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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
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="stable_hair/configs/hair_transfer.yaml", device="cuda", weight_dtype=torch.float16) -> None:
print("Initializing Stable Hair Pipeline...")
self.config = OmegaConf.load(config)
self.device = device
### Load 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 = UniPCMultistepScheduler.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(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.float16, 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, 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="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_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")
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