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import re
from typing import List
import numpy as np
import torch
from torch.cuda import nvtx
from polygraphy.logger import G_LOGGER
import gradio as gr
from modules import script_callbacks, sd_unet, devices, scripts, shared
import ui_trt
from utilities import Engine
from model_manager import TRT_MODEL_DIR, modelmanager
from datastructures import ModelType
from scripts.lora import apply_loras
G_LOGGER.module_severity = G_LOGGER.ERROR
class TrtUnetOption(sd_unet.SdUnetOption):
def __init__(self, name: str, filename: List[dict]):
self.label = f"[TRT] {name}"
self.model_name = name
self.configs = filename
def create_unet(self):
return TrtUnet(self.model_name, self.configs)
class TrtUnet(sd_unet.SdUnet):
def __init__(self, model_name: str, configs: List[dict], *args, **kwargs):
super().__init__(*args, **kwargs)
self.stream = None
self.model_name = model_name
self.configs = configs
self.profile_idx = 0
self.loaded_config = None
self.engine_vram_req = 0
self.refitted_keys = set()
self.engine = None
def forward(
self,
x: torch.Tensor,
timesteps: torch.Tensor,
context: torch.Tensor,
*args,
**kwargs,
) -> torch.Tensor:
nvtx.range_push("forward")
feed_dict = {
"sample": x.float(),
"timesteps": timesteps.float(),
"encoder_hidden_states": context.float(),
}
if "y" in kwargs:
feed_dict["y"] = kwargs["y"].float()
tmp = torch.empty(
self.engine_vram_req, dtype=torch.uint8, device=devices.device
)
self.engine.context.device_memory = tmp.data_ptr()
self.cudaStream = torch.cuda.current_stream().cuda_stream
self.engine.allocate_buffers(feed_dict)
out = self.engine.infer(feed_dict, self.cudaStream)["latent"]
nvtx.range_pop()
return out
def apply_loras(self, refit_dict: dict):
if not self.refitted_keys.issubset(set(refit_dict.keys())):
# Need to ensure that weights that have been modified before and are not present anymore are reset.
self.refitted_keys = set()
self.switch_engine()
self.engine.refit_from_dict(refit_dict, is_fp16=True)
self.refitted_keys = set(refit_dict.keys())
def switch_engine(self):
self.loaded_config = self.configs[self.profile_idx]
self.engine.reset(os.path.join(TRT_MODEL_DIR, self.loaded_config["filepath"]))
self.activate()
def activate(self):
self.loaded_config = self.configs[self.profile_idx]
if self.engine is None:
self.engine = Engine(
os.path.join(TRT_MODEL_DIR, self.loaded_config["filepath"])
)
self.engine.load()
print(f"\nLoaded Profile: {self.profile_idx}")
print(self.engine)
self.engine_vram_req = self.engine.engine.device_memory_size
self.engine.activate(True)
def deactivate(self):
del self.engine
class TensorRTScript(scripts.Script):
def __init__(self) -> None:
self.loaded_model = None
self.lora_hash = ""
self.update_lora = False
self.lora_refit_dict = {}
self.idx = None
self.hr_idx = None
self.torch_unet = False
def title(self):
return "TensorRT"
def show(self, is_img2img):
return scripts.AlwaysVisible
def setup(self, p, *args):
return super().setup(p, *args)
def before_process(self, p, *args): # 1
# Check divisibilty
if p.width % 64 or p.height % 64:
gr.Error("Target resolution must be divisible by 64 in both dimensions.")
if self.is_img2img:
return
if p.enable_hr:
hr_w = int(p.width * p.hr_scale)
hr_h = int(p.height * p.hr_scale)
if hr_w % 64 or hr_h % 64:
gr.Error(
"HIRES Fix resolution must be divisible by 64 in both dimensions. Please change the upscale factor or disable HIRES Fix."
)
def get_profile_idx(self, p, model_name: str, model_type: ModelType) -> (int, int):
best_hr = None
if self.is_img2img:
hr_scale = 1
else:
hr_scale = p.hr_scale if p.enable_hr else 1
(
valid_models,
distances,
idx,
) = modelmanager.get_valid_models(
model_name,
p.width,
p.height,
p.batch_size,
77, # model_type
) # TODO: max_embedding, just ignore?
if len(valid_models) == 0:
gr.Error(
f"""No valid profile found for ({model_name}) LOWRES. Please go to the TensorRT tab and generate an engine with the necessary profile.
If using hires.fix, you need an engine for both the base and upscaled resolutions. Otherwise, use the default (torch) U-Net."""
)
return None, None
best = idx[np.argmin(distances)]
best_hr = best
if hr_scale != 1:
hr_w = int(p.width * p.hr_scale)
hr_h = int(p.height * p.hr_scale)
valid_models_hr, distances_hr, idx_hr = modelmanager.get_valid_models(
model_name,
hr_w,
hr_h,
p.batch_size,
77, # model_type
) # TODO: max_embedding
if len(valid_models_hr) == 0:
gr.Error(
f"""No valid profile found for ({model_name}) HIRES. Please go to the TensorRT tab and generate an engine with the necessary profile.
If using hires.fix, you need an engine for both the base and upscaled resolutions. Otherwise, use the default (torch) U-Net."""
)
merged_idx = [i for i, id in enumerate(idx) if id in idx_hr]
if len(merged_idx) == 0:
gr.Warning(
"No model available for both ({}) LOWRES ({}x{}) and HIRES ({}x{}). This will slow-down inference.".format(
model_name, p.width, p.height, hr_w, hr_h
)
)
return None, None
else:
_distances = [distances[i] for i in merged_idx]
best_hr = merged_idx[np.argmin(_distances)]
best = best_hr
return best, best_hr
def get_loras(self, p):
lora_pathes = []
lora_scales = []
# get lora from prompt
_prompt = p.prompt
extra_networks = re.findall("\<(.*?)\>", _prompt)
loras = [net for net in extra_networks if net.startswith("lora")]
# Avoid that extra networks will be loaded
for lora in loras:
_prompt = _prompt.replace(f"<{lora}>", "")
p.prompt = _prompt
# check if lora config has changes
if self.lora_hash != "".join(loras):
self.lora_hash = "".join(loras)
self.update_lora = True
if self.lora_hash == "":
self.lora_refit_dict = {}
return
else:
return
# Get pathes
print("Apllying LoRAs: " + str(loras))
available = modelmanager.available_loras()
for lora in loras:
lora_name, lora_scale = lora.split(":")[1:]
lora_scales.append(float(lora_scale))
if lora_name not in available:
raise Exception(
f"Please export the LoRA checkpoint {lora_name} first from the TensorRT LoRA tab"
)
lora_pathes.append(
available[lora_name]
)
# Merge lora refit dicts
base_name, base_path = modelmanager.get_onnx_path(p.sd_model_name)
refit_dict = apply_loras(base_path, lora_pathes, lora_scales)
self.lora_refit_dict = refit_dict
def process(self, p, *args):
# before unet_init
sd_unet_option = sd_unet.get_unet_option()
if sd_unet_option is None:
return
if not sd_unet_option.model_name == p.sd_model_name:
gr.Error(
"""Selected torch model ({}) does not match the selected TensorRT U-Net ({}).
Please ensure that both models are the same or select Automatic from the SD UNet dropdown.""".format(
p.sd_model_name, sd_unet_option.model_name
)
)
self.idx, self.hr_idx = self.get_profile_idx(p, p.sd_model_name, ModelType.UNET)
self.torch_unet = self.idx is None or self.hr_idx is None
try:
if not self.torch_unet:
self.get_loras(p)
except Exception as e:
gr.Error(e)
raise e
self.apply_unet(sd_unet_option)
def apply_unet(self, sd_unet_option):
if (
sd_unet_option == sd_unet.current_unet_option
and sd_unet.current_unet is not None
and not self.torch_unet
):
return
if sd_unet.current_unet is not None:
sd_unet.current_unet.deactivate()
if self.torch_unet:
gr.Warning("Enabling PyTorch fallback as no engine was found.")
sd_unet.current_unet = None
sd_unet.current_unet_option = sd_unet_option
shared.sd_model.model.diffusion_model.to(devices.device)
return
else:
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
if self.lora_refit_dict:
self.update_lora = True
sd_unet.current_unet = sd_unet_option.create_unet()
sd_unet.current_unet.profile_idx = self.idx
sd_unet.current_unet.option = sd_unet_option
sd_unet.current_unet_option = sd_unet_option
print(f"Activating unet: {sd_unet.current_unet.option.label}")
sd_unet.current_unet.activate()
def process_batch(self, p, *args, **kwargs):
# Called for each batch count
if self.torch_unet:
return super().process_batch(p, *args, **kwargs)
if sd_unet.current_unet is not None and self.idx != sd_unet.current_unet.profile_idx:
sd_unet.current_unet.profile_idx = self.idx
sd_unet.current_unet.switch_engine()
def before_hr(self, p, *args):
if self.idx != self.hr_idx:
sd_unet.current_unet.profile_idx = self.hr_idx
sd_unet.current_unet.switch_engine()
return super().before_hr(p, *args) # 4 (Only when HR starts.....)
def after_extra_networks_activate(self, p, *args, **kwargs):
if self.update_lora and not self.torch_unet:
self.update_lora = False
sd_unet.current_unet.apply_loras(self.lora_refit_dict)
def list_unets(l):
model = modelmanager.available_models()
for k, v in model.items():
if v[0]["config"].lora:
continue
label = "{} ({})".format(k, v[0]["base_model"]) if v[0]["config"].lora else k
l.append(TrtUnetOption(label, v))
script_callbacks.on_list_unets(list_unets)
script_callbacks.on_ui_tabs(ui_trt.on_ui_tabs)
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