import gradio as gr from PIL import Image import base64 from io import BytesIO 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 encode_image(orig_image): buffered = BytesIO() orig_image.save(buffered, format="JPEG") img_str = base64.b64encode(buffered.getvalue()) return img_str def resize_image(input_image, mask_image): orig_image = mask_image.resize(input_image.size, Image.Resampling.LANCZOS) 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_old(image_path, mask_path, text_prompt="undress", negative_prompt=""): result = client.predict( text_prompt, # str in 'parameter_10' Textbox component negative_prompt, # 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 ∣ 1:2', # 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 ) def generate_image(image_path, mask_path, text_prompt="undress", negative_prompt=""): client = Client("https://fooocus-ui.emcdn.ru/") result = client.predict( False, # bool in 'Generate Image Grid for Each Batch' Checkbox component text_prompt, # str in 'parameter_12' Textbox component negative_prompt, # str in 'Negative Prompt' Textbox component ["Fooocus V2"], # List[str] in 'Selected Styles' Checkboxgroup component from: ["Fooocus V2","Fooocus Enhance","Fooocus Sharp"] "Quality", # str in 'Performance' Radio component '704×1408 ∣ 1:2', # str in 'Aspect Ratios' Radio component 1, # int | float (numeric value between 1 and 32) in 'Image Number' Slider component "png", # str in 'Output Format' Radio component "-1", # str in 'Seed' Textbox component True, # bool in 'Read wildcards in order' Checkbox 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_version8Rundiffusion.safetensors", # str (Option from: ['animaPencilXL_v500.safetensors', 'juggernautXL_v8Rundiffusion.safetensors', 'playground-v2.5-1024px-aesthetic.fp16.safetensors', 'ponyDiffusionV6XL.safetensors', 'realisticStockPhoto_v20.safetensors', 'sd_xl_base_1.0_0.9vae.safetensors', 'sd_xl_refiner_1.0_0.9vae.safetensors']) in 'Base Model (SDXL only)' Dropdown component "None", # str (Option from: ['None', 'animaPencilXL_v500.safetensors', 'juggernautXL_v8Rundiffusion.safetensors', 'playground-v2.5-1024px-aesthetic.fp16.safetensors', 'ponyDiffusionV6XL.safetensors', 'realisticStockPhoto_v20.safetensors', 'sd_xl_base_1.0_0.9vae.safetensors', 'sd_xl_refiner_1.0_0.9vae.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 True, # bool in 'Enable' Checkbox component "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors', 'sdxl_hyper_sd_4step_lora.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 1' Dropdown component -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component True, # bool in 'Enable' Checkbox component "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors', 'sdxl_hyper_sd_4step_lora.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 2' Dropdown component -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component True, # bool in 'Enable' Checkbox component "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors', 'sdxl_hyper_sd_4step_lora.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 3' Dropdown component -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component True, # bool in 'Enable' Checkbox component "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors', 'sdxl_hyper_sd_4step_lora.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.safetensors']) in 'LoRA 4' Dropdown component -2, # int | float (numeric value between -2 and 2) in 'Weight' Slider component True, # bool in 'Enable' Checkbox component "None", # str (Option from: ['None', 'sd_xl_offset_example-lora_1.0.safetensors', 'SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors', 'sdxl_hyper_sd_4step_lora.safetensors', 'sdxl_lcm_lora.safetensors', 'sdxl_lightning_4step_lora.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 "-1", # str in 'parameter_212' Textbox component "Disabled", # str in 'Upscale or Variation:' Radio component "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Image' Image component ["Left"], # List[str] in 'Outpaint Direction' Checkboxgroup component "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Image' Image component "", # str in 'Inpaint Additional Prompt' Textbox component "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Mask Upload' Image component True, # bool in 'Disable Preview' Checkbox component True, # bool in 'Disable Intermediate Results' Checkbox component True, # bool in 'Disable seed increment' Checkbox component False, # bool in 'Black Out NSFW' Checkbox component 0.1, # int | float (numeric value between 0.1 and 3.0) in 'Positive ADM Guidance Scaler' Slider component 0.1, # int | float (numeric value between 0.1 and 3.0) in 'Negative ADM Guidance Scaler' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'ADM Guidance End At Step' Slider component 1, # int | float (numeric value between 1.0 and 30.0) in 'CFG Mimicking from TSNR' Slider component 1, # int | float (numeric value between 1 and 12) in 'CLIP Skip' Slider component "euler", # str (Option from: ['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2', 'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive', 'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'tcd', 'restart', 'ddim', 'uni_pc', 'uni_pc_bh2']) in 'Sampler' Dropdown component "normal", # str (Option from: ['normal', 'karras', 'exponential', 'sgm_uniform', 'simple', 'ddim_uniform', 'lcm', 'turbo', 'align_your_steps', 'tcd', 'edm_playground_v2.5']) in 'Scheduler' Dropdown component "Default (model)", # str (Option from: ['Default (model)', 'ponyDiffusionV6XL_vae.safetensors']) in 'VAE' Dropdown component -1, # int | float (numeric value between -1 and 200) in 'Forced Overwrite of Sampling Step' Slider component -1, # int | float (numeric value between -1 and 200) in 'Forced Overwrite of Refiner Switch Step' Slider component -1, # int | float (numeric value between -1 and 2048) in 'Forced Overwrite of Generating Width' Slider component -1, # int | float (numeric value between -1 and 2048) in 'Forced Overwrite of Generating Height' Slider component -1, # int | float (numeric value between -1 and 1.0) in 'Forced Overwrite of Denoising Strength of "Vary"' Slider component -1, # int | float (numeric value between -1 and 1.0) in 'Forced Overwrite of Denoising Strength of "Upscale"' Slider component True, # bool in 'Mixing Image Prompt and Vary/Upscale' Checkbox component True, # bool in 'Mixing Image Prompt and Inpaint' Checkbox component True, # bool in 'Debug Preprocessors' Checkbox component True, # bool in 'Skip Preprocessors' Checkbox component 1, # int | float (numeric value between 1 and 255) in 'Canny Low Threshold' Slider component 1, # int | float (numeric value between 1 and 255) in 'Canny High Threshold' Slider component "joint", # str (Option from: ['joint', 'separate', 'vae']) in 'Refiner swap method' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Softness of ControlNet' Slider component True, # bool in 'Enabled' Checkbox component 0, # int | float (numeric value between 0 and 2) in 'B1' Slider component 0, # int | float (numeric value between 0 and 2) in 'B2' Slider component 0, # int | float (numeric value between 0 and 4) in 'S1' Slider component 0, # int | float (numeric value between 0 and 4) in 'S2' Slider component True, # bool in 'Debug Inpaint Preprocessing' Checkbox component True, # bool in 'Disable initial latent in inpaint' Checkbox component "None", # str (Option from: ['None', 'v1', 'v2.5', 'v2.6']) in 'Inpaint Engine' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Denoising Strength' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Respective Field' Slider component True, # bool in 'Enable Advanced Masking Features' Checkbox component True, # bool in 'Invert Mask When Generating' Checkbox component -64, # int | float (numeric value between -64 and 64) in 'Mask Erode or Dilate' Slider component True, # bool in 'Save only final enhanced image' Checkbox component True, # bool in 'Save Metadata to Images' Checkbox component "fooocus", # str in 'Metadata Scheme' Radio component "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # 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 "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # 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 "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # 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 "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # 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 True, # bool in 'Debug GroundingDINO' Checkbox component -64, # int | float (numeric value between -64 and 64) in 'GroundingDINO Box Erode or Dilate' Slider component True, # bool in 'Debug Enhance Masks' Checkbox component "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", # str (filepath or URL to image) in 'Use with Enhance, skips image generation' Image component True, # bool in 'Enhance' Checkbox component "Disabled", # str in 'Upscale or Variation:' Radio component "Before First Enhancement", # str in 'Order of Processing' Radio component "Original Prompts", # str in 'Prompt' Radio component True, # bool in 'Enable' Checkbox component "", # str in 'Detection prompt' Textbox component "", # str in 'Enhancement positive prompt' Textbox component "", # str in 'Enhancement negative prompt' Textbox component "u2net", # str (Option from: ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']) in 'Mask generation model' Dropdown component "full", # str (Option from: ['full', 'upper', 'lower']) in 'Cloth category' Dropdown component "vit_b", # str (Option from: ['vit_b', 'vit_l', 'vit_h']) in 'SAM model' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Text Threshold' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Box Threshold' Slider component 0, # int | float (numeric value between 0 and 10) in 'Maximum number of detections' Slider component True, # bool in 'Disable initial latent in inpaint' Checkbox component "None", # str (Option from: ['None', 'v1', 'v2.5', 'v2.6']) in 'Inpaint Engine' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Denoising Strength' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Respective Field' Slider component -64, # int | float (numeric value between -64 and 64) in 'Mask Erode or Dilate' Slider component True, # bool in 'Invert Mask' Checkbox component True, # bool in 'Enable' Checkbox component "", # str in 'Detection prompt' Textbox component "", # str in 'Enhancement positive prompt' Textbox component "", # str in 'Enhancement negative prompt' Textbox component "u2net", # str (Option from: ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']) in 'Mask generation model' Dropdown component "full", # str (Option from: ['full', 'upper', 'lower']) in 'Cloth category' Dropdown component "vit_b", # str (Option from: ['vit_b', 'vit_l', 'vit_h']) in 'SAM model' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Text Threshold' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Box Threshold' Slider component 0, # int | float (numeric value between 0 and 10) in 'Maximum number of detections' Slider component True, # bool in 'Disable initial latent in inpaint' Checkbox component "None", # str (Option from: ['None', 'v1', 'v2.5', 'v2.6']) in 'Inpaint Engine' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Denoising Strength' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Respective Field' Slider component -64, # int | float (numeric value between -64 and 64) in 'Mask Erode or Dilate' Slider component True, # bool in 'Invert Mask' Checkbox component True, # bool in 'Enable' Checkbox component "", # str in 'Detection prompt' Textbox component "", # str in 'Enhancement positive prompt' Textbox component "", # str in 'Enhancement negative prompt' Textbox component "u2net", # str (Option from: ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']) in 'Mask generation model' Dropdown component "full", # str (Option from: ['full', 'upper', 'lower']) in 'Cloth category' Dropdown component "vit_b", # str (Option from: ['vit_b', 'vit_l', 'vit_h']) in 'SAM model' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Text Threshold' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Box Threshold' Slider component 0, # int | float (numeric value between 0 and 10) in 'Maximum number of detections' Slider component True, # bool in 'Disable initial latent in inpaint' Checkbox component "None", # str (Option from: ['None', 'v1', 'v2.5', 'v2.6']) in 'Inpaint Engine' Dropdown component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Denoising Strength' Slider component 0, # int | float (numeric value between 0.0 and 1.0) in 'Inpaint Respective Field' Slider component -64, # int | float (numeric value between -64 and 64) in 'Mask Erode or Dilate' Slider component True, # bool in 'Invert Mask' Checkbox component fn_index=67 ) print(result) # 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(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)