vae-inpainting.safetensors"
    device = "cpu"  # Explicitly use CPU
    pipe = StableDiffusionInpaintPipeline.from_single_file(
        model_path,
        torch_dtype=torch.float32,  # Use float32 for CPU
        safety_checker=None
    ).to(device)
    return pipe

"""
# Load the model once globally to avoid repeated loading
def load_upscaling_model():
    # Load pipeline
    device = "cpu"  # Explicitly use CPU
    controlnet = FluxControlNetModel.from_pretrained(
        "jasperai/Flux.1-dev-Controlnet-Upscaler",
        torch_dtype=torch.float32
    )
    pipe = FluxControlNetPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        controlnet=controlnet,
        torch_dtype=torch.float32
    ).to(device)
    pipe = DiffusionPipeline.from_pretrained("jasperai/Flux.1-dev-Controlnet-Upscaler")    
    return pipe
"""

# Preload the model once
inpaint_pipeline = load_inpainting_model()
# Preload the model once
#upscale_pipeline = load_upscaling_model()

# Function to resize image (simpler interpolation method for speed)
def resize_to_match(input_image, output_image):

    #w, h = output_image.size
    #control_image = output_image.resize((w * 4, h * 4))
    """    
    scaled_image = pipe(
        prompt="", 
        control_image=control_image,
        controlnet_conditioning_scale=0.6,
        num_inference_steps=28, 
        guidance_scale=3.5,
        height=control_image.size[1],
        width=control_image.size[0]
    ).images[0]    
    """
    #return scaled_image
    
    #torch_img = pil_to_torch(input_image)
    #torch_img_scaled = F.interpolate(torch_img.unsqueeze(0),mode='trilinear').squeeze(0)
    #output_image = torchvision.transforms.functional.to_pil_image(torch_img_scaled, mode=None)
    return output_image.resize(input_image.size, Image.BICUBIC)  # Use BILINEAR for faster resizing

# Function to generate the mask using Florence SAM Masking API (Replicate)
def generate_mask(image_path, text_prompt="clothing"):
    client_sam = Client("SkalskiP/florence-sam-masking")
    mask_result = client_sam.predict(
        image_input=handle_file(image_path),  # Provide your image path here
        text_input=text_prompt,  # Use "clothing" as the prompt
        api_name="/process_image"
    )
    return mask_result  # This is the local path to the generated mask

# Save the generated mask
def save_mask(mask_local_path, save_path="generated_mask.png"):
    try:
        shutil.copy(mask_local_path, save_path)
    except Exception as e:
        print(f"Failed to save the mask: {e}")

# Function to perform inpainting
def inpaint_image(input_image, mask_image):
    prompt = "undress, naked, real skin, detailed nipples, erect nipples, detailed pussy, (detailed nipples), (detailed skin), (detailed pussy)"
    negative_prompt = "bad anatomy, deformed, ugly, disfigured, (extra arms), (extra legs), (extra hands), (extra feet), (extra finger)"
    """
    IMAGE_SIZE = (1024,1024)
    initial_input_image = input_image.resize(IMAGE_SIZE)
    initial_mask_image = mask_image.resize(IMAGE_SIZE)
    blurred_mask_image = inpaint_pipeline.mask_processor.blur(initial_mask_image,blur_factor=50)
    result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, height=IMAGE_SIZE[0], width=IMAGE_SIZE[0], image=initial_input_image, mask_image=blurred_mask_image, padding_mask_crop=32)
    """
    #blurred_mask_image = inpaint_pipeline.mask_processor.blur(mask_image,blur_factor=50)
    result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, image=input_image, mask_image=mask_image, padding_mask_crop=32)
    inpainted_image = result.images[0]
    #inpainted_image = resize_to_match(input_image, inpainted_image)
    return inpainted_image

# Function to process input image and mask
def process_image(input_image):
    # Save the input image temporarily to process with Replicate
    input_image_path = "temp_input_image.png"
    input_image.save(input_image_path)

    # Generate the mask using Florence SAM API
    mask_local_path = generate_mask(image_path=input_image_path)

    # Save the generated mask
    mask_image_path = "generated_mask.png"
    save_mask(mask_local_path, save_path=mask_image_path)

    # Open the mask image and perform inpainting
    mask_image = Image.open(mask_image_path)
    result_image = inpaint_image(input_image, mask_image)

    # Clean up temporary files
    os.remove(input_image_path)
    os.remove(mask_image_path)

    return result_image

# Define Gradio interface using Blocks API
with gr.Blocks() as demo:
    with gr.Row():
        input_image = gr.Image(label="Upload Input Image", type="pil")
        output_image = gr.Image(type="pil", label="Output Image")

    # Button to trigger the process
    with gr.Row():
        btn = gr.Button("Run Inpainting")

    # Function to run when button is clicked
    btn.click(fn=process_image, inputs=[input_image], outputs=output_image)

# Launch the Gradio app
demo.launch(share=True)