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PuLID / pulid /pipeline.py
邬彦泽
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import gc
import cv2
import insightface
import torch
import torch.nn as nn
from basicsr.utils import img2tensor, tensor2img
from diffusers import (
DPMSolverMultistepScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from facexlib.parsing import init_parsing_model
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from huggingface_hub import hf_hub_download, snapshot_download
from insightface.app import FaceAnalysis
from safetensors.torch import load_file
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import normalize, resize
from eva_clip import create_model_and_transforms
from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from pulid.encoders import IDEncoder
from pulid.utils import is_torch2_available
if is_torch2_available():
from pulid.attention_processor import AttnProcessor2_0 as AttnProcessor
from pulid.attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
else:
from pulid.attention_processor import AttnProcessor, IDAttnProcessor
class PuLIDPipeline:
def __init__(self, *args, **kwargs):
super().__init__()
self.device = 'cuda'
sdxl_base_repo = 'stabilityai/stable-diffusion-xl-base-1.0'
sdxl_lightning_repo = 'ByteDance/SDXL-Lightning'
self.sdxl_base_repo = sdxl_base_repo
# load base model
unet = UNet2DConditionModel.from_config(sdxl_base_repo, subfolder='unet').to(self.device, torch.float16)
unet.load_state_dict(
load_file(
hf_hub_download(sdxl_lightning_repo, 'sdxl_lightning_4step_unet.safetensors'), device=self.device
)
)
self.hack_unet_attn_layers(unet)
self.pipe = StableDiffusionXLPipeline.from_pretrained(
sdxl_base_repo, unet=unet, torch_dtype=torch.float16, variant="fp16"
).to(self.device)
self.pipe.watermark = None
# scheduler
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
self.pipe.scheduler.config, timestep_spacing="trailing"
)
# ID adapters
self.id_adapter = IDEncoder().to(self.device)
# preprocessors
# face align and parsing
self.face_helper = FaceRestoreHelper(
upscale_factor=1,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
device=self.device,
)
self.face_helper.face_parse = None
self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
# clip-vit backbone
model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True)
model = model.visual
self.clip_vision_model = model.to(self.device)
eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
if not isinstance(eva_transform_mean, (list, tuple)):
eva_transform_mean = (eva_transform_mean,) * 3
if not isinstance(eva_transform_std, (list, tuple)):
eva_transform_std = (eva_transform_std,) * 3
self.eva_transform_mean = eva_transform_mean
self.eva_transform_std = eva_transform_std
# antelopev2
snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2')
self.app = FaceAnalysis(
name='antelopev2', root='.', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
)
self.app.prepare(ctx_id=0, det_size=(640, 640))
self.handler_ante = insightface.model_zoo.get_model('models/antelopev2/glintr100.onnx')
self.handler_ante.prepare(ctx_id=0)
gc.collect()
torch.cuda.empty_cache()
self.load_pretrain()
# other configs
self.debug_img_list = []
def hack_unet_attn_layers(self, unet):
id_adapter_attn_procs = {}
for name, _ in unet.attn_processors.items():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is not None:
id_adapter_attn_procs[name] = IDAttnProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
).to(unet.device)
else:
id_adapter_attn_procs[name] = AttnProcessor()
unet.set_attn_processor(id_adapter_attn_procs)
self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
def load_pretrain(self):
hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir='models')
ckpt_path = 'models/pulid_v1.bin'
state_dict = torch.load(ckpt_path, map_location='cpu')
state_dict_dict = {}
for k, v in state_dict.items():
module = k.split('.')[0]
state_dict_dict.setdefault(module, {})
new_k = k[len(module) + 1 :]
state_dict_dict[module][new_k] = v
for module in state_dict_dict:
print(f'loading from {module}')
getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)
def to_gray(self, img):
x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3]
x = x.repeat(1, 3, 1, 1)
return x
def get_id_embedding(self, image):
"""
Args:
image: numpy rgb image, range [0, 255]
"""
self.face_helper.clean_all()
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# get antelopev2 embedding
face_info = self.app.get(image_bgr)
if len(face_info) > 0:
face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * x['bbox'][3] - x['bbox'][1])[
-1
] # only use the maximum face
id_ante_embedding = face_info['embedding']
self.debug_img_list.append(
image[
int(face_info['bbox'][1]) : int(face_info['bbox'][3]),
int(face_info['bbox'][0]) : int(face_info['bbox'][2]),
]
)
else:
id_ante_embedding = None
# using facexlib to detect and align face
self.face_helper.read_image(image_bgr)
self.face_helper.get_face_landmarks_5(only_center_face=True)
self.face_helper.align_warp_face()
if len(self.face_helper.cropped_faces) == 0:
raise RuntimeError('facexlib align face fail')
align_face = self.face_helper.cropped_faces[0]
# incase insightface didn't detect face
if id_ante_embedding is None:
print('fail to detect face using insightface, extract embedding on align face')
id_ante_embedding = self.handler_ante.get_feat(align_face)
id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
if id_ante_embedding.ndim == 1:
id_ante_embedding = id_ante_embedding.unsqueeze(0)
# parsing
input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0
input = input.to(self.device)
parsing_out = self.face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
parsing_out = parsing_out.argmax(dim=1, keepdim=True)
bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
bg = sum(parsing_out == i for i in bg_label).bool()
white_image = torch.ones_like(input)
# only keep the face features
face_features_image = torch.where(bg, white_image, self.to_gray(input))
self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False))
# transform img before sending to eva-clip-vit
face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC)
face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std)
id_cond_vit, id_vit_hidden = self.clip_vision_model(
face_features_image, return_all_features=False, return_hidden=True, shuffle=False
)
id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)
id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1)
id_uncond = torch.zeros_like(id_cond)
id_vit_hidden_uncond = []
for layer_idx in range(0, len(id_vit_hidden)):
id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden[layer_idx]))
id_embedding = self.id_adapter(id_cond, id_vit_hidden)
uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)
# return id_embedding
return torch.cat((uncond_id_embedding, id_embedding), dim=0)
def inference(self, prompt, size, prompt_n='', image_embedding=None, id_scale=1.0, guidance_scale=1.2, steps=4):
images = self.pipe(
prompt=prompt,
negative_prompt=prompt_n,
num_images_per_prompt=size[0],
height=size[1],
width=size[2],
num_inference_steps=steps,
guidance_scale=guidance_scale,
cross_attention_kwargs={'id_embedding': image_embedding, 'id_scale': id_scale},
).images
return images