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import copy
import random
from collections import OrderedDict
import os
from contextlib import nullcontext
from typing import Optional, Union, List
from torch.utils.data import ConcatDataset, DataLoader
from toolkit.config_modules import ReferenceDatasetConfig
from toolkit.data_loader import PairedImageDataset
from toolkit.prompt_utils import concat_prompt_embeds, split_prompt_embeds
from toolkit.stable_diffusion_model import StableDiffusion, PromptEmbeds
from toolkit.train_tools import get_torch_dtype, apply_snr_weight
import gc
from toolkit import train_tools
import torch
from jobs.process import BaseSDTrainProcess
import random
from toolkit.basic import value_map
def flush():
torch.cuda.empty_cache()
gc.collect()
class ReferenceSliderConfig:
def __init__(self, **kwargs):
self.additional_losses: List[str] = kwargs.get('additional_losses', [])
self.weight_jitter: float = kwargs.get('weight_jitter', 0.0)
self.datasets: List[ReferenceDatasetConfig] = [ReferenceDatasetConfig(**d) for d in kwargs.get('datasets', [])]
class ImageReferenceSliderTrainerProcess(BaseSDTrainProcess):
sd: StableDiffusion
data_loader: DataLoader = None
def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
super().__init__(process_id, job, config, **kwargs)
self.prompt_txt_list = None
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.slider_config = ReferenceSliderConfig(**self.get_conf('slider', {}))
def load_datasets(self):
if self.data_loader is None:
print(f"Loading datasets")
datasets = []
for dataset in self.slider_config.datasets:
print(f" - Dataset: {dataset.pair_folder}")
config = {
'path': dataset.pair_folder,
'size': dataset.size,
'default_prompt': dataset.target_class,
'network_weight': dataset.network_weight,
'pos_weight': dataset.pos_weight,
'neg_weight': dataset.neg_weight,
'pos_folder': dataset.pos_folder,
'neg_folder': dataset.neg_folder,
}
image_dataset = PairedImageDataset(config)
datasets.append(image_dataset)
concatenated_dataset = ConcatDataset(datasets)
self.data_loader = DataLoader(
concatenated_dataset,
batch_size=self.train_config.batch_size,
shuffle=True,
num_workers=2
)
def before_model_load(self):
pass
def hook_before_train_loop(self):
self.sd.vae.eval()
self.sd.vae.to(self.device_torch)
self.load_datasets()
pass
def hook_train_loop(self, batch):
with torch.no_grad():
imgs, prompts, network_weights = batch
network_pos_weight, network_neg_weight = network_weights
if isinstance(network_pos_weight, torch.Tensor):
network_pos_weight = network_pos_weight.item()
if isinstance(network_neg_weight, torch.Tensor):
network_neg_weight = network_neg_weight.item()
# get an array of random floats between -weight_jitter and weight_jitter
loss_jitter_multiplier = 1.0
weight_jitter = self.slider_config.weight_jitter
if weight_jitter > 0.0:
jitter_list = random.uniform(-weight_jitter, weight_jitter)
orig_network_pos_weight = network_pos_weight
network_pos_weight += jitter_list
network_neg_weight += (jitter_list * -1.0)
# penalize the loss for its distance from network_pos_weight
# a jitter_list of abs(3.0) on a weight of 5.0 is a 60% jitter
# so the loss_jitter_multiplier needs to be 0.4
loss_jitter_multiplier = value_map(abs(jitter_list), 0.0, weight_jitter, 1.0, 0.0)
# if items in network_weight list are tensors, convert them to floats
dtype = get_torch_dtype(self.train_config.dtype)
imgs: torch.Tensor = imgs.to(self.device_torch, dtype=dtype)
# split batched images in half so left is negative and right is positive
negative_images, positive_images = torch.chunk(imgs, 2, dim=3)
positive_latents = self.sd.encode_images(positive_images)
negative_latents = self.sd.encode_images(negative_images)
height = positive_images.shape[2]
width = positive_images.shape[3]
batch_size = positive_images.shape[0]
if self.train_config.gradient_checkpointing:
# may get disabled elsewhere
self.sd.unet.enable_gradient_checkpointing()
noise_scheduler = self.sd.noise_scheduler
optimizer = self.optimizer
lr_scheduler = self.lr_scheduler
self.sd.noise_scheduler.set_timesteps(
self.train_config.max_denoising_steps, device=self.device_torch
)
timesteps = torch.randint(0, self.train_config.max_denoising_steps, (1,), device=self.device_torch)
timesteps = timesteps.long()
# get noise
noise_positive = self.sd.get_latent_noise(
pixel_height=height,
pixel_width=width,
batch_size=batch_size,
noise_offset=self.train_config.noise_offset,
).to(self.device_torch, dtype=dtype)
noise_negative = noise_positive.clone()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_positive_latents = noise_scheduler.add_noise(positive_latents, noise_positive, timesteps)
noisy_negative_latents = noise_scheduler.add_noise(negative_latents, noise_negative, timesteps)
noisy_latents = torch.cat([noisy_positive_latents, noisy_negative_latents], dim=0)
noise = torch.cat([noise_positive, noise_negative], dim=0)
timesteps = torch.cat([timesteps, timesteps], dim=0)
network_multiplier = [network_pos_weight * 1.0, network_neg_weight * -1.0]
self.optimizer.zero_grad()
noisy_latents.requires_grad = False
# if training text encoder enable grads, else do context of no grad
with torch.set_grad_enabled(self.train_config.train_text_encoder):
# fix issue with them being tuples sometimes
prompt_list = []
for prompt in prompts:
if isinstance(prompt, tuple):
prompt = prompt[0]
prompt_list.append(prompt)
conditional_embeds = self.sd.encode_prompt(prompt_list).to(self.device_torch, dtype=dtype)
conditional_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
# if self.model_config.is_xl:
# # todo also allow for setting this for low ram in general, but sdxl spikes a ton on back prop
# network_multiplier_list = network_multiplier
# noisy_latent_list = torch.chunk(noisy_latents, 2, dim=0)
# noise_list = torch.chunk(noise, 2, dim=0)
# timesteps_list = torch.chunk(timesteps, 2, dim=0)
# conditional_embeds_list = split_prompt_embeds(conditional_embeds)
# else:
network_multiplier_list = [network_multiplier]
noisy_latent_list = [noisy_latents]
noise_list = [noise]
timesteps_list = [timesteps]
conditional_embeds_list = [conditional_embeds]
losses = []
# allow to chunk it out to save vram
for network_multiplier, noisy_latents, noise, timesteps, conditional_embeds in zip(
network_multiplier_list, noisy_latent_list, noise_list, timesteps_list, conditional_embeds_list
):
with self.network:
assert self.network.is_active
self.network.multiplier = network_multiplier
noise_pred = self.sd.predict_noise(
latents=noisy_latents.to(self.device_torch, dtype=dtype),
conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
timestep=timesteps,
)
noise = noise.to(self.device_torch, dtype=dtype)
if self.sd.prediction_type == 'v_prediction':
# v-parameterization training
target = noise_scheduler.get_velocity(noisy_latents, noise, timesteps)
else:
target = noise
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3])
if self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001:
# add min_snr_gamma
loss = apply_snr_weight(loss, timesteps, noise_scheduler, self.train_config.min_snr_gamma)
loss = loss.mean() * loss_jitter_multiplier
loss_float = loss.item()
losses.append(loss_float)
# back propagate loss to free ram
loss.backward()
# apply gradients
optimizer.step()
lr_scheduler.step()
# reset network
self.network.multiplier = 1.0
loss_dict = OrderedDict(
{'loss': sum(losses) / len(losses) if len(losses) > 0 else 0.0}
)
return loss_dict
# end hook_train_loop
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