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| 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 = "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() | |
| def resize_image(orig_image): | |
| aspect_ratio = orig_image.height / orig_image.width | |
| old_width = orig_image.width | |
| new_width = int(orig_image.width*1.2) | |
| old_height = orig_image.height | |
| new_height = int(new_width * aspect_ratio) | |
| resized_image = orig_image.resize((new_width, new_height), Image.Resampling.LANCZOS) | |
| left_crop = int((new_width - old_width)/2) | |
| right_crop = new_width - int((new_width - old_width) / 2) | |
| top_crop = int((new_height - old_height)/2) | |
| bottom_crop = new_height - int((new_height - old_height) / 2) | |
| cropped_image = resized_image.crop((left_crop,top_crop,right_crop,bottom_crop)) | |
| return cropped_image | |
| # 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 | |
| def generate_image(image_path, mask_path, text_prompt="undress"): | |
| result = client.predict( | |
| "", # str in 'parameter_10' Textbox component | |
| "", # str in 'Negative Prompt' Textbox component | |
| ["Fooocus V2","Fooocus Enhance","Fooocus Sharp"], # List[str] in 'Selected Styles' Checkboxgroup component | |
| "Quality", # str in 'Performance' Radio component | |
| '704×1408 <span style="color: grey;"> ∣ 1:2</span>', # str in 'Aspect Ratios' Radio component | |
| 1, # int | float (numeric value between 1 and 32) in 'Image Number' Slider component | |
| "-1", # str in 'Seed' Textbox component | |
| 0, # int | float (numeric value between 0.0 and 30.0) in 'Image Sharpness' Slider component | |
| 1, # int | float (numeric value between 1.0 and 30.0) in 'Guidance Scale' Slider component | |
| "juggernautXL_version6Rundiffusion.safetensors", # str (Option from: ['ACertainty.ckpt', 'ACertainty.safetensors', 'juggernautXL_version6Rundiffusion.safetensors']) in 'Base Model (SDXL only)' Dropdown component | |
| "None", # str (Option from: ['None', 'ACertainty.ckpt', 'ACertainty.safetensors', 'juggernautXL_version6Rundiffusion.safetensors']) in 'Refiner (SDXL or SD 1.5)' Dropdown component | |
| 0.1, # int | float (numeric value between 0.1 and 1.0) in 'Refiner Switch At' Slider component | |
| "None", # str (Option from: ['None', 'sdxl_lcm_lora.safetensors', 'sd_xl_offset_example-lora_1.0.safetensors']) in 'LoRA 1' Dropdown component | |
| -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component | |
| "None", # str (Option from: ['None', 'sdxl_lcm_lora.safetensors', 'sd_xl_offset_example-lora_1.0.safetensors']) in 'LoRA 2' Dropdown component | |
| -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component | |
| "None", # str (Option from: ['None', 'sdxl_lcm_lora.safetensors', 'sd_xl_offset_example-lora_1.0.safetensors']) in 'LoRA 3' Dropdown component | |
| -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component | |
| "None", # str (Option from: ['None', 'sdxl_lcm_lora.safetensors', 'sd_xl_offset_example-lora_1.0.safetensors']) in 'LoRA 4' Dropdown component | |
| -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component | |
| "None", # str (Option from: ['None', 'sdxl_lcm_lora.safetensors', 'sd_xl_offset_example-lora_1.0.safetensors']) in 'LoRA 5' Dropdown component | |
| -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component | |
| True, # bool in 'Input Image' Checkbox component | |
| "", # str in 'parameter_85' Textbox component | |
| "Disabled", # str in 'Upscale or Variation:' Radio component | |
| None, # str (filepath or URL to image) in 'Drag above image to here' Image component | |
| [], # List[str] in 'Outpaint Direction' Checkboxgroup component | |
| image_path, # str (filepath or URL to image) in 'Drag inpaint or outpaint image to here' Image component | |
| "", # str in 'Inpaint Additional Prompt' Textbox component | |
| mask_path, # str (filepath or URL to image) in 'Mask Upload' Image component | |
| image_path, # str (filepath or URL to image) in 'Image' Image component | |
| 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component | |
| 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component | |
| "ImagePrompt", # str in 'Type' Radio component | |
| None, # str (filepath or URL to image) in 'Image' Image component | |
| 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component | |
| 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component | |
| "ImagePrompt", # str in 'Type' Radio component | |
| None, # str (filepath or URL to image) in 'Image' Image component | |
| 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component | |
| 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component | |
| "ImagePrompt", # str in 'Type' Radio component | |
| None, # str (filepath or URL to image) in 'Image' Image component | |
| 0, # int | float (numeric value between 0.0 and 1.0) in 'Stop At' Slider component | |
| 0, # int | float (numeric value between 0.0 and 2.0) in 'Weight' Slider component | |
| "ImagePrompt", # str in 'Type' Radio component | |
| fn_index=33 | |
| ) | |
| # 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( | |
| #mode_dropdown = "open vocabulary detection + image masks", | |
| image_input=handle_file(image_path), # Provide your image path here | |
| text_input=text_prompt, # Use "clothing" as the prompt | |
| api_name="/process_image" | |
| ) | |
| print("mask_result=", mask_result) | |
| 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), accurate anatomy" | |
| 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=10) | |
| #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=10) | |
| result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, image=input_image, mask_image=mask_image, padding_mask_crop=10) | |
| 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) | |
| #mask_local_path1 = str(mask_local_path)#[0]) | |
| # 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 = resize_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) | |