-
Notifications
You must be signed in to change notification settings - Fork 84
/
inference_IMAGdressing_controlnetpose.py
210 lines (175 loc) · 8.19 KB
/
inference_IMAGdressing_controlnetpose.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
197
198
199
200
201
202
203
204
205
206
207
208
209
210
import diffusers
from dressing_sd.pipelines.IMAGDressing_v1_pipeline_controlnet import IMAGDressing_v1
import os
import sys
import torch
from PIL import Image
from diffusers import ControlNetModel, UNet2DConditionModel, \
AutoencoderKL, DDIMScheduler
from torchvision import transforms
from transformers import CLIPImageProcessor
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from adapter.attention_processor import CacheAttnProcessor2_0, RefSAttnProcessor2_0, CAttnProcessor2_0
import argparse
from adapter.resampler import Resampler
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def resize_img(input_image, max_side=640, min_side=512, size=None,
pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
w, h = input_image.size
ratio = min_side / min(h, w)
w, h = round(ratio * w), round(ratio * h)
ratio = max_side / max(h, w)
input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
input_image = input_image.resize([w_resize_new, h_resize_new], mode)
return input_image
def prepare(args):
generator = torch.Generator(device=args.device).manual_seed(42)
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(dtype=torch.float16, device=args.device)
tokenizer = CLIPTokenizer.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="text_encoder").to(
dtype=torch.float16, device=args.device)
image_encoder = CLIPVisionModelWithProjection.from_pretrained("h94/IP-Adapter", subfolder="models/image_encoder").to(
dtype=torch.float16, device=args.device)
unet = UNet2DConditionModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="unet").to(
dtype=torch.float16,
device=args.device)
# load ipa weight
image_proj = Resampler(
dim=unet.config.cross_attention_dim,
depth=4,
dim_head=64,
heads=12,
num_queries=16,
embedding_dim=image_encoder.config.hidden_size,
output_dim=unet.config.cross_attention_dim,
ff_mult=4
)
image_proj = image_proj.to(dtype=torch.float16, device=args.device)
# set attention processor
attn_procs = {}
st = unet.state_dict()
for name in unet.attn_processors.keys():
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 None:
attn_procs[name] = RefSAttnProcessor2_0(name, hidden_size)
else:
attn_procs[name] = CAttnProcessor2_0(name, hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
unet.set_attn_processor(attn_procs)
adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
adapter_modules = adapter_modules.to(dtype=torch.float16, device=args.device)
del st
ref_unet = UNet2DConditionModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="unet").to(
dtype=torch.float16,
device=args.device)
ref_unet.set_attn_processor(
{name: CacheAttnProcessor2_0() for name in ref_unet.attn_processors.keys()}) # set cache
ref_unet.set_attn_processor(
{name: CacheAttnProcessor2_0() for name in ref_unet.attn_processors.keys()}) # set cache
# weights load
model_sd = torch.load(args.model_ckpt, map_location="cpu")["module"]
ref_unet_dict = {}
unet_dict = {}
image_proj_dict = {}
adapter_modules_dict = {}
for k in model_sd.keys():
if k.startswith("ref_unet"):
ref_unet_dict[k.replace("ref_unet.", "")] = model_sd[k]
elif k.startswith("unet"):
unet_dict[k.replace("unet.", "")] = model_sd[k]
elif k.startswith("proj"):
image_proj_dict[k.replace("proj.", "")] = model_sd[k]
elif k.startswith("adapter_modules") and 'ref' in k:
adapter_modules_dict[k.replace("adapter_modules.", "")] = model_sd[k]
else:
print(k)
ref_unet.load_state_dict(ref_unet_dict)
image_proj.load_state_dict(image_proj_dict)
adapter_modules.load_state_dict(adapter_modules_dict, strict=False)
noise_scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
)
control_net_openpose = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose",
torch_dtype=torch.float16).to(device=args.device)
pipe = IMAGDressing_v1(vae=vae, reference_unet=ref_unet, unet=unet, tokenizer=tokenizer,
text_encoder=text_encoder, controlnet=control_net_openpose, image_encoder=image_encoder,
ImgProj=image_proj, scheduler=noise_scheduler,
safety_checker=StableDiffusionSafetyChecker,
feature_extractor=CLIPImageProcessor)
return pipe, generator
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='IMAGDressing_v1')
parser.add_argument('--model_ckpt',
default="ckpt/IMAGDressing-v1_512.pt",
type=str)
parser.add_argument('--cloth_path', type=str, required=True)
parser.add_argument('--pose_path', type=str, required=True)
parser.add_argument('--output_path', type=str, default="./output_sd_control")
parser.add_argument('--device', type=str, default="cuda:0")
args = parser.parse_args()
# svae path
output_path = args.output_path
if not os.path.exists(output_path):
os.makedirs(output_path)
pipe, generator = prepare(args)
print('====================== pipe load finish ===================')
num_samples = 1
clip_image_processor = CLIPImageProcessor()
img_transform = transforms.Compose([
transforms.Resize([640, 512], interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
])
prompt = 'A beautiful woman'
prompt = prompt + ', best quality, high quality'
null_prompt = ''
negative_prompt = 'bare, naked, nude, undressed, monochrome, lowres, bad anatomy, worst quality, low quality'
clothes_img = Image.open(args.cloth_path).convert("RGB")
clothes_img = resize_img(clothes_img)
vae_clothes = img_transform(clothes_img).unsqueeze(0)
ref_clip_image = clip_image_processor(images=clothes_img, return_tensors="pt").pixel_values
pose_image = diffusers.utils.load_image(args.pose_path)
output = pipe(
ref_image=vae_clothes,
prompt=prompt,
ref_clip_image=ref_clip_image,
pose_image=pose_image,
null_prompt=null_prompt,
negative_prompt=negative_prompt,
width=512,
height=640,
num_images_per_prompt=num_samples,
guidance_scale=7.5,
image_scale=1.0,
generator=generator,
num_inference_steps=50,
).images
save_output = []
save_output.append(output[0])
save_output.insert(0, clothes_img.resize((512, 640), Image.BICUBIC))
grid = image_grid(save_output, 1, 2)
grid.save(
output_path + '/' + args.cloth_path.split("/")[-1])