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 def _resolve_weight(prefix_path: str, filename: str) -> str: """Resolve a weight path, downloading from Hugging Face Hub if needed. prefix_path can be either a local directory (e.g., ./models/stage2) or a hub path like Org/Repo/subfolder. When it looks like a hub path, we download the file via hf_hub_download using repo_id Org/Repo and subfolder the remaining segments. """ # Try local first local_path = os.path.join(prefix_path, filename) if os.path.exists(local_path): return local_path # Attempt Hub download try: from huggingface_hub import hf_hub_download parts = prefix_path.strip("/").split("/") if len(parts) >= 2: repo_id = "/".join(parts[:2]) subfolder = "/".join(parts[2:]) if len(parts) > 2 else None downloaded = hf_hub_download( repo_id=repo_id, filename=filename, subfolder=subfolder, token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"), ) return downloaded except Exception as exc: # noqa: WPS440 raise RuntimeError(f"Failed to fetch {filename} from hub ({prefix_path}): {exc}") raise FileNotFoundError(f"Weight not found locally and not a valid hub path: {prefix_path}/{filename}") def concatenate_images(image_files, output_file, type="pil"): if type == "np": image_files = [Image.fromarray(img) for img in image_files] images = image_files # list max_height = max(img.height for img in images) images = [img.resize((img.width, max_height)) for img in images] total_width = sum(img.width for img in images) combined = Image.new('RGB', (total_width, max_height)) x_offset = 0 for img in images: combined.paste(img, (x_offset, 0)) x_offset += img.width combined.save(output_file) class StableHair: def __init__(self, config="./configs/hair_transfer.yaml", device="cuda", weight_dtype=torch.float32) -> None: print("Initializing Stable Hair Pipeline...") self.config = OmegaConf.load(config) self.device = device # Hugging Face repo with weights - using the working approach from app.py repo_id = "LogicGoInfotechSpaces/new_weights" # Download weights from Hugging Face using the direct approach that worked from huggingface_hub import hf_hub_download encoder_hf_path = hf_hub_download(repo_id=repo_id, filename="stage2/pytorch_model.bin") adapter_hf_path = hf_hub_download(repo_id=repo_id, filename="stage2/pytorch_model_1.bin") controlnet_hf_path = hf_hub_download(repo_id=repo_id, filename="stage2/pytorch_model_2.bin") bald_converter_hf_path = hf_hub_download(repo_id=repo_id, filename="stage1/pytorch_model.bin") ### 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(controlnet_hf_path, map_location="cpu") 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 = ref_unet.from_pretrained(self.config.pretrained_model_path, subfolder="unet").to(device) _state_dict = torch.load(encoder_hf_path, map_location="cpu") self.hair_encoder.load_state_dict(_state_dict, strict=False) self.hair_adapter = adapter_injection(self.pipeline.unet, device=self.device, dtype=torch.float16, use_resampler=False) _state_dict = torch.load(adapter_hf_path, map_location="cpu") 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(bald_converter_hf_path, map_location="cpu") 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, size=512): prompt = "" n_prompt = "" random_seed = int(random_seed) step = int(step) guidance_scale = float(guidance_scale) scale = float(scale) # load imgs source_image = Image.open(source_image).convert("RGB").resize((size, size)) id = np.array(source_image) reference_image = np.array(Image.open(reference_image).convert("RGB").resize((size, size))) source_image_bald = np.array(self.get_bald(source_image, scale=0.9)) H, W, C = source_image_bald.shape # generate images set_scale(self.pipeline.unet, scale) generator = torch.Generator(device=self.device) 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_bald, controlnet_conditioning_scale=controlnet_conditioning_scale, generator=generator, reference_encoder=self.hair_encoder, ref_image=reference_image, ).samples return id, sample, source_image_bald, 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 if __name__ == '__main__': model = StableHair(config="./configs/hair_transfer.yaml", weight_dtype=torch.float32) kwargs = OmegaConf.to_container(model.config.inference_kwargs) id, image, source_image_bald, reference_image = model.Hair_Transfer(**kwargs) os.makedirs(model.config.output_path, exist_ok=True) output_file = os.path.join(model.config.output_path, model.config.save_name) concatenate_images([id, source_image_bald, reference_image, (image*255.).astype(np.uint8)], output_file=output_file, type="np")