-
Notifications
You must be signed in to change notification settings - Fork 33
/
stabel_vition.py
196 lines (169 loc) · 7.89 KB
/
stabel_vition.py
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
import os
import shutil
import numpy as np
import torchvision.transforms as transforms
import cv2
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
import torch
from importlib import import_module
from .cldm.model import create_model
from .cldm.plms_hacked import PLMSSampler
from .utils.utils import *
from .utils.file_util import *
vition_path = node_path("ComfyUI_Seg_VITON")
cache_dir = os.path.join(vition_path,"cache")
model_load_path = os.path.join( vition_path,"checkpoints/VITONHD.ckpt")
yaml_path = os.path.join(vition_path,"configs/VITON512_COMFYUI.yaml")
def tensor2img_seg(x):
'''
x : [BS x c x H x W] or [c x H x W]
'''
if x.ndim == 3:
x = x.unsqueeze(0)
BS, C, H, W = x.shape
x = x.permute(0,2,3,1).reshape(-1, W, C).detach().cpu().numpy()
x = np.clip(x, -1, 1)
x = (x+1)/2
x = np.uint8(x*255.0)
if x.shape[-1] == 1:
x = np.concatenate([x,x,x], axis=-1)
return x
def imread(p, h, w, is_mask=False, in_inverse_mask=False, img=None):
if img is None:
img = cv2.imread(p)
if not is_mask:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (w,h))
img = (img.astype(np.float32) / 127.5) - 1.0 # [-1, 1]
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (w,h))
img = (img >= 128).astype(np.float32) # 0 or 1
img = img[:,:,None]
if in_inverse_mask:
img = 1-img
return img
class stabel_vition:
def __init__(self):
self.model = None
self.sampler = None
@classmethod
def INPUT_TYPES(cls):
return {"required":
{
"agn":("IMAGE", {"default": "","multiline": False}),
"agn_mask":("MASK", {"default": "","multiline": False}),
"cloth":("IMAGE", {"default": "","multiline": False}),
"image":("IMAGE", {"default": "","multiline": False}),
"image_densepose":("IMAGE", {"default": "","multiline": False}),
"img_H": ("INT", {"default": 512, "min": 268, "max": 2048}),
"img_W": ("INT", {"default": 384, "min": 268, "max": 2048}),
"denoise_steps": ("INT", {"default": 20, "min": 5, "max": 200}),
"batch_size": ("INT", {"default": 16, "min": 0, "max": 32, "step": 16}),
"eta": ("INT", {"default": 0, "min": 0, "max": 200}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cache": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
"repaint": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
}
}
RETURN_TYPES = ("IMAGE","BOOLEAN")
RETURN_NAMES = ("image","open")
OUTPUT_NODE = True
FUNCTION = "sample"
CATEGORY = "CXH"
def sample(self,agn,agn_mask,cloth,image,image_densepose,img_H,img_W,denoise_steps,batch_size,eta,seed,cache,repaint):
seed = str(seed)
img_fn = seed+"_img.jpg"
cloth_fn = seed+"_cloth.jpg"
#创建缓存文件夹 +缓存本地（待优化直接tensor转cv2）
mkdir(cache_dir)
agnostic_v3_2_dir = os.path.join(cache_dir,seed,"agnostic_v3_2")
mkdir(agnostic_v3_2_dir)
agnostic_v3_2_img_path = os.path.join(agnostic_v3_2_dir,img_fn)
save_tensor_image(agn,agnostic_v3_2_img_path)
agnostic_mask_dir = os.path.join(cache_dir,seed,"agnostic_mask")
mkdir(agnostic_mask_dir)
agnostic_mask_img_path = os.path.join(agnostic_mask_dir,img_fn)
save_tensor_image(agn_mask,agnostic_mask_img_path)
cloth_dir = os.path.join(cache_dir,seed,"cloth")
mkdir(cloth_dir)
cloth_img_path = os.path.join(cloth_dir,img_fn)
save_tensor_image(cloth,cloth_img_path)
image_dir = os.path.join(cache_dir,seed,"image")
mkdir(image_dir)
image_img_path = os.path.join(image_dir,img_fn)
save_tensor_image(image,image_img_path)
image_densepose_dir = os.path.join(cache_dir,seed,"image_densepose")
mkdir(image_densepose_dir)
image_densepose_img_path = os.path.join(image_densepose_dir,img_fn)
save_tensor_image(image_densepose,image_densepose_img_path)
agn = imread(agnostic_v3_2_img_path, img_H, img_W)
agn_mask = imread(agnostic_mask_img_path, img_H, img_W, is_mask=True, in_inverse_mask=True)
cloth = imread(cloth_img_path, img_H, img_W)
image = imread(image_img_path, img_H, img_W)
image_densepose = imread(image_densepose_img_path, img_H, img_W)
config = OmegaConf.load(yaml_path)
config.model.params.img_H = img_H
config.model.params.img_W = img_W
params = config.model.params
if self.model == None:
self.model = create_model(config_path=None, config=config)
self.model.load_state_dict(torch.load(model_load_path, map_location="cpu"))
self.model = self.model.cuda()
self.model.eval()
if self.sampler == None:
self.sampler = PLMSSampler(self.model)
dataset = getattr(import_module("comyui_dataset"), config.dataset_name)(
img_fn,
cloth_fn,
agn,
agn_mask,
cloth,
image,
image_densepose,
)
dataloader = DataLoader(dataset, num_workers=4, shuffle=False, batch_size=batch_size, pin_memory=True)
shape = (4, img_H//8, img_W//8)
x_sample_list =[]
for batch_idx, batch in enumerate(dataloader):
print(f"{batch_idx}/{len(dataloader)}")
z, c = self.model.get_input(batch, params.first_stage_key)
bs = z.shape[0]
c_crossattn = c["c_crossattn"][0][:bs]
if c_crossattn.ndim == 4:
c_crossattn = self.model.get_learned_conditioning(c_crossattn)
c["c_crossattn"] = [c_crossattn]
uc_cross = self.model.get_unconditional_conditioning(bs)
uc_full = {"c_concat": c["c_concat"], "c_crossattn": [uc_cross]}
uc_full["first_stage_cond"] = c["first_stage_cond"]
for k, v in batch.items():
if isinstance(v, torch.Tensor):
batch[k] = v.cuda()
self.sampler.model.batch = batch
ts = torch.full((1,), 999, device=z.device, dtype=torch.long)
start_code = self.model.q_sample(z, ts)
samples, _, _ = self.sampler.sample(
denoise_steps,
bs,
shape,
c,
x_T=start_code,
verbose=False,
eta=eta,
unconditional_conditioning=uc_full,
)
x_samples = self.model.decode_first_stage(samples)
for sample_idx, (x_sample, fn, cloth_fn) in enumerate(zip(x_samples, batch['img_fn'], batch["cloth_fn"])):
x_sample_img = tensor2img_seg(x_sample)
x_sample_list.append(x_sample_img)
if repaint:
repaint_agn_img = np.uint8((batch["image"][sample_idx].cpu().numpy()+1)/2 * 255) # [0,255]
repaint_agn_mask_img = batch["agn_mask"][sample_idx].cpu().numpy() # 0 or 1
x_sample_img = repaint_agn_img * repaint_agn_mask_img + x_sample_img * (1-repaint_agn_mask_img)
x_sample_img = np.uint8(x_sample_img)
to_path = os.path.join(cache_dir,seed,"result_"+str(sample_idx)+".jpg")
cv2.imwrite(to_path, x_sample_img[:,:,::-1])
if not cache:
shutil.rmtree(os.path.join(cache_dir,seed))
return pil2tensor(x_sample_list[0]),True