infer_full.py

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")