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canny2image_TRT copy.py
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canny2image_TRT copy.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
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)
H = 256
W = 384
# """-----------------------------------------------加载clip的engine模型-----------------------------------------------"""
# 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)
# print("clip转换完成")
# os.system("trtexec --onnx=./sd_clip.onnx --saveEngine=./sd_clip_fp32.plan --builderOptimizationLevel=5")
# self.trt_logger = trt.Logger(trt.Logger.WARNING) #创建logger记录
# trt.init_libnvinfer_plugins(self.trt_logger, '') #初始化插件库
# with open("./sd_clip_fp32.plan", 'rb') as f:
# engine_str = f.read() #读取字节1
# clip_engine = trt.Runtime(self.trt_logger).deserialize_cuda_engine(engine_str) #字节序列恢复为对象
# clip_context = clip_engine.create_execution_context() #创建推理的上下文context
# #这里加载进去context
# self.model.cond_stage_model.clip_context = clip_context #替换模型的上下文，与engine是1对多
# print("加载成功clip的engine")
# """-----------------------------------------------"""
"""-----------------------------------------------加载controlnet的engine模型-----------------------------------------------"""
control_model = self.model.control_model
x_in = torch.randn(1, 4, H//8, W //8, dtype=torch.float32).to("cuda")
h_in = torch.randn(1, 3, H, W, dtype=torch.float32).to("cuda")
t_in = torch.zeros(1, dtype=torch.int64).to("cuda")
c_in = torch.randn(1, 77, 768, dtype=torch.float32).to("cuda")
output_names = []
for i in range(13): #这里还不知道为什么是13,但确实是13个
output_names.append("out_"+ str(i))
torch.onnx.export(control_model,
(x_in, h_in, t_in, c_in),
"./sd_control.onnx",
export_params=True,
opset_version=16,
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)
# --optShapes=x_in:1x4x32x48,h_in:1x3x256x384,t_in:1,c_in:1x77x768
print("controlnet成功转为onnx模型")
os.system("trtexec --onnx=./sd_control.onnx --saveEngine=./sd_control_fp16.engine --fp16 --builderOptimizationLevel=5")
#修改 shape调到固定最小
self.trt_logger = trt.Logger(trt.Logger.WARNING)
trt.init_libnvinfer_plugins(self.trt_logger, '')
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, (1, 4, H // 8, W // 8))
# control_context.set_binding_shape(1, (1, 3, H, W))
# control_context.set_binding_shape(2, (1,))
# control_context.set_binding_shape(3, (1, 77, 768))
self.model.control_context = control_context
print("加载成功controlnet的engine")
"""-----------------------------------------------"""
"""-----------------------------------------------加载unet的engine模型-----------------------------------------------"""
diffusion_model = self.model.model.diffusion_model #找对了
print("转换diffusion_model为onnx模型")
x_in = torch.randn(1, 4, H//8, W //8, dtype=torch.float32).to("cuda")
time_in = torch.zeros(1, dtype=torch.int64).to("cuda")
context_in = torch.randn(1, 77, 768, dtype=torch.float32).to("cuda")
control = []
control.append(torch.randn(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 320, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 640, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
control.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
input_names = ["x_in", "time_in", "context_in","crotrol"]
output_names = ["out_h"]
print("开始转换diffusion_model为onnx！\n")
torch.onnx.export(diffusion_model,
(x_in, time_in, context_in, control),
"./sd_diffusion.onnx",
export_params=True,#
opset_version=16,
keep_initializers_as_inputs=True,
do_constant_folding=True,
input_names =input_names,
output_names = output_names)
print("转换diffusion_model为onnx成功！")
os.system("trtexec --onnx=./sd_diffusion.onnx --saveEngine=./sd_diffusion_fp16.engine --fp16 --builderOptimizationLevel=5")
self.trt_logger = trt.Logger(trt.Logger.WARNING)
trt.init_libnvinfer_plugins(self.trt_logger, '')
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()
self.model.diffusion_context = diffusion_context
print("加载成功diffusion_model的engine")
"""-----------------------------------------------"""
"""-------------------------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(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 320, H//8, W //8, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 320, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 640, H//16, W //16, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 640, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//32, W //32, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.control_out.append(torch.randn(1, 1280, H//64, W //64, dtype=torch.float32).to("cuda"))
self.model.eps = torch.zeros(1, 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
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