import subprocess from PIL import Image def download_file(url, output_filename): command = ['wget', '-O', output_filename, '-q', url] subprocess.run(command, check=True) url1 = 'https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_multiclass_256x256/float32/latest/selfie_multiclass_256x256.tflite' url2 = 'https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_segmenter/float16/latest/selfie_segmenter.tflite' filename1 = 'selfie_multiclass_256x256.tflite' filename2 = 'selfie_segmenter.tflite' download_file(url1, filename1) download_file(url2, filename2) import cv2 import mediapipe as mp import numpy as np from mediapipe.tasks import python from mediapipe.tasks.python import vision import random import gradio as gr import spaces import torch from diffusers import FluxInpaintPipeline from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast DEVICE = "cuda" if torch.cuda.is_available() else "cpu" bfl_repo="black-forest-labs/FLUX.1-dev" BG_COLOR = (255, 255, 255) # white MASK_COLOR = (0, 0 , 0) # black def maskHead(input): base_options = python.BaseOptions(model_asset_path='selfie_multiclass_256x256.tflite') options = vision.ImageSegmenterOptions(base_options=base_options, output_category_mask=True) with vision.ImageSegmenter.create_from_options(options) as segmenter: image = mp.Image.create_from_file(input) segmentation_result = segmenter.segment(image) hairmask = segmentation_result.confidence_masks[1] facemask = segmentation_result.confidence_masks[3] image_data = image.numpy_view() fg_image = np.zeros(image_data.shape, dtype=np.uint8) fg_image[:] = MASK_COLOR bg_image = np.zeros(image_data.shape, dtype=np.uint8) bg_image[:] = BG_COLOR combined_mask = np.maximum(hairmask.numpy_view(), facemask.numpy_view()) condition = np.stack((combined_mask,) * 3, axis=-1) > 0.2 output_image = np.where(condition, fg_image, bg_image) return output_image def random_positioning(input, output_size=(1024, 1024)): background = cv2.imread("default.jpeg") if background is None: raise ValueError("Unable to load background image") background = cv2.resize(background, output_size, interpolation=cv2.INTER_AREA) if input is None: raise ValueError("Unable to load input image") scale_factor = random.uniform(0.5, 1.0) new_size = (int(input.shape[1] * scale_factor), int(input.shape[0] * scale_factor)) resized_image = cv2.resize(input, new_size, interpolation=cv2.INTER_AREA) if background.shape[2] != resized_image.shape[2]: raise ValueError("Input image and background image must have the same number of channels") x_offset = random.randint(0, output_size[0] - new_size[0]) y_offset = random.randint(0, output_size[1] - new_size[1]) background[y_offset:y_offset+new_size[1], x_offset:x_offset+new_size[0]] = resized_image return background def remove_background(image_path, mask): image = cv2.imread(image_path) inverted_mask = cv2.bitwise_not(mask) _, binary_mask = cv2.threshold(inverted_mask, 127, 255, cv2.THRESH_BINARY) result = np.zeros_like(image, dtype=np.uint8) result[binary_mask == 255] = image[binary_mask == 255] return result pipe = FluxInpaintPipeline.from_pretrained(bfl_repo, torch_dtype=torch.bfloat16).to(DEVICE) MAX_SEED = np.iinfo(np.int32).max TRIGGER = "a photo of TOK" @spaces.GPU(duration=100) def execute(image, prompt): if not prompt : gr.Info("Please enter a text prompt.") return None if not image : gr.Info("Please upload a image.") return None img = cv2.imread(image) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) imgs = [ random_positioning(img), random_positioning(img)] pipe.load_lora_weights("XLabs-AI/flux-RealismLora", weight_name='lora.safetensors') response = [] seed_slicer = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed_slicer) for image in range(len(imgs)): current_img = imgs[image] cv2.imwrite('base_image.jpg', current_img) cv2.imwrite("mask.jpg", maskHead('base_image.jpg')) im = Image.open('base_image.jpg') np_arr = np.array(im) rgb_image = cv2.cvtColor(np_arr, cv2.COLOR_BGR2RGB) im = Image.fromarray(rgb_image) mask = Image.open('mask.jpg') result = pipe( prompt=f"{prompt} {TRIGGER}", image=im, mask_image=mask, width=1024, height=1024, strength=0.85, generator=generator, num_inference_steps=28, max_sequence_length=256, joint_attention_kwargs={"scale": 0.9}, ).images[0] response.append(result) return response # Created by Fountai # https://x.com/EuFountai description = "This is an unofficial implementation of the ip face adapter for FLUX DEV and does not explicitly follow the ip face model, I created a wrapper with inpaint and mediapipe, I like to call Fake IP Adapter" title = "Flux IP Face Adapter" iface = gr.Interface( fn=execute, description=description, title=title, inputs=[ gr.Image(type="filepath"), gr.Textbox(label="Prompt") ], outputs="gallery" ) iface.launch()