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import glob
import os
from collections import OrderedDict
import random
from typing import Optional, List
from safetensors.torch import save_file, load_file
from tqdm import tqdm
from toolkit.layers import ReductionKernel
from toolkit.stable_diffusion_model import PromptEmbeds
from toolkit.train_tools import get_torch_dtype, apply_noise_offset
import gc
from toolkit import train_tools
import torch
from .BaseSDTrainProcess import BaseSDTrainProcess, StableDiffusion
def flush():
torch.cuda.empty_cache()
gc.collect()
class RescaleConfig:
def __init__(
self,
**kwargs
):
self.from_resolution = kwargs.get('from_resolution', 512)
self.scale = kwargs.get('scale', 0.5)
self.latent_tensor_dir = kwargs.get('latent_tensor_dir', None)
self.num_latent_tensors = kwargs.get('num_latent_tensors', 1000)
self.to_resolution = kwargs.get('to_resolution', int(self.from_resolution * self.scale))
self.prompt_dropout = kwargs.get('prompt_dropout', 0.1)
class PromptEmbedsCache:
prompts: dict[str, PromptEmbeds] = {}
def __setitem__(self, __name: str, __value: PromptEmbeds) -> None:
self.prompts[__name] = __value
def __getitem__(self, __name: str) -> Optional[PromptEmbeds]:
if __name in self.prompts:
return self.prompts[__name]
else:
return None
class TrainSDRescaleProcess(BaseSDTrainProcess):
def __init__(self, process_id: int, job, config: OrderedDict):
# pass our custom pipeline to super so it sets it up
super().__init__(process_id, job, config)
self.step_num = 0
self.start_step = 0
self.device = self.get_conf('device', self.job.device)
self.device_torch = torch.device(self.device)
self.rescale_config = RescaleConfig(**self.get_conf('rescale', required=True))
self.reduce_size_fn = ReductionKernel(
in_channels=4,
kernel_size=int(self.rescale_config.from_resolution // self.rescale_config.to_resolution),
dtype=get_torch_dtype(self.train_config.dtype),
device=self.device_torch,
)
self.latent_paths: List[str] = []
self.empty_embedding: PromptEmbeds = None
def before_model_load(self):
pass
def get_latent_tensors(self):
dtype = get_torch_dtype(self.train_config.dtype)
num_to_generate = 0
# check if dir exists
if not os.path.exists(self.rescale_config.latent_tensor_dir):
os.makedirs(self.rescale_config.latent_tensor_dir)
num_to_generate = self.rescale_config.num_latent_tensors
else:
# find existing
current_tensor_list = glob.glob(os.path.join(self.rescale_config.latent_tensor_dir, "*.safetensors"))
num_to_generate = self.rescale_config.num_latent_tensors - len(current_tensor_list)
self.latent_paths = current_tensor_list
if num_to_generate > 0:
print(f"Generating {num_to_generate}/{self.rescale_config.num_latent_tensors} latent tensors")
# unload other model
self.sd.unet.to('cpu')
# load aux network
self.sd_parent = StableDiffusion(
self.device_torch,
model_config=self.model_config,
dtype=self.train_config.dtype,
)
self.sd_parent.load_model()
self.sd_parent.unet.to(self.device_torch, dtype=dtype)
# we dont need text encoder for this
del self.sd_parent.text_encoder
del self.sd_parent.tokenizer
self.sd_parent.unet.eval()
self.sd_parent.unet.requires_grad_(False)
# save current seed state for training
rng_state = torch.get_rng_state()
cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
text_embeddings = train_tools.concat_prompt_embeddings(
self.empty_embedding, # unconditional (negative prompt)
self.empty_embedding, # conditional (positive prompt)
self.train_config.batch_size,
)
torch.set_default_device(self.device_torch)
for i in tqdm(range(num_to_generate)):
dtype = get_torch_dtype(self.train_config.dtype)
# get a random seed
seed = torch.randint(0, 2 ** 32, (1,)).item()
# zero pad seed string to max length
seed_string = str(seed).zfill(10)
# set seed
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# # ger a random number of steps
timesteps_to = self.train_config.max_denoising_steps
# set the scheduler to the number of steps
self.sd.noise_scheduler.set_timesteps(
timesteps_to, device=self.device_torch
)
noise = self.sd.get_latent_noise(
pixel_height=self.rescale_config.from_resolution,
pixel_width=self.rescale_config.from_resolution,
batch_size=self.train_config.batch_size,
noise_offset=self.train_config.noise_offset,
).to(self.device_torch, dtype=dtype)
# get latents
latents = noise * self.sd.noise_scheduler.init_noise_sigma
latents = latents.to(self.device_torch, dtype=dtype)
# get random guidance scale from 1.0 to 10.0 (CFG)
guidance_scale = torch.rand(1).item() * 9.0 + 1.0
# do a timestep of 1
timestep = 1
noise_pred_target = self.sd_parent.predict_noise(
latents,
text_embeddings=text_embeddings,
timestep=timestep,
guidance_scale=guidance_scale
)
# build state dict
state_dict = OrderedDict()
state_dict['noise_pred_target'] = noise_pred_target.to('cpu', dtype=torch.float16)
state_dict['latents'] = latents.to('cpu', dtype=torch.float16)
state_dict['guidance_scale'] = torch.tensor(guidance_scale).to('cpu', dtype=torch.float16)
state_dict['timestep'] = torch.tensor(timestep).to('cpu', dtype=torch.float16)
state_dict['timesteps_to'] = torch.tensor(timesteps_to).to('cpu', dtype=torch.float16)
state_dict['seed'] = torch.tensor(seed).to('cpu', dtype=torch.float32) # must be float 32 to prevent overflow
file_name = f"{seed_string}_{i}.safetensors"
file_path = os.path.join(self.rescale_config.latent_tensor_dir, file_name)
save_file(state_dict, file_path)
self.latent_paths.append(file_path)
print("Removing parent model")
# delete parent
del self.sd_parent
flush()
torch.set_rng_state(rng_state)
if cuda_rng_state is not None:
torch.cuda.set_rng_state(cuda_rng_state)
self.sd.unet.to(self.device_torch, dtype=dtype)
def hook_before_train_loop(self):
# encode our empty prompt
self.empty_embedding = self.sd.encode_prompt("")
self.empty_embedding = self.empty_embedding.to(self.device_torch,
dtype=get_torch_dtype(self.train_config.dtype))
# Move train model encoder to cpu
if isinstance(self.sd.text_encoder, list):
for encoder in self.sd.text_encoder:
encoder.to('cpu')
encoder.eval()
encoder.requires_grad_(False)
else:
self.sd.text_encoder.to('cpu')
self.sd.text_encoder.eval()
self.sd.text_encoder.requires_grad_(False)
# self.sd.unet.to('cpu')
flush()
self.get_latent_tensors()
flush()
# end hook_before_train_loop
def hook_train_loop(self, batch):
dtype = get_torch_dtype(self.train_config.dtype)
loss_function = torch.nn.MSELoss()
# train it
# Begin gradient accumulation
self.sd.unet.train()
self.sd.unet.requires_grad_(True)
self.sd.unet.to(self.device_torch, dtype=dtype)
with torch.no_grad():
self.optimizer.zero_grad()
# pick random latent tensor
latent_path = random.choice(self.latent_paths)
latent_tensor = load_file(latent_path)
noise_pred_target = (latent_tensor['noise_pred_target']).to(self.device_torch, dtype=dtype)
latents = (latent_tensor['latents']).to(self.device_torch, dtype=dtype)
guidance_scale = (latent_tensor['guidance_scale']).item()
timestep = int((latent_tensor['timestep']).item())
timesteps_to = int((latent_tensor['timesteps_to']).item())
# seed = int((latent_tensor['seed']).item())
text_embeddings = train_tools.concat_prompt_embeddings(
self.empty_embedding, # unconditional (negative prompt)
self.empty_embedding, # conditional (positive prompt)
self.train_config.batch_size,
)
self.sd.noise_scheduler.set_timesteps(
timesteps_to, device=self.device_torch
)
denoised_target = self.sd.noise_scheduler.step(noise_pred_target, timestep, latents).prev_sample
# get the reduced latents
# reduced_pred = self.reduce_size_fn(noise_pred_target.detach())
denoised_target = self.reduce_size_fn(denoised_target.detach())
reduced_latents = self.reduce_size_fn(latents.detach())
denoised_target.requires_grad = False
self.optimizer.zero_grad()
noise_pred_train = self.sd.predict_noise(
reduced_latents,
text_embeddings=text_embeddings,
timestep=timestep,
guidance_scale=guidance_scale
)
denoised_pred = self.sd.noise_scheduler.step(noise_pred_train, timestep, reduced_latents).prev_sample
loss = loss_function(denoised_pred, denoised_target)
loss_float = loss.item()
loss.backward()
self.optimizer.step()
self.lr_scheduler.step()
self.optimizer.zero_grad()
flush()
loss_dict = OrderedDict(
{'loss': loss_float},
)
return loss_dict
# end hook_train_loop
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