<code>

import sys

import cv2

import numpy as np

import torch

from pulid import attention_processor as attention

from pulid.pipeline import PuLIDPipeline

from pulid.utils import resize_numpy_image_long, seed_everything

torch.set_grad_enabled(False)

class PulidInference:

def __init__(self):

self.pipeline = PuLIDPipeline()

self.pipeline.pipe.load_lora_weights("../data/data282269/", weight_name="cute.safetensors", adapter_name="cute")code>

self.pipeline.pipe.set_adapters(["cute"], adapter_weights=[0.9])

self.pipeline.pipe.load_lora_weights("../data/data283423/", weight_name="Sacred_beast_v2.3.safetensors", adapter_name="Sacred")code>

self.pipeline.pipe.set_adapters(["Sacred"], adapter_weights=[0.9])

# other params

self.DEFAULT_NEGATIVE_PROMPT = (

'flaws in the eyes, flaws in the face, flaws, lowres, non-HDRi, low quality, worst quality,'

'bad hands, bad fingers,artifacts noise, text, watermark, glitch, deformed, mutated, ugly, disfigured, '

'low resolution, partially rendered objects, deformed or partially rendered eyes, '

'deformed, deformed eyeballs, cross-eyed,blurry'

)

def run(self,*args):

id_image = args[0]

supp_images = args[1:4]

prompt, neg_prompt, scale, n_samples, seed, steps, H, W, id_scale, mode, id_mix = args[4:]

self.pipeline.debug_img_list = []

if mode == 'fidelity':

attention.NUM_ZERO = 8

attention.ORTHO = False

attention.ORTHO_v2 = True

elif mode == 'extremely style':

attention.NUM_ZERO = 16

attention.ORTHO = True

attention.ORTHO_v2 = False

else:

raise ValueError

if id_image is not None:

id_image = resize_numpy_image_long(id_image, 1024)

id_embeddings = self.pipeline.get_id_embedding(id_image)

for supp_id_image in supp_images:

if supp_id_image is not None:

supp_id_image = resize_numpy_image_long(supp_id_image, 1024)

supp_id_embeddings = self.pipeline.get_id_embedding(supp_id_image)

id_embeddings = torch.cat(

(id_embeddings, supp_id_embeddings if id_mix else supp_id_embeddings[:, :5]), dim=1

)

else:

id_embeddings = None

seed_everything(seed)

ims = []

for _ in range(n_samples):

img = self.pipeline.inference(prompt, (1, H, W), neg_prompt, id_embeddings, id_scale, scale, steps)[0]

ims.append(np.array(img))

return ims, self.pipeline.debug_img_list

def pulid_inference(self,face_image, prompt_in,neg_prompt_in,supp_images=None,seed = 55):

scale = 1.2

n_samples = 8

steps = 4

H, W = 1024, 768

id_scale = 0.99

mode = 'extremely style' #'fidelity', 'extremely style' (full body:1.2),slim,simple clothes, black dress,

id_mix = False #MG_ip,pixar,chibi,

prompt='MG_ip,pixar,chibi,portrait,cinematic, best quality,' + prompt_in #cinematic film, cinematic photo, realistic, portraitcode>

neg_prompt = self.DEFAULT_NEGATIVE_PROMPT + neg_prompt_in

# face_image = cv2.imread('../y.jpg')

# cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB, face_image)

#supp_image2 = cv2.imread('../fei3.jpg')

inps = [

face_image,

None,

None,

None,

prompt,

neg_prompt,

scale,

n_samples,

seed,

steps,

H,

W,

id_scale,

mode,

id_mix,

]

ims, debug_img = self.run(*inps)

return ims

if __name__ == '__main__':

pulid = PulidInference()

face_image = cv2.imread('./input3.jpg')

ims = pulid.pulid_inference(face_image, 'BJ_Sacred_beast, 1 girl, white hanfu dress,')

for i in range(len(ims)):

cv2.cvtColor(ims[i], cv2.COLOR_BGR2RGB, ims[i])

cv2.imwrite(f'plu-b-{ i}.jpg', ims[i])

结果：

6. 资源链接