import gradio as gr import torch from PIL import Image import numpy as np from PIL import Image from omegaconf import OmegaConf import os import cv2 from diffusers import DDIMScheduler, UniPCMultistepScheduler from diffusers.models import UNet2DConditionModel from ref_encoder.latent_controlnet import ControlNetModel from ref_encoder.adapter import * from ref_encoder.reference_unet import ref_unet from utils.pipeline import StableHairPipeline from utils.pipeline_cn import StableDiffusionControlNetPipeline torch.cuda.set_per_process_memory_fraction(0.80, device="cuda:0") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") class StableHair: def __init__(self, config="./configs/hair_transfer.yaml", device=device, weight_dtype=torch.float32) -> None: print("Initializing Stable Hair Pipeline...") self.config = OmegaConf.load(config) self.device = device ### Load vae controlnet unet = UNet2DConditionModel.from_pretrained(self.config.pretrained_model_path, subfolder="unet").to(device) controlnet = ControlNetModel.from_unet(unet).to(device) _state_dict = torch.load(os.path.join(self.config.pretrained_folder, self.config.controlnet_path)) controlnet.load_state_dict(_state_dict, strict=False) controlnet.to(weight_dtype) ### >>> create pipeline >>> ### self.pipeline = StableHairPipeline.from_pretrained( self.config.pretrained_model_path, controlnet=controlnet, safety_checker=None, torch_dtype=weight_dtype, ).to(device) self.pipeline.scheduler = DDIMScheduler.from_config(self.pipeline.scheduler.config) ### load Hair encoder/adapter self.hair_encoder.to("cpu") self.hair_encoder = ref_unet.from_pretrained(self.config.pretrained_model_path, subfolder="unet").to(device) _state_dict = torch.load(os.path.join(self.config.pretrained_folder, self.config.encoder_path)) self.hair_encoder.load_state_dict(_state_dict, strict=False) self.hair_adapter = adapter_injection(self.pipeline.unet, device=self.device, dtype=torch.float32, use_resampler=False) _state_dict = torch.load(os.path.join(self.config.pretrained_folder, self.config.adapter_path)) self.hair_adapter.load_state_dict(_state_dict, strict=False) ### load bald converter bald_converter = ControlNetModel.from_unet(unet).to(device) _state_dict = torch.load(self.config.bald_converter_path) bald_converter.load_state_dict(_state_dict, strict=False) bald_converter.to(dtype=weight_dtype) del unet ### create pipeline for hair removal self.remove_hair_pipeline = StableDiffusionControlNetPipeline.from_pretrained( self.config.pretrained_model_path, controlnet=bald_converter, safety_checker=None, torch_dtype=weight_dtype, ) self.remove_hair_pipeline.scheduler = UniPCMultistepScheduler.from_config(self.remove_hair_pipeline.scheduler.config) self.remove_hair_pipeline = self.remove_hair_pipeline.to(device) ### move to fp16 self.hair_encoder.to(weight_dtype) self.hair_adapter.to(weight_dtype) print("Initialization Done!") def Hair_Transfer(self, source_image, reference_image, random_seed, step, guidance_scale, scale, controlnet_conditioning_scale): prompt = "" n_prompt = "" random_seed = int(random_seed) step = int(step) guidance_scale = float(guidance_scale) scale = float(scale) controlnet_conditioning_scale = float(controlnet_conditioning_scale) # load imgs H, W, C = source_image.shape # generate images set_scale(self.pipeline.unet, scale) generator = torch.Generator(device="cuda") generator.manual_seed(random_seed) sample = self.pipeline( prompt, negative_prompt=n_prompt, num_inference_steps=step, guidance_scale=guidance_scale, width=W, height=H, controlnet_condition=source_image, controlnet_conditioning_scale=controlnet_conditioning_scale, generator=generator, reference_encoder=self.hair_encoder, ref_image=reference_image, ).samples return sample, source_image, reference_image def get_bald(self, id_image, scale): H, W = id_image.size scale = float(scale) image = self.remove_hair_pipeline( prompt="", negative_prompt="", num_inference_steps=30, guidance_scale=1.5, width=W, height=H, image=id_image, controlnet_conditioning_scale=scale, generator=None, ).images[0] return image model = StableHair(config="./configs/hair_transfer.yaml", weight_dtype=torch.float16) # Define your ML model or function here def model_call(id_image, ref_hair, converter_scale, scale, guidance_scale, controlnet_conditioning_scale): # # Your ML logic goes here id_image = Image.fromarray(id_image.astype('uint8'), 'RGB') ref_hair = Image.fromarray(ref_hair.astype('uint8'), 'RGB') id_image = id_image.resize((512, 512)) ref_hair = ref_hair.resize((512, 512)) id_image_bald = model.get_bald(id_image, converter_scale) id_image_bald = np.array(id_image_bald) ref_hair = np.array(ref_hair) image, source_image, reference_image = model.Hair_Transfer(source_image=id_image_bald, reference_image=ref_hair, random_seed=-1, step=30, guidance_scale=guidance_scale, scale=scale, controlnet_conditioning_scale=controlnet_conditioning_scale ) image = Image.fromarray((image * 255.).astype(np.uint8)) return id_image_bald, image # Create a Gradio interface image1 = gr.inputs.Image(label="id_image") image2 = gr.inputs.Image(label="ref_hair") number0 = gr.inputs.Slider(minimum=0.5, maximum=1.5, default=1, label="Converter Scale") number1 = gr.inputs.Slider(minimum=0.0, maximum=3, default=1.0, label="Hair Encoder Scale") number2 = gr.inputs.Slider(minimum=1.1, maximum=3.0, default=1.5, label="CFG") number3 = gr.inputs.Slider(minimum=0.1, maximum=2.0, default=1, label="Latent IdentityNet Scale") output1 = gr.outputs.Image(type="pil", label="Bald_Result") output2 = gr.outputs.Image(type="pil", label="Transfer Result") iface = gr.Interface( fn=lambda id_image, ref_hair, num0, num1, num2, num3, : model_call(id_image, ref_hair, num0, num1, num2, num3), inputs=[image1, image2, number0, number1, number2, number3], outputs=[output1, output2], title="Hair Transfer Demo", description="In general, aligned faces work well, but can also be used on non-aligned faces, and you need to resize to 512 * 512" ) # Launch the Gradio interface iface.queue().launch(server_name='0.0.0.0', server_port=8986)