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canny2image_TRT -1-0812-得5700分.py
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canny2image_TRT -1-0812-得5700分.py
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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-----------------------------------------------"""
"""---------------------------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")
"""-----------------------------------------------"""
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()