File size: 5,180 Bytes
4ea5904 | 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 | import scipy
import PIL
import numpy as np
import torch
import torch.nn.functional as F
@torch.no_grad()
def project_face_embs(pipeline, face_embs):
'''
face_embs: (N, 512) normalized ArcFace embeddings
'''
arcface_token_id = pipeline.tokenizer.encode("id", add_special_tokens=False)[0]
input_ids = pipeline.tokenizer(
"photo of a id person",
truncation=True,
padding="max_length",
max_length=pipeline.tokenizer.model_max_length,
return_tensors="pt",
).input_ids.to(pipeline.device)
face_embs_padded = F.pad(face_embs, (0, pipeline.text_encoder.config.hidden_size-512), "constant", 0)
token_embs = pipeline.text_encoder(input_ids=input_ids.repeat(len(face_embs), 1), return_token_embs=True)
token_embs[input_ids==arcface_token_id] = face_embs_padded
prompt_embeds = pipeline.text_encoder(
input_ids=input_ids,
input_token_embs=token_embs
)[0]
return prompt_embeds
def project_face_embs_with_grad(encoder, tokenizer, face_embs):
"""
Same as project_face_embs but allows gradients for training.
"""
arcface_token_id = tokenizer.encode("id", add_special_tokens=False)[0]
input_ids = tokenizer(
"photo of a id person",
truncation=True,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
).input_ids.to(encoder.device)
face_embs_padded = F.pad(face_embs, (0, encoder.config.hidden_size - 512), "constant", 0)
input_ids_batch = input_ids.repeat(len(face_embs), 1)
token_embs = encoder(input_ids=input_ids_batch, return_token_embs=True)
face_embs_padded = face_embs_padded.to(token_embs.dtype)
token_embs[input_ids_batch == arcface_token_id] = face_embs_padded
prompt_embeds = encoder(
input_ids=input_ids_batch,
input_token_embs=token_embs
)[0]
return prompt_embeds
def image_align(img,
face_landmarks,
output_size=1024,
transform_size=4096,
enable_padding=True):
# Align function from FFHQ dataset pre-processing step
# https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
lm = face_landmarks
lm_eye_left = lm[36:42]
lm_eye_right = lm[42:48]
lm_mouth_outer = lm[48:60]
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
img = img.convert('RGB')
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)),
int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.LANCZOS)
quad /= shrink
qsize /= shrink
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0),
max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(np.float32(img),
((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(
1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
img = img.transform((transform_size, transform_size), PIL.Image.QUAD,
(quad + 0.5).flatten(), PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.LANCZOS)
return img
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