File size: 8,910 Bytes
7e328d3 c6f74e6 66a46f6 c6f74e6 7e328d3 c6f74e6 7e328d3 c6f74e6 7e328d3 c6f74e6 7e328d3 c6f74e6 7e328d3 c6f74e6 7e328d3 c6f74e6 7e328d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
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
try:
### Load controlnet
# Try multiple model paths in case of access issues
model_paths = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1",
"stabilityai/stable-diffusion-2-1-base"
]
unet = None
for model_path in model_paths:
try:
print(f"Trying to load model from: {model_path}")
unet = UNet2DConditionModel.from_pretrained(model_path, subfolder="unet").to(device)
self.config.pretrained_model_path = model_path # Update config with working path
print(f"Successfully loaded model from: {model_path}")
break
except Exception as e:
print(f"Failed to load {model_path}: {str(e)}")
continue
if unet is None:
raise Exception("Could not load any Stable Diffusion model")
controlnet = ControlNetModel.from_unet(unet).to(device)
# Try to load custom controlnet weights, fallback to default if not available
controlnet_path = os.path.join(self.config.pretrained_folder, self.config.controlnet_path)
if os.path.exists(controlnet_path):
print(f"Loading custom controlnet from {controlnet_path}")
_state_dict = torch.load(controlnet_path)
controlnet.load_state_dict(_state_dict, strict=False)
else:
print(f"Custom controlnet not found at {controlnet_path}, using default")
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)
# Try to load custom encoder weights, fallback to default if not available
encoder_path = os.path.join(self.config.pretrained_folder, self.config.encoder_path)
if os.path.exists(encoder_path):
print(f"Loading custom encoder from {encoder_path}")
_state_dict = torch.load(encoder_path)
self.hair_encoder.load_state_dict(_state_dict, strict=False)
else:
print(f"Custom encoder not found at {encoder_path}, using default")
self.hair_adapter = adapter_injection(self.pipeline.unet, device=self.device, dtype=torch.float16, use_resampler=False)
# Try to load custom adapter weights, fallback to default if not available
adapter_path = os.path.join(self.config.pretrained_folder, self.config.adapter_path)
if os.path.exists(adapter_path):
print(f"Loading custom adapter from {adapter_path}")
_state_dict = torch.load(adapter_path)
self.hair_adapter.load_state_dict(_state_dict, strict=False)
else:
print(f"Custom adapter not found at {adapter_path}, using default")
### load bald converter
bald_converter = ControlNetModel.from_unet(unet).to(device)
# Try to load custom bald converter weights, fallback to default if not available
bald_converter_path = self.config.bald_converter_path
if os.path.exists(bald_converter_path):
print(f"Loading custom bald converter from {bald_converter_path}")
_state_dict = torch.load(bald_converter_path)
bald_converter.load_state_dict(_state_dict, strict=False)
else:
print(f"Custom bald converter not found at {bald_converter_path}, using default")
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!")
except Exception as e:
print(f"Error during model initialization: {str(e)}")
raise Exception(f"Model initialization failed: {str(e)}")
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")
|