-
Notifications
You must be signed in to change notification settings - Fork 50
/
pipelines.py
212 lines (191 loc) · 8.5 KB
/
pipelines.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
211
212
import torch
from libs.base_utils import do_resize_content
from imagedream.ldm.util import (
instantiate_from_config,
get_obj_from_str,
)
from omegaconf import OmegaConf
from PIL import Image
import PIL
import rembg
class TwoStagePipeline(object):
def __init__(
self,
stage1_model_config,
stage2_model_config,
stage1_sampler_config,
stage2_sampler_config,
device="cuda",
dtype=torch.float16,
resize_rate=1,
) -> None:
"""
only for two stage generate process.
- the first stage was condition on single pixel image, gererate multi-view pixel image, based on the v2pp config
- the second stage was condition on multiview pixel image generated by the first stage, generate the final image, based on the stage2-test config
"""
self.resize_rate = resize_rate
self.stage1_model = instantiate_from_config(OmegaConf.load(stage1_model_config.config).model)
self.stage1_model.load_state_dict(torch.load(stage1_model_config.resume, map_location="cpu"), strict=False)
self.stage1_model = self.stage1_model.to(device).to(dtype)
self.stage2_model = instantiate_from_config(OmegaConf.load(stage2_model_config.config).model)
sd = torch.load(stage2_model_config.resume, map_location="cpu")
self.stage2_model.load_state_dict(sd, strict=False)
self.stage2_model = self.stage2_model.to(device).to(dtype)
self.stage1_model.device = device
self.stage2_model.device = device
self.device = device
self.dtype = dtype
self.stage1_sampler = get_obj_from_str(stage1_sampler_config.target)(
self.stage1_model, device=device, dtype=dtype, **stage1_sampler_config.params
)
self.stage2_sampler = get_obj_from_str(stage2_sampler_config.target)(
self.stage2_model, device=device, dtype=dtype, **stage2_sampler_config.params
)
def stage1_sample(
self,
pixel_img,
prompt="3D assets",
neg_texts="uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear.",
step=50,
scale=5,
ddim_eta=0.0,
):
if type(pixel_img) == str:
pixel_img = Image.open(pixel_img)
if isinstance(pixel_img, Image.Image):
if pixel_img.mode == "RGBA":
background = Image.new('RGBA', pixel_img.size, (0, 0, 0, 0))
pixel_img = Image.alpha_composite(background, pixel_img).convert("RGB")
else:
pixel_img = pixel_img.convert("RGB")
else:
raise
uc = self.stage1_sampler.model.get_learned_conditioning([neg_texts]).to(self.device)
stage1_images = self.stage1_sampler.i2i(
self.stage1_sampler.model,
self.stage1_sampler.size,
prompt,
uc=uc,
sampler=self.stage1_sampler.sampler,
ip=pixel_img,
step=step,
scale=scale,
batch_size=self.stage1_sampler.batch_size,
ddim_eta=ddim_eta,
dtype=self.stage1_sampler.dtype,
device=self.stage1_sampler.device,
camera=self.stage1_sampler.camera,
num_frames=self.stage1_sampler.num_frames,
pixel_control=(self.stage1_sampler.mode == "pixel"),
transform=self.stage1_sampler.image_transform,
offset_noise=self.stage1_sampler.offset_noise,
)
stage1_images = [Image.fromarray(img) for img in stage1_images]
stage1_images.pop(self.stage1_sampler.ref_position)
return stage1_images
def stage2_sample(self, pixel_img, stage1_images, scale=5, step=50):
if type(pixel_img) == str:
pixel_img = Image.open(pixel_img)
if isinstance(pixel_img, Image.Image):
if pixel_img.mode == "RGBA":
background = Image.new('RGBA', pixel_img.size, (0, 0, 0, 0))
pixel_img = Image.alpha_composite(background, pixel_img).convert("RGB")
else:
pixel_img = pixel_img.convert("RGB")
else:
raise
stage2_images = self.stage2_sampler.i2iStage2(
self.stage2_sampler.model,
self.stage2_sampler.size,
"3D assets",
self.stage2_sampler.uc,
self.stage2_sampler.sampler,
pixel_images=stage1_images,
ip=pixel_img,
step=step,
scale=scale,
batch_size=self.stage2_sampler.batch_size,
ddim_eta=0.0,
dtype=self.stage2_sampler.dtype,
device=self.stage2_sampler.device,
camera=self.stage2_sampler.camera,
num_frames=self.stage2_sampler.num_frames,
pixel_control=(self.stage2_sampler.mode == "pixel"),
transform=self.stage2_sampler.image_transform,
offset_noise=self.stage2_sampler.offset_noise,
)
stage2_images = [Image.fromarray(img) for img in stage2_images]
return stage2_images
def set_seed(self, seed):
self.stage1_sampler.seed = seed
self.stage2_sampler.seed = seed
def __call__(self, pixel_img, prompt="3D assets", scale=5, step=50):
pixel_img = do_resize_content(pixel_img, self.resize_rate)
stage1_images = self.stage1_sample(pixel_img, prompt, scale=scale, step=step)
stage2_images = self.stage2_sample(pixel_img, stage1_images, scale=scale, step=step)
return {
"ref_img": pixel_img,
"stage1_images": stage1_images,
"stage2_images": stage2_images,
}
rembg_session = rembg.new_session()
def expand_to_square(image, bg_color=(0, 0, 0, 0)):
# expand image to 1:1
width, height = image.size
if width == height:
return image
new_size = (max(width, height), max(width, height))
new_image = Image.new("RGBA", new_size, bg_color)
paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
new_image.paste(image, paste_position)
return new_image
def remove_background(
image: PIL.Image.Image,
rembg_session = None,
force: bool = False,
**rembg_kwargs,
) -> PIL.Image.Image:
do_remove = True
if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
# explain why current do not rm bg
print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
background = Image.new("RGBA", image.size, (0, 0, 0, 0))
image = Image.alpha_composite(background, image)
do_remove = False
do_remove = do_remove or force
if do_remove:
image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
return image
def do_resize_content(original_image: Image, scale_rate):
# resize image content wile retain the original image size
if scale_rate != 1:
# Calculate the new size after rescaling
new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
# Resize the image while maintaining the aspect ratio
resized_image = original_image.resize(new_size)
# Create a new image with the original size and black background
padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
padded_image.paste(resized_image, paste_position)
return padded_image
else:
return original_image
def add_background(image, bg_color=(255, 255, 255)):
# given an RGBA image, alpha channel is used as mask to add background color
background = Image.new("RGBA", image.size, bg_color)
return Image.alpha_composite(background, image)
def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
"""
input image is a pil image in RGBA, return RGB image
"""
print(background_choice)
if background_choice == "Alpha as mask":
background = Image.new("RGBA", image.size, (0, 0, 0, 0))
image = Image.alpha_composite(background, image)
else:
image = remove_background(image, rembg_session, force_remove=True)
image = do_resize_content(image, foreground_ratio)
image = expand_to_square(image)
image = add_background(image, backgroud_color)
return image.convert("RGB")