-
Notifications
You must be signed in to change notification settings - Fork 0
/
canny2image_TRT-3-0813-加入clip提分.py
287 lines (246 loc) · 15.6 KB
/
canny2image_TRT-3-0813-加入clip提分.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
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
import os
import tensorrt as trt
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.canny import CannyDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
import onnx
from polygraphy.backend.onnx import onnx_from_path
class hackathon():
def initialize(self):
self.apply_canny = CannyDetector()
self.model = create_model('./models/cldm_v15.yaml').cpu()
self.model.load_state_dict(load_state_dict('/home/player/ControlNet/models/control_sd15_canny.pth', location='cuda'))
self.model = self.model.cuda()
self.ddim_sampler = DDIMSampler(self.model)
self.trt_logger = trt.Logger(trt.Logger.WARNING)
trt.init_libnvinfer_plugins(self.trt_logger, '')
H = 256
W = 384
"""-----------------------------------------------加载clip的engine模型-----------------------------------------------"""
if not os.path.isfile("sd_clip.engine"):
cond_stage_model = self.model.cond_stage_model
clip = cond_stage_model.transformer #
input_ids = torch.zeros((1,77),dtype=torch.int32).to("cuda") #需要特别注意这里的输入是int64
dynamic_axes = {'input_ids' : {0 : 'bs'},
'context' : {0 : 'bs'},
'pooled_output' : {0 : 'bs'}}
input_names = ["input_ids"]
output_names = ["context","pooled_output"]
print("开始转换clip为onnx")
torch.onnx.export(clip,
(input_ids),
"./sd_clip.onnx",
export_params=True,
opset_version=16,
do_constant_folding=True,
keep_initializers_as_inputs=True,
input_names = input_names,
output_names = output_names,
dynamic_axes=dynamic_axes)
os.system("trtexec --onnx=./sd_clip.onnx --saveEngine=./sd_clip.engine --useCudaGraph --builderOptimizationLevel=5")
print("clip转换完成")
with open("./sd_clip.engine", 'rb') as f:
engine_str = f.read()
clip_engine = trt.Runtime(self.trt_logger).deserialize_cuda_engine(engine_str)
clip_context = clip_engine.create_execution_context()
self.model.cond_stage_model.clip_context = clip_context
print("加载成功clip！！！")
"""---------------------------加载controlnet--------------------"""
if not os.path.isfile("sd_control_fp16.engine"):
control_model = self.model.control_model
x_in = torch.randn(2, 4, H//8, W //8, dtype=torch.float32).to("cuda")
h_in = torch.randn(2, 3, H, W, dtype=torch.float32).to("cuda")
t_in = torch.zeros(2, dtype=torch.int64).to("cuda")
c_in = torch.randn(2, 77, 768, dtype=torch.float32).to("cuda")
# controls = control_model(x=x_in, hint=h_in, timesteps=t_in, context=c_in)
output_names = []
for i in range(13):
output_names.append("out_"+ str(i))
dynamic_table = {'x_in' : {0 : 'bs'},
'h_in' : {0 : 'bs'},
't_in' : {0 : 'bs'},
'c_in' : {0 : 'bs'}}
for i in range(13):
dynamic_table[output_names[i]] = {0 : "bs"}
torch.onnx.export(control_model,
(x_in, h_in, t_in, c_in),
"./sd_control.onnx",
export_params=True,
opset_version=17,
do_constant_folding=True,
keep_initializers_as_inputs=True,
input_names = ['x_in', "h_in", "t_in", "c_in"],
output_names = output_names,
dynamic_axes = dynamic_table)
os.system("trtexec --onnx=./sd_control.onnx --saveEngine=./sd_control_fp16.engine --fp16 --verbose --useCudaGraph --optShapes=x_in:2x4x32x48,h_in:2x3x256x384,t_in:2,c_in:2x77x768 --minShapes=x_in:2x4x32x48,h_in:2x3x256x384,t_in:2,c_in:2x77x768 --maxShapes=x_in:2x4x32x48,h_in:2x3x256x384,t_in:2,c_in:2x77x768 --builderOptimizationLevel=3")
#自带cuda graph
with open("./sd_control_fp16.engine", 'rb') as f:
engine_str = f.read()
control_engine = trt.Runtime(self.trt_logger).deserialize_cuda_engine(engine_str)
control_context = control_engine.create_execution_context()
control_context.set_binding_shape(0, (2, 4, H // 8, W // 8))
control_context.set_binding_shape(1, (2, 3, H, W))
control_context.set_binding_shape(2, (2,))
control_context.set_binding_shape(3, (2, 77, 768))
self.model.control_context = control_context
print("\ncontrolnet成功启用")
"""-----------------------------------------------加载unet的engine模型-----------------------------------------------"""
if not os.path.isfile("sd_diffusion_fp16.engine"):
diffusion_model = self.model.model.diffusion_model #找对了
print("转换diffusion_model为onnx模型")
x_in = torch.randn(2, 4, H//8, W //8, dtype=torch.float32).to("cuda")
time_in = torch.zeros(2, dtype=torch.int64).to("cuda")
context_in = torch.randn(2, 77, 768, dtype=torch.float32).to("cuda")
control = []
control.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 320, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 640, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
input_names = ["x_in", "time_in", "context_in"]
for i in range(13):
input_names.append("control_"+str(i))
output_names = ["out_h"]
dynamic_table = {'x_in' : {0 : 'bs', 2 : 'H', 3 : 'W'},
'time_in' : {0 : 'bs'},
'context_in' : {0 : 'bs'}}
for i in range(3,15):
dynamic_table[input_names[i]] = {0 : "bs"}
print("开始转换diffusion_model为onnx！\n")
torch.onnx.export(diffusion_model,
(x_in, time_in, context_in, control),
"./ooo/sd_diffusion.onnx",
export_params=True,#
opset_version=17,
keep_initializers_as_inputs=True,
do_constant_folding=True,
input_names =input_names,
output_names = output_names)
#dynamic
print("转换diffusion_model为onnx成功！")
os.system("trtexec --onnx=./ooo/sd_diffusion.onnx --saveEngine=./sd_diffusion_fp16.engine --fp16 --useCudaGraph --verbose --builderOptimizationLevel=3")
#level = 4 会 killed; level = 5 会 segment default
with open("sd_diffusion_fp16.engine", 'rb') as f:
diffusion_engine_str = f.read()
diffusion_engine = trt.Runtime(self.trt_logger).deserialize_cuda_engine(diffusion_engine_str)
diffusion_context = diffusion_engine.create_execution_context()
diffusion_context.set_binding_shape(0, (2, 4, H//8, W //8))
diffusion_context.set_binding_shape(1, (2,))
diffusion_context.set_binding_shape(2, (2, 77,768))
diffusion_context.set_binding_shape(3, (2, 320, H//8, W //8))
diffusion_context.set_binding_shape(4, (2, 320, H//8, W //8))
diffusion_context.set_binding_shape(5, (2, 320, H//8, W //8))
diffusion_context.set_binding_shape(6, (2, 320, H//16, W //16))
diffusion_context.set_binding_shape(7, (2, 640, H//16, W //16))
diffusion_context.set_binding_shape(8, (2, 640, H//16, W //16))
diffusion_context.set_binding_shape(9, (2, 640, H//32, W //32))
diffusion_context.set_binding_shape(10, (2,1280, H//32, W //32))
diffusion_context.set_binding_shape(11, (2,1280, H//32, W //32))
diffusion_context.set_binding_shape(12, (2,1280, H//64, W //64))
diffusion_context.set_binding_shape(13, (2,1280, H//64, W //64))
diffusion_context.set_binding_shape(14, (2,1280, H//64, W //64))
diffusion_context.set_binding_shape(15, (2,1280, H//64, W //64))
self.model.diffusion_context = diffusion_context
print("加载成功diffusion_model的engine")
"""----------------------------------------------------------------------------------------------"""
"""------------------------添加vae的部分-----------------------"""
if not os.path.isfile("sd_vae_fp16.engine"):
model = self.model.first_stage_model
# vae调用的是decode,而导出onnx需要forward,所以这里做一个替换即可。
model.forward = model.decode
print("开始生成vae的onnx")
torch.onnx.export(
model,
(torch.randn(1, 4, 32, 48, device="cuda")),
'./sd_vae.onnx',
export_params=True,
opset_version=17,
do_constant_folding=True,
input_names=['z'],
output_names=['dec'],
dynamic_axes={'z': {0: 'B'}, 'dec': {0: 'B'}},
)
print("vae的onnx生成完成")
os.system("trtexec --onnx=./sd_vae.onnx --saveEngine=./sd_vae_fp16.engine --fp16 --useCudaGraph --optShapes=z:1x4x32x48 --builderOptimizationLevel=5")
with open("./sd_vae_fp16.engine", 'rb') as f:
engine_str = f.read()
vae_decode_engine = trt.Runtime(self.trt_logger).deserialize_cuda_engine(engine_str)
vae_decode_context = vae_decode_engine.create_execution_context()
vae_decode_context.set_binding_shape(0, (1, 4, 32,48))
self.model.vae_decode_context = vae_decode_context
print("finished vae!")
"""-----------------------------------------------"""
"""-------------------------提前开buffer----------------------"""
#controlnet:4 -> 13
#unet: 3 + 13 -> 1
#总共：4 +13 +3+1=21
self.model.control_out = []
self.model.control_out.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 320, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 640, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(2, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.eps = torch.zeros(2, 4, 32, 48, dtype=torch.float32).to("cuda")
self.model.decode_result = torch.zeros(1,3,256,384,dtype=torch.float32).to("cuda")
"""-----------------------------------------------"""
def process(self, input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold):
with torch.no_grad():
img = resize_image(HWC3(input_image), image_resolution)
H, W, C = img.shape
detected_map = self.apply_canny(img, low_threshold, high_threshold)
detected_map = HWC3(detected_map)
control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
if seed == -1:
seed = random.randint(0, 65535)
seed_everything(seed)
if config.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([n_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)
if config.save_memory:
self.model.low_vram_shift(is_diffusing=True)
ddim_steps = 10 #当小于6的时候，图像会发生质变
self.model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = self.ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
if config.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
results = [x_samples[i] for i in range(num_samples)]
return results
if __name__ == "__main__":
h = hackathon()
h.initialize()