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import os
import random
from collections import OrderedDict
from typing import Union, Literal, List, Optional
import numpy as np
from diffusers import T2IAdapter, AutoencoderTiny, ControlNetModel
import torch.functional as F
from safetensors.torch import load_file
from torch.utils.data import DataLoader, ConcatDataset
from toolkit import train_tools
from toolkit.basic import value_map, adain, get_mean_std
from toolkit.clip_vision_adapter import ClipVisionAdapter
from toolkit.config_modules import GuidanceConfig
from toolkit.data_loader import get_dataloader_datasets
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO, FileItemDTO
from toolkit.guidance import get_targeted_guidance_loss, get_guidance_loss, GuidanceType
from toolkit.image_utils import show_tensors, show_latents
from toolkit.ip_adapter import IPAdapter
from toolkit.custom_adapter import CustomAdapter
from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
from toolkit.reference_adapter import ReferenceAdapter
from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
from toolkit.train_tools import get_torch_dtype, apply_snr_weight, add_all_snr_to_noise_scheduler, \
apply_learnable_snr_gos, LearnableSNRGamma
import gc
import torch
from jobs.process import BaseSDTrainProcess
from torchvision import transforms
from diffusers import EMAModel
import math
from toolkit.train_tools import precondition_model_outputs_flow_match
def flush():
torch.cuda.empty_cache()
gc.collect()
adapter_transforms = transforms.Compose([
transforms.ToTensor(),
])
class SDTrainer(BaseSDTrainProcess):
def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
super().__init__(process_id, job, config, **kwargs)
self.assistant_adapter: Union['T2IAdapter', 'ControlNetModel', None]
self.do_prior_prediction = False
self.do_long_prompts = False
self.do_guided_loss = False
self.taesd: Optional[AutoencoderTiny] = None
self._clip_image_embeds_unconditional: Union[List[str], None] = None
self.negative_prompt_pool: Union[List[str], None] = None
self.batch_negative_prompt: Union[List[str], None] = None
self.scaler = torch.cuda.amp.GradScaler()
self.is_bfloat = self.train_config.dtype == "bfloat16" or self.train_config.dtype == "bf16"
self.do_grad_scale = True
if self.is_fine_tuning:
self.do_grad_scale = False
if self.adapter_config is not None:
if self.adapter_config.train:
self.do_grad_scale = False
if self.train_config.dtype in ["fp16", "float16"]:
# patch the scaler to allow fp16 training
org_unscale_grads = self.scaler._unscale_grads_
def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
return org_unscale_grads(optimizer, inv_scale, found_inf, True)
self.scaler._unscale_grads_ = _unscale_grads_replacer
def before_model_load(self):
pass
def before_dataset_load(self):
self.assistant_adapter = None
# get adapter assistant if one is set
if self.train_config.adapter_assist_name_or_path is not None:
adapter_path = self.train_config.adapter_assist_name_or_path
if self.train_config.adapter_assist_type == "t2i":
# dont name this adapter since we are not training it
self.assistant_adapter = T2IAdapter.from_pretrained(
adapter_path, torch_dtype=get_torch_dtype(self.train_config.dtype)
).to(self.device_torch)
elif self.train_config.adapter_assist_type == "control_net":
self.assistant_adapter = ControlNetModel.from_pretrained(
adapter_path, torch_dtype=get_torch_dtype(self.train_config.dtype)
).to(self.device_torch, dtype=get_torch_dtype(self.train_config.dtype))
else:
raise ValueError(f"Unknown adapter assist type {self.train_config.adapter_assist_type}")
self.assistant_adapter.eval()
self.assistant_adapter.requires_grad_(False)
flush()
if self.train_config.train_turbo and self.train_config.show_turbo_outputs:
if self.model_config.is_xl:
self.taesd = AutoencoderTiny.from_pretrained("madebyollin/taesdxl",
torch_dtype=get_torch_dtype(self.train_config.dtype))
else:
self.taesd = AutoencoderTiny.from_pretrained("madebyollin/taesd",
torch_dtype=get_torch_dtype(self.train_config.dtype))
self.taesd.to(dtype=get_torch_dtype(self.train_config.dtype), device=self.device_torch)
self.taesd.eval()
self.taesd.requires_grad_(False)
def hook_before_train_loop(self):
if self.train_config.do_prior_divergence:
self.do_prior_prediction = True
# move vae to device if we did not cache latents
if not self.is_latents_cached:
self.sd.vae.eval()
self.sd.vae.to(self.device_torch)
else:
# offload it. Already cached
self.sd.vae.to('cpu')
flush()
add_all_snr_to_noise_scheduler(self.sd.noise_scheduler, self.device_torch)
if self.adapter is not None:
self.adapter.to(self.device_torch)
# check if we have regs and using adapter and caching clip embeddings
has_reg = self.datasets_reg is not None and len(self.datasets_reg) > 0
is_caching_clip_embeddings = self.datasets is not None and any([self.datasets[i].cache_clip_vision_to_disk for i in range(len(self.datasets))])
if has_reg and is_caching_clip_embeddings:
# we need a list of unconditional clip image embeds from other datasets to handle regs
unconditional_clip_image_embeds = []
datasets = get_dataloader_datasets(self.data_loader)
for i in range(len(datasets)):
unconditional_clip_image_embeds += datasets[i].clip_vision_unconditional_cache
if len(unconditional_clip_image_embeds) == 0:
raise ValueError("No unconditional clip image embeds found. This should not happen")
self._clip_image_embeds_unconditional = unconditional_clip_image_embeds
if self.train_config.negative_prompt is not None:
if os.path.exists(self.train_config.negative_prompt):
with open(self.train_config.negative_prompt, 'r') as f:
self.negative_prompt_pool = f.readlines()
# remove empty
self.negative_prompt_pool = [x.strip() for x in self.negative_prompt_pool if x.strip() != ""]
else:
# single prompt
self.negative_prompt_pool = [self.train_config.negative_prompt]
def process_output_for_turbo(self, pred, noisy_latents, timesteps, noise, batch):
# to process turbo learning, we make one big step from our current timestep to the end
# we then denoise the prediction on that remaining step and target our loss to our target latents
# this currently only works on euler_a (that I know of). Would work on others, but needs to be coded to do so.
# needs to be done on each item in batch as they may all have different timesteps
batch_size = pred.shape[0]
pred_chunks = torch.chunk(pred, batch_size, dim=0)
noisy_latents_chunks = torch.chunk(noisy_latents, batch_size, dim=0)
timesteps_chunks = torch.chunk(timesteps, batch_size, dim=0)
latent_chunks = torch.chunk(batch.latents, batch_size, dim=0)
noise_chunks = torch.chunk(noise, batch_size, dim=0)
with torch.no_grad():
# set the timesteps to 1000 so we can capture them to calculate the sigmas
self.sd.noise_scheduler.set_timesteps(
self.sd.noise_scheduler.config.num_train_timesteps,
device=self.device_torch
)
train_timesteps = self.sd.noise_scheduler.timesteps.clone().detach()
train_sigmas = self.sd.noise_scheduler.sigmas.clone().detach()
# set the scheduler to one timestep, we build the step and sigmas for each item in batch for the partial step
self.sd.noise_scheduler.set_timesteps(
1,
device=self.device_torch
)
denoised_pred_chunks = []
target_pred_chunks = []
for i in range(batch_size):
pred_item = pred_chunks[i]
noisy_latents_item = noisy_latents_chunks[i]
timesteps_item = timesteps_chunks[i]
latents_item = latent_chunks[i]
noise_item = noise_chunks[i]
with torch.no_grad():
timestep_idx = [(train_timesteps == t).nonzero().item() for t in timesteps_item][0]
single_step_timestep_schedule = [timesteps_item.squeeze().item()]
# extract the sigma idx for our midpoint timestep
sigmas = train_sigmas[timestep_idx:timestep_idx + 1].to(self.device_torch)
end_sigma_idx = random.randint(timestep_idx, len(train_sigmas) - 1)
end_sigma = train_sigmas[end_sigma_idx:end_sigma_idx + 1].to(self.device_torch)
# add noise to our target
# build the big sigma step. The to step will now be to 0 giving it a full remaining denoising half step
# self.sd.noise_scheduler.sigmas = torch.cat([sigmas, torch.zeros_like(sigmas)]).detach()
self.sd.noise_scheduler.sigmas = torch.cat([sigmas, end_sigma]).detach()
# set our single timstep
self.sd.noise_scheduler.timesteps = torch.from_numpy(
np.array(single_step_timestep_schedule, dtype=np.float32)
).to(device=self.device_torch)
# set the step index to None so it will be recalculated on first step
self.sd.noise_scheduler._step_index = None
denoised_latent = self.sd.noise_scheduler.step(
pred_item, timesteps_item, noisy_latents_item.detach(), return_dict=False
)[0]
residual_noise = (noise_item * end_sigma.flatten()).detach().to(self.device_torch, dtype=get_torch_dtype(
self.train_config.dtype))
# remove the residual noise from the denoised latents. Output should be a clean prediction (theoretically)
denoised_latent = denoised_latent - residual_noise
denoised_pred_chunks.append(denoised_latent)
denoised_latents = torch.cat(denoised_pred_chunks, dim=0)
# set the scheduler back to the original timesteps
self.sd.noise_scheduler.set_timesteps(
self.sd.noise_scheduler.config.num_train_timesteps,
device=self.device_torch
)
output = denoised_latents / self.sd.vae.config['scaling_factor']
output = self.sd.vae.decode(output).sample
if self.train_config.show_turbo_outputs:
# since we are completely denoising, we can show them here
with torch.no_grad():
show_tensors(output)
# we return our big partial step denoised latents as our pred and our untouched latents as our target.
# you can do mse against the two here or run the denoised through the vae for pixel space loss against the
# input tensor images.
return output, batch.tensor.to(self.device_torch, dtype=get_torch_dtype(self.train_config.dtype))
# you can expand these in a child class to make customization easier
def calculate_loss(
self,
noise_pred: torch.Tensor,
noise: torch.Tensor,
noisy_latents: torch.Tensor,
timesteps: torch.Tensor,
batch: 'DataLoaderBatchDTO',
mask_multiplier: Union[torch.Tensor, float] = 1.0,
prior_pred: Union[torch.Tensor, None] = None,
**kwargs
):
loss_target = self.train_config.loss_target
is_reg = any(batch.get_is_reg_list())
prior_mask_multiplier = None
target_mask_multiplier = None
dtype = get_torch_dtype(self.train_config.dtype)
has_mask = batch.mask_tensor is not None
with torch.no_grad():
loss_multiplier = torch.tensor(batch.loss_multiplier_list).to(self.device_torch, dtype=torch.float32)
if self.train_config.match_noise_norm:
# match the norm of the noise
noise_norm = torch.linalg.vector_norm(noise, ord=2, dim=(1, 2, 3), keepdim=True)
noise_pred_norm = torch.linalg.vector_norm(noise_pred, ord=2, dim=(1, 2, 3), keepdim=True)
noise_pred = noise_pred * (noise_norm / noise_pred_norm)
if self.train_config.pred_scaler != 1.0:
noise_pred = noise_pred * self.train_config.pred_scaler
target = None
if self.train_config.target_noise_multiplier != 1.0:
noise = noise * self.train_config.target_noise_multiplier
if self.train_config.correct_pred_norm or (self.train_config.inverted_mask_prior and prior_pred is not None and has_mask):
if self.train_config.correct_pred_norm and not is_reg:
with torch.no_grad():
# this only works if doing a prior pred
if prior_pred is not None:
prior_mean = prior_pred.mean([2,3], keepdim=True)
prior_std = prior_pred.std([2,3], keepdim=True)
noise_mean = noise_pred.mean([2,3], keepdim=True)
noise_std = noise_pred.std([2,3], keepdim=True)
mean_adjust = prior_mean - noise_mean
std_adjust = prior_std - noise_std
mean_adjust = mean_adjust * self.train_config.correct_pred_norm_multiplier
std_adjust = std_adjust * self.train_config.correct_pred_norm_multiplier
target_mean = noise_mean + mean_adjust
target_std = noise_std + std_adjust
eps = 1e-5
# match the noise to the prior
noise = (noise - noise_mean) / (noise_std + eps)
noise = noise * (target_std + eps) + target_mean
noise = noise.detach()
if self.train_config.inverted_mask_prior and prior_pred is not None and has_mask:
assert not self.train_config.train_turbo
with torch.no_grad():
# we need to make the noise prediction be a masked blending of noise and prior_pred
stretched_mask_multiplier = value_map(
mask_multiplier,
batch.file_items[0].dataset_config.mask_min_value,
1.0,
0.0,
1.0
)
prior_mask_multiplier = 1.0 - stretched_mask_multiplier
# target_mask_multiplier = mask_multiplier
# mask_multiplier = 1.0
target = noise
# target = (noise * mask_multiplier) + (prior_pred * prior_mask_multiplier)
# set masked multiplier to 1.0 so we dont double apply it
# mask_multiplier = 1.0
elif prior_pred is not None and not self.train_config.do_prior_divergence:
assert not self.train_config.train_turbo
# matching adapter prediction
target = prior_pred
elif self.sd.prediction_type == 'v_prediction':
# v-parameterization training
target = self.sd.noise_scheduler.get_velocity(batch.tensor, noise, timesteps)
elif self.sd.is_flow_matching:
target = (noise - batch.latents).detach()
else:
target = noise
if target is None:
target = noise
pred = noise_pred
if self.train_config.train_turbo:
pred, target = self.process_output_for_turbo(pred, noisy_latents, timesteps, noise, batch)
ignore_snr = False
if loss_target == 'source' or loss_target == 'unaugmented':
assert not self.train_config.train_turbo
# ignore_snr = True
if batch.sigmas is None:
raise ValueError("Batch sigmas is None. This should not happen")
# src https://github.com/huggingface/diffusers/blob/324d18fba23f6c9d7475b0ff7c777685f7128d40/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L1190
denoised_latents = noise_pred * (-batch.sigmas) + noisy_latents
weighing = batch.sigmas ** -2.0
if loss_target == 'source':
# denoise the latent and compare to the latent in the batch
target = batch.latents
elif loss_target == 'unaugmented':
# we have to encode images into latents for now
# we also denoise as the unaugmented tensor is not a noisy diffirental
with torch.no_grad():
unaugmented_latents = self.sd.encode_images(batch.unaugmented_tensor).to(self.device_torch, dtype=dtype)
unaugmented_latents = unaugmented_latents * self.train_config.latent_multiplier
target = unaugmented_latents.detach()
# Get the target for loss depending on the prediction type
if self.sd.noise_scheduler.config.prediction_type == "epsilon":
target = target # we are computing loss against denoise latents
elif self.sd.noise_scheduler.config.prediction_type == "v_prediction":
target = self.sd.noise_scheduler.get_velocity(target, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {self.sd.noise_scheduler.config.prediction_type}")
# mse loss without reduction
loss_per_element = (weighing.float() * (denoised_latents.float() - target.float()) ** 2)
loss = loss_per_element
else:
if self.train_config.loss_type == "mae":
loss = torch.nn.functional.l1_loss(pred.float(), target.float(), reduction="none")
else:
loss = torch.nn.functional.mse_loss(pred.float(), target.float(), reduction="none")
# handle linear timesteps and only adjust the weight of the timesteps
if self.sd.is_flow_matching and (self.train_config.linear_timesteps or self.train_config.linear_timesteps2):
# calculate the weights for the timesteps
timestep_weight = self.sd.noise_scheduler.get_weights_for_timesteps(
timesteps,
v2=self.train_config.linear_timesteps2
).to(loss.device, dtype=loss.dtype)
timestep_weight = timestep_weight.view(-1, 1, 1, 1).detach()
loss = loss * timestep_weight
if self.train_config.do_prior_divergence and prior_pred is not None:
loss = loss + (torch.nn.functional.mse_loss(pred.float(), prior_pred.float(), reduction="none") * -1.0)
if self.train_config.train_turbo:
mask_multiplier = mask_multiplier[:, 3:, :, :]
# resize to the size of the loss
mask_multiplier = torch.nn.functional.interpolate(mask_multiplier, size=(pred.shape[2], pred.shape[3]), mode='nearest')
# multiply by our mask
loss = loss * mask_multiplier
prior_loss = None
if self.train_config.inverted_mask_prior and prior_pred is not None and prior_mask_multiplier is not None:
assert not self.train_config.train_turbo
if self.train_config.loss_type == "mae":
prior_loss = torch.nn.functional.l1_loss(pred.float(), prior_pred.float(), reduction="none")
else:
prior_loss = torch.nn.functional.mse_loss(pred.float(), prior_pred.float(), reduction="none")
prior_loss = prior_loss * prior_mask_multiplier * self.train_config.inverted_mask_prior_multiplier
if torch.isnan(prior_loss).any():
print("Prior loss is nan")
prior_loss = None
else:
prior_loss = prior_loss.mean([1, 2, 3])
# loss = loss + prior_loss
# loss = loss + prior_loss
# loss = loss + prior_loss
loss = loss.mean([1, 2, 3])
# apply loss multiplier before prior loss
loss = loss * loss_multiplier
if prior_loss is not None:
loss = loss + prior_loss
if not self.train_config.train_turbo:
if self.train_config.learnable_snr_gos:
# add snr_gamma
loss = apply_learnable_snr_gos(loss, timesteps, self.snr_gos)
elif self.train_config.snr_gamma is not None and self.train_config.snr_gamma > 0.000001 and not ignore_snr:
# add snr_gamma
loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.snr_gamma,
fixed=True)
elif self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001 and not ignore_snr:
# add min_snr_gamma
loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.min_snr_gamma)
loss = loss.mean()
# check for additional losses
if self.adapter is not None and hasattr(self.adapter, "additional_loss") and self.adapter.additional_loss is not None:
loss = loss + self.adapter.additional_loss.mean()
self.adapter.additional_loss = None
if self.train_config.target_norm_std:
# seperate out the batch and channels
pred_std = noise_pred.std([2, 3], keepdim=True)
norm_std_loss = torch.abs(self.train_config.target_norm_std_value - pred_std).mean()
loss = loss + norm_std_loss
return loss
def preprocess_batch(self, batch: 'DataLoaderBatchDTO'):
return batch
def get_guided_loss(
self,
noisy_latents: torch.Tensor,
conditional_embeds: PromptEmbeds,
match_adapter_assist: bool,
network_weight_list: list,
timesteps: torch.Tensor,
pred_kwargs: dict,
batch: 'DataLoaderBatchDTO',
noise: torch.Tensor,
unconditional_embeds: Optional[PromptEmbeds] = None,
**kwargs
):
loss = get_guidance_loss(
noisy_latents=noisy_latents,
conditional_embeds=conditional_embeds,
match_adapter_assist=match_adapter_assist,
network_weight_list=network_weight_list,
timesteps=timesteps,
pred_kwargs=pred_kwargs,
batch=batch,
noise=noise,
sd=self.sd,
unconditional_embeds=unconditional_embeds,
scaler=self.scaler,
**kwargs
)
return loss
def get_guided_loss_targeted_polarity(
self,
noisy_latents: torch.Tensor,
conditional_embeds: PromptEmbeds,
match_adapter_assist: bool,
network_weight_list: list,
timesteps: torch.Tensor,
pred_kwargs: dict,
batch: 'DataLoaderBatchDTO',
noise: torch.Tensor,
**kwargs
):
with torch.no_grad():
# Perform targeted guidance (working title)
dtype = get_torch_dtype(self.train_config.dtype)
conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
mean_latents = (conditional_latents + unconditional_latents) / 2.0
unconditional_diff = (unconditional_latents - mean_latents)
conditional_diff = (conditional_latents - mean_latents)
# we need to determine the amount of signal and noise that would be present at the current timestep
# conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
# unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
# unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
# conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
# unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
# target_noise = noise + unconditional_signal
conditional_noisy_latents = self.sd.add_noise(
mean_latents,
noise,
timesteps
).detach()
unconditional_noisy_latents = self.sd.add_noise(
mean_latents,
noise,
timesteps
).detach()
# Disable the LoRA network so we can predict parent network knowledge without it
self.network.is_active = False
self.sd.unet.eval()
# Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
# This acts as our control to preserve the unaltered parts of the image.
baseline_prediction = self.sd.predict_noise(
latents=unconditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
timestep=timesteps,
guidance_scale=1.0,
**pred_kwargs # adapter residuals in here
).detach()
# double up everything to run it through all at once
cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
cat_latents = torch.cat([conditional_noisy_latents, conditional_noisy_latents], dim=0)
cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
# since we are dividing the polarity from the middle out, we need to double our network
# weights on training since the convergent point will be at half network strength
negative_network_weights = [weight * -2.0 for weight in network_weight_list]
positive_network_weights = [weight * 2.0 for weight in network_weight_list]
cat_network_weight_list = positive_network_weights + negative_network_weights
# turn the LoRA network back on.
self.sd.unet.train()
self.network.is_active = True
self.network.multiplier = cat_network_weight_list
# do our prediction with LoRA active on the scaled guidance latents
prediction = self.sd.predict_noise(
latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
timestep=cat_timesteps,
guidance_scale=1.0,
**pred_kwargs # adapter residuals in here
)
pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
pred_pos = pred_pos - baseline_prediction
pred_neg = pred_neg - baseline_prediction
pred_loss = torch.nn.functional.mse_loss(
pred_pos.float(),
unconditional_diff.float(),
reduction="none"
)
pred_loss = pred_loss.mean([1, 2, 3])
pred_neg_loss = torch.nn.functional.mse_loss(
pred_neg.float(),
conditional_diff.float(),
reduction="none"
)
pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
loss = (pred_loss + pred_neg_loss) / 2.0
# loss = self.apply_snr(loss, timesteps)
loss = loss.mean()
loss.backward()
# detach it so parent class can run backward on no grads without throwing error
loss = loss.detach()
loss.requires_grad_(True)
return loss
def get_guided_loss_masked_polarity(
self,
noisy_latents: torch.Tensor,
conditional_embeds: PromptEmbeds,
match_adapter_assist: bool,
network_weight_list: list,
timesteps: torch.Tensor,
pred_kwargs: dict,
batch: 'DataLoaderBatchDTO',
noise: torch.Tensor,
**kwargs
):
with torch.no_grad():
# Perform targeted guidance (working title)
dtype = get_torch_dtype(self.train_config.dtype)
conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
inverse_latents = unconditional_latents - (conditional_latents - unconditional_latents)
mean_latents = (conditional_latents + unconditional_latents) / 2.0
# unconditional_diff = (unconditional_latents - mean_latents)
# conditional_diff = (conditional_latents - mean_latents)
# we need to determine the amount of signal and noise that would be present at the current timestep
# conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
# unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
# unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
# conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
# unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
# make a differential mask
differential_mask = torch.abs(conditional_latents - unconditional_latents)
max_differential = \
differential_mask.max(dim=1, keepdim=True)[0].max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]
differential_scaler = 1.0 / max_differential
differential_mask = differential_mask * differential_scaler
spread_point = 0.1
# adjust mask to amplify the differential at 0.1
differential_mask = ((differential_mask - spread_point) * 10.0) + spread_point
# clip it
differential_mask = torch.clamp(differential_mask, 0.0, 1.0)
# target_noise = noise + unconditional_signal
conditional_noisy_latents = self.sd.add_noise(
conditional_latents,
noise,
timesteps
).detach()
unconditional_noisy_latents = self.sd.add_noise(
unconditional_latents,
noise,
timesteps
).detach()
inverse_noisy_latents = self.sd.add_noise(
inverse_latents,
noise,
timesteps
).detach()
# Disable the LoRA network so we can predict parent network knowledge without it
self.network.is_active = False
self.sd.unet.eval()
# Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
# This acts as our control to preserve the unaltered parts of the image.
# baseline_prediction = self.sd.predict_noise(
# latents=unconditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
# conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
# timestep=timesteps,
# guidance_scale=1.0,
# **pred_kwargs # adapter residuals in here
# ).detach()
# double up everything to run it through all at once
cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
cat_latents = torch.cat([conditional_noisy_latents, unconditional_noisy_latents], dim=0)
cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
# since we are dividing the polarity from the middle out, we need to double our network
# weights on training since the convergent point will be at half network strength
negative_network_weights = [weight * -1.0 for weight in network_weight_list]
positive_network_weights = [weight * 1.0 for weight in network_weight_list]
cat_network_weight_list = positive_network_weights + negative_network_weights
# turn the LoRA network back on.
self.sd.unet.train()
self.network.is_active = True
self.network.multiplier = cat_network_weight_list
# do our prediction with LoRA active on the scaled guidance latents
prediction = self.sd.predict_noise(
latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
timestep=cat_timesteps,
guidance_scale=1.0,
**pred_kwargs # adapter residuals in here
)
pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
# create a loss to balance the mean to 0 between the two predictions
differential_mean_pred_loss = torch.abs(pred_pos - pred_neg).mean([1, 2, 3]) ** 2.0
# pred_pos = pred_pos - baseline_prediction
# pred_neg = pred_neg - baseline_prediction
pred_loss = torch.nn.functional.mse_loss(
pred_pos.float(),
noise.float(),
reduction="none"
)
# apply mask
pred_loss = pred_loss * (1.0 + differential_mask)
pred_loss = pred_loss.mean([1, 2, 3])
pred_neg_loss = torch.nn.functional.mse_loss(
pred_neg.float(),
noise.float(),
reduction="none"
)
# apply inverse mask
pred_neg_loss = pred_neg_loss * (1.0 - differential_mask)
pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
# make a loss to balance to losses of the pos and neg so they are equal
# differential_mean_loss_loss = torch.abs(pred_loss - pred_neg_loss)
#
# differential_mean_loss = differential_mean_pred_loss + differential_mean_loss_loss
#
# # add a multiplier to balancing losses to make them the top priority
# differential_mean_loss = differential_mean_loss
# remove the grads from the negative as it is only a balancing loss
# pred_neg_loss = pred_neg_loss.detach()
# loss = pred_loss + pred_neg_loss + differential_mean_loss
loss = pred_loss + pred_neg_loss
# loss = self.apply_snr(loss, timesteps)
loss = loss.mean()
loss.backward()
# detach it so parent class can run backward on no grads without throwing error
loss = loss.detach()
loss.requires_grad_(True)
return loss
def get_prior_prediction(
self,
noisy_latents: torch.Tensor,
conditional_embeds: PromptEmbeds,
match_adapter_assist: bool,
network_weight_list: list,
timesteps: torch.Tensor,
pred_kwargs: dict,
batch: 'DataLoaderBatchDTO',
noise: torch.Tensor,
unconditional_embeds: Optional[PromptEmbeds] = None,
conditioned_prompts=None,
**kwargs
):
# todo for embeddings, we need to run without trigger words
was_unet_training = self.sd.unet.training
was_network_active = False
if self.network is not None:
was_network_active = self.network.is_active
self.network.is_active = False
can_disable_adapter = False
was_adapter_active = False
if self.adapter is not None and (isinstance(self.adapter, IPAdapter) or
isinstance(self.adapter, ReferenceAdapter) or
(isinstance(self.adapter, CustomAdapter))
):
can_disable_adapter = True
was_adapter_active = self.adapter.is_active
self.adapter.is_active = False
# do a prediction here so we can match its output with network multiplier set to 0.0
with torch.no_grad():
dtype = get_torch_dtype(self.train_config.dtype)
embeds_to_use = conditional_embeds.clone().detach()
# handle clip vision adapter by removing triggers from prompt and replacing with the class name
if (self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter)) or self.embedding is not None:
prompt_list = batch.get_caption_list()
class_name = ''
triggers = ['[trigger]', '[name]']
remove_tokens = []
if self.embed_config is not None:
triggers.append(self.embed_config.trigger)
for i in range(1, self.embed_config.tokens):
remove_tokens.append(f"{self.embed_config.trigger}_{i}")
if self.embed_config.trigger_class_name is not None:
class_name = self.embed_config.trigger_class_name
if self.adapter is not None:
triggers.append(self.adapter_config.trigger)
for i in range(1, self.adapter_config.num_tokens):
remove_tokens.append(f"{self.adapter_config.trigger}_{i}")
if self.adapter_config.trigger_class_name is not None:
class_name = self.adapter_config.trigger_class_name
for idx, prompt in enumerate(prompt_list):
for remove_token in remove_tokens:
prompt = prompt.replace(remove_token, '')
for trigger in triggers:
prompt = prompt.replace(trigger, class_name)
prompt_list[idx] = prompt
embeds_to_use = self.sd.encode_prompt(
prompt_list,
long_prompts=self.do_long_prompts).to(
self.device_torch,
dtype=dtype).detach()
# dont use network on this
# self.network.multiplier = 0.0
self.sd.unet.eval()
if self.adapter is not None and isinstance(self.adapter, IPAdapter) and not self.sd.is_flux:
# we need to remove the image embeds from the prompt except for flux
embeds_to_use: PromptEmbeds = embeds_to_use.clone().detach()
end_pos = embeds_to_use.text_embeds.shape[1] - self.adapter_config.num_tokens
embeds_to_use.text_embeds = embeds_to_use.text_embeds[:, :end_pos, :]
if unconditional_embeds is not None:
unconditional_embeds = unconditional_embeds.clone().detach()
unconditional_embeds.text_embeds = unconditional_embeds.text_embeds[:, :end_pos]
if unconditional_embeds is not None:
unconditional_embeds = unconditional_embeds.to(self.device_torch, dtype=dtype).detach()
prior_pred = self.sd.predict_noise(
latents=noisy_latents.to(self.device_torch, dtype=dtype).detach(),
conditional_embeddings=embeds_to_use.to(self.device_torch, dtype=dtype).detach(),
unconditional_embeddings=unconditional_embeds,
timestep=timesteps,
guidance_scale=self.train_config.cfg_scale,
rescale_cfg=self.train_config.cfg_rescale,
**pred_kwargs # adapter residuals in here
)
if was_unet_training:
self.sd.unet.train()
prior_pred = prior_pred.detach()
# remove the residuals as we wont use them on prediction when matching control
if match_adapter_assist and 'down_intrablock_additional_residuals' in pred_kwargs:
del pred_kwargs['down_intrablock_additional_residuals']
if match_adapter_assist and 'down_block_additional_residuals' in pred_kwargs:
del pred_kwargs['down_block_additional_residuals']
if match_adapter_assist and 'mid_block_additional_residual' in pred_kwargs:
del pred_kwargs['mid_block_additional_residual']
if can_disable_adapter:
self.adapter.is_active = was_adapter_active
# restore network
# self.network.multiplier = network_weight_list
if self.network is not None:
self.network.is_active = was_network_active
return prior_pred
def before_unet_predict(self):
pass
def after_unet_predict(self):
pass
def end_of_training_loop(self):
pass
def predict_noise(
self,
noisy_latents: torch.Tensor,
timesteps: Union[int, torch.Tensor] = 1,
conditional_embeds: Union[PromptEmbeds, None] = None,
unconditional_embeds: Union[PromptEmbeds, None] = None,
**kwargs,
):
dtype = get_torch_dtype(self.train_config.dtype)
return self.sd.predict_noise(
latents=noisy_latents.to(self.device_torch, dtype=dtype),
conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
unconditional_embeddings=unconditional_embeds,
timestep=timesteps,
guidance_scale=self.train_config.cfg_scale,
detach_unconditional=False,
rescale_cfg=self.train_config.cfg_rescale,
**kwargs
)
def train_single_accumulation(self, batch: DataLoaderBatchDTO):
self.timer.start('preprocess_batch')
batch = self.preprocess_batch(batch)
dtype = get_torch_dtype(self.train_config.dtype)
# sanity check
if self.sd.vae.dtype != self.sd.vae_torch_dtype:
self.sd.vae = self.sd.vae.to(self.sd.vae_torch_dtype)
if isinstance(self.sd.text_encoder, list):
for encoder in self.sd.text_encoder:
if encoder.dtype != self.sd.te_torch_dtype:
encoder.to(self.sd.te_torch_dtype)
else:
if self.sd.text_encoder.dtype != self.sd.te_torch_dtype:
self.sd.text_encoder.to(self.sd.te_torch_dtype)
noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
if self.train_config.do_cfg or self.train_config.do_random_cfg:
# pick random negative prompts
if self.negative_prompt_pool is not None:
negative_prompts = []
for i in range(noisy_latents.shape[0]):
num_neg = random.randint(1, self.train_config.max_negative_prompts)
this_neg_prompts = [random.choice(self.negative_prompt_pool) for _ in range(num_neg)]
this_neg_prompt = ', '.join(this_neg_prompts)
negative_prompts.append(this_neg_prompt)
self.batch_negative_prompt = negative_prompts
else:
self.batch_negative_prompt = ['' for _ in range(batch.latents.shape[0])]
if self.adapter and isinstance(self.adapter, CustomAdapter):
# condition the prompt
# todo handle more than one adapter image
self.adapter.num_control_images = 1
conditioned_prompts = self.adapter.condition_prompt(conditioned_prompts)
network_weight_list = batch.get_network_weight_list()
if self.train_config.single_item_batching:
network_weight_list = network_weight_list + network_weight_list
has_adapter_img = batch.control_tensor is not None
has_clip_image = batch.clip_image_tensor is not None
has_clip_image_embeds = batch.clip_image_embeds is not None
# force it to be true if doing regs as we handle those differently
if any([batch.file_items[idx].is_reg for idx in range(len(batch.file_items))]):
has_clip_image = True
if self._clip_image_embeds_unconditional is not None:
has_clip_image_embeds = True # we are caching embeds, handle that differently
has_clip_image = False
if self.adapter is not None and isinstance(self.adapter, IPAdapter) and not has_clip_image and has_adapter_img:
raise ValueError(
"IPAdapter control image is now 'clip_image_path' instead of 'control_path'. Please update your dataset config ")
match_adapter_assist = False
# check if we are matching the adapter assistant
if self.assistant_adapter:
if self.train_config.match_adapter_chance == 1.0:
match_adapter_assist = True
elif self.train_config.match_adapter_chance > 0.0:
match_adapter_assist = torch.rand(
(1,), device=self.device_torch, dtype=dtype
) < self.train_config.match_adapter_chance
self.timer.stop('preprocess_batch')
is_reg = False
with torch.no_grad():
loss_multiplier = torch.ones((noisy_latents.shape[0], 1, 1, 1), device=self.device_torch, dtype=dtype)
for idx, file_item in enumerate(batch.file_items):
if file_item.is_reg:
loss_multiplier[idx] = loss_multiplier[idx] * self.train_config.reg_weight
is_reg = True
adapter_images = None
sigmas = None
if has_adapter_img and (self.adapter or self.assistant_adapter):
with self.timer('get_adapter_images'):
# todo move this to data loader
if batch.control_tensor is not None:
adapter_images = batch.control_tensor.to(self.device_torch, dtype=dtype).detach()
# match in channels
if self.assistant_adapter is not None:
in_channels = self.assistant_adapter.config.in_channels
if adapter_images.shape[1] != in_channels:
# we need to match the channels
adapter_images = adapter_images[:, :in_channels, :, :]
else:
raise NotImplementedError("Adapter images now must be loaded with dataloader")
clip_images = None
if has_clip_image:
with self.timer('get_clip_images'):
# todo move this to data loader
if batch.clip_image_tensor is not None:
clip_images = batch.clip_image_tensor.to(self.device_torch, dtype=dtype).detach()
mask_multiplier = torch.ones((noisy_latents.shape[0], 1, 1, 1), device=self.device_torch, dtype=dtype)
if batch.mask_tensor is not None:
with self.timer('get_mask_multiplier'):
# upsampling no supported for bfloat16
mask_multiplier = batch.mask_tensor.to(self.device_torch, dtype=torch.float16).detach()
# scale down to the size of the latents, mask multiplier shape(bs, 1, width, height), noisy_latents shape(bs, channels, width, height)
mask_multiplier = torch.nn.functional.interpolate(
mask_multiplier, size=(noisy_latents.shape[2], noisy_latents.shape[3])
)
# expand to match latents
mask_multiplier = mask_multiplier.expand(-1, noisy_latents.shape[1], -1, -1)
mask_multiplier = mask_multiplier.to(self.device_torch, dtype=dtype).detach()
def get_adapter_multiplier():
if self.adapter and isinstance(self.adapter, T2IAdapter):
# training a t2i adapter, not using as assistant.
return 1.0
elif match_adapter_assist:
# training a texture. We want it high
adapter_strength_min = 0.9
adapter_strength_max = 1.0
else:
# training with assistance, we want it low
# adapter_strength_min = 0.4
# adapter_strength_max = 0.7
adapter_strength_min = 0.5
adapter_strength_max = 1.1
adapter_conditioning_scale = torch.rand(
(1,), device=self.device_torch, dtype=dtype
)
adapter_conditioning_scale = value_map(
adapter_conditioning_scale,
0.0,
1.0,
adapter_strength_min,
adapter_strength_max
)
return adapter_conditioning_scale
# flush()
with self.timer('grad_setup'):
# text encoding
grad_on_text_encoder = False
if self.train_config.train_text_encoder:
grad_on_text_encoder = True
if self.embedding is not None:
grad_on_text_encoder = True
if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
grad_on_text_encoder = True
if self.adapter_config and self.adapter_config.type == 'te_augmenter':
grad_on_text_encoder = True
# have a blank network so we can wrap it in a context and set multipliers without checking every time
if self.network is not None:
network = self.network
else:
network = BlankNetwork()
# set the weights
network.multiplier = network_weight_list
# activate network if it exits
prompts_1 = conditioned_prompts
prompts_2 = None
if self.train_config.short_and_long_captions_encoder_split and self.sd.is_xl:
prompts_1 = batch.get_caption_short_list()
prompts_2 = conditioned_prompts
# make the batch splits
if self.train_config.single_item_batching:
if self.model_config.refiner_name_or_path is not None:
raise ValueError("Single item batching is not supported when training the refiner")
batch_size = noisy_latents.shape[0]
# chunk/split everything
noisy_latents_list = torch.chunk(noisy_latents, batch_size, dim=0)
noise_list = torch.chunk(noise, batch_size, dim=0)
timesteps_list = torch.chunk(timesteps, batch_size, dim=0)
conditioned_prompts_list = [[prompt] for prompt in prompts_1]
if imgs is not None:
imgs_list = torch.chunk(imgs, batch_size, dim=0)
else:
imgs_list = [None for _ in range(batch_size)]
if adapter_images is not None:
adapter_images_list = torch.chunk(adapter_images, batch_size, dim=0)
else:
adapter_images_list = [None for _ in range(batch_size)]
if clip_images is not None:
clip_images_list = torch.chunk(clip_images, batch_size, dim=0)
else:
clip_images_list = [None for _ in range(batch_size)]
mask_multiplier_list = torch.chunk(mask_multiplier, batch_size, dim=0)
if prompts_2 is None:
prompt_2_list = [None for _ in range(batch_size)]
else:
prompt_2_list = [[prompt] for prompt in prompts_2]
else:
noisy_latents_list = [noisy_latents]
noise_list = [noise]
timesteps_list = [timesteps]
conditioned_prompts_list = [prompts_1]
imgs_list = [imgs]
adapter_images_list = [adapter_images]
clip_images_list = [clip_images]
mask_multiplier_list = [mask_multiplier]
if prompts_2 is None:
prompt_2_list = [None]
else:
prompt_2_list = [prompts_2]
for noisy_latents, noise, timesteps, conditioned_prompts, imgs, adapter_images, clip_images, mask_multiplier, prompt_2 in zip(
noisy_latents_list,
noise_list,
timesteps_list,
conditioned_prompts_list,
imgs_list,
adapter_images_list,
clip_images_list,
mask_multiplier_list,
prompt_2_list
):
# if self.train_config.negative_prompt is not None:
# # add negative prompt
# conditioned_prompts = conditioned_prompts + [self.train_config.negative_prompt for x in
# range(len(conditioned_prompts))]
# if prompt_2 is not None:
# prompt_2 = prompt_2 + [self.train_config.negative_prompt for x in range(len(prompt_2))]
with (network):
# encode clip adapter here so embeds are active for tokenizer
if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
with self.timer('encode_clip_vision_embeds'):
if has_clip_image:
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
clip_images.detach().to(self.device_torch, dtype=dtype),
is_training=True,
has_been_preprocessed=True
)
else:
# just do a blank one
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
torch.zeros(
(noisy_latents.shape[0], 3, 512, 512),
device=self.device_torch, dtype=dtype
),
is_training=True,
has_been_preprocessed=True,
drop=True
)
# it will be injected into the tokenizer when called
self.adapter(conditional_clip_embeds)
# do the custom adapter after the prior prediction
if self.adapter and isinstance(self.adapter, CustomAdapter) and has_clip_image:
quad_count = random.randint(1, 4)
self.adapter.train()
self.adapter.trigger_pre_te(
tensors_0_1=clip_images if not is_reg else None, # on regs we send none to get random noise
is_training=True,
has_been_preprocessed=True,
quad_count=quad_count,
batch_size=noisy_latents.shape[0]
)
with self.timer('encode_prompt'):
unconditional_embeds = None
if grad_on_text_encoder:
with torch.set_grad_enabled(True):
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
conditional_embeds = self.sd.encode_prompt(
conditioned_prompts, prompt_2,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,
dtype=dtype)
if self.train_config.do_cfg:
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = True
# todo only do one and repeat it
unconditional_embeds = self.sd.encode_prompt(
self.batch_negative_prompt,
self.batch_negative_prompt,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,
dtype=dtype)
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
else:
with torch.set_grad_enabled(False):
# make sure it is in eval mode
if isinstance(self.sd.text_encoder, list):
for te in self.sd.text_encoder:
te.eval()
else:
self.sd.text_encoder.eval()
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
conditional_embeds = self.sd.encode_prompt(
conditioned_prompts, prompt_2,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,
dtype=dtype)
if self.train_config.do_cfg:
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = True
unconditional_embeds = self.sd.encode_prompt(
self.batch_negative_prompt,
dropout_prob=self.train_config.prompt_dropout_prob,
long_prompts=self.do_long_prompts).to(
self.device_torch,
dtype=dtype)
if isinstance(self.adapter, CustomAdapter):
self.adapter.is_unconditional_run = False
# detach the embeddings
conditional_embeds = conditional_embeds.detach()
if self.train_config.do_cfg:
unconditional_embeds = unconditional_embeds.detach()
# flush()
pred_kwargs = {}
if has_adapter_img:
if (self.adapter and isinstance(self.adapter, T2IAdapter)) or (
self.assistant_adapter and isinstance(self.assistant_adapter, T2IAdapter)):
with torch.set_grad_enabled(self.adapter is not None):
adapter = self.assistant_adapter if self.assistant_adapter is not None else self.adapter
adapter_multiplier = get_adapter_multiplier()
with self.timer('encode_adapter'):
down_block_additional_residuals = adapter(adapter_images)
if self.assistant_adapter:
# not training. detach
down_block_additional_residuals = [
sample.to(dtype=dtype).detach() * adapter_multiplier for sample in
down_block_additional_residuals
]
else:
down_block_additional_residuals = [
sample.to(dtype=dtype) * adapter_multiplier for sample in
down_block_additional_residuals
]
pred_kwargs['down_intrablock_additional_residuals'] = down_block_additional_residuals
if self.adapter and isinstance(self.adapter, IPAdapter):
with self.timer('encode_adapter_embeds'):
# number of images to do if doing a quad image
quad_count = random.randint(1, 4)
image_size = self.adapter.input_size
if has_clip_image_embeds:
# todo handle reg images better than this
if is_reg:
# get unconditional image embeds from cache
embeds = [
load_file(random.choice(batch.clip_image_embeds_unconditional)) for i in
range(noisy_latents.shape[0])
]
conditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
embeds,
quad_count=quad_count
)
if self.train_config.do_cfg:
embeds = [
load_file(random.choice(batch.clip_image_embeds_unconditional)) for i in
range(noisy_latents.shape[0])
]
unconditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
embeds,
quad_count=quad_count
)
else:
conditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
batch.clip_image_embeds,
quad_count=quad_count
)
if self.train_config.do_cfg:
unconditional_clip_embeds = self.adapter.parse_clip_image_embeds_from_cache(
batch.clip_image_embeds_unconditional,
quad_count=quad_count
)
elif is_reg:
# we will zero it out in the img embedder
clip_images = torch.zeros(
(noisy_latents.shape[0], 3, image_size, image_size),
device=self.device_torch, dtype=dtype
).detach()
# drop will zero it out
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
clip_images,
drop=True,
is_training=True,
has_been_preprocessed=False,
quad_count=quad_count
)
if self.train_config.do_cfg:
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
torch.zeros(
(noisy_latents.shape[0], 3, image_size, image_size),
device=self.device_torch, dtype=dtype
).detach(),
is_training=True,
drop=True,
has_been_preprocessed=False,
quad_count=quad_count
)
elif has_clip_image:
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
clip_images.detach().to(self.device_torch, dtype=dtype),
is_training=True,
has_been_preprocessed=True,
quad_count=quad_count,
# do cfg on clip embeds to normalize the embeddings for when doing cfg
# cfg_embed_strength=3.0 if not self.train_config.do_cfg else None
# cfg_embed_strength=3.0 if not self.train_config.do_cfg else None
)
if self.train_config.do_cfg:
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
clip_images.detach().to(self.device_torch, dtype=dtype),
is_training=True,
drop=True,
has_been_preprocessed=True,
quad_count=quad_count
)
else:
print("No Clip Image")
print([file_item.path for file_item in batch.file_items])
raise ValueError("Could not find clip image")
if not self.adapter_config.train_image_encoder:
# we are not training the image encoder, so we need to detach the embeds
conditional_clip_embeds = conditional_clip_embeds.detach()
if self.train_config.do_cfg:
unconditional_clip_embeds = unconditional_clip_embeds.detach()
with self.timer('encode_adapter'):
self.adapter.train()
conditional_embeds = self.adapter(
conditional_embeds.detach(),
conditional_clip_embeds,
is_unconditional=False
)
if self.train_config.do_cfg:
unconditional_embeds = self.adapter(
unconditional_embeds.detach(),
unconditional_clip_embeds,
is_unconditional=True
)
else:
# wipe out unconsitional
self.adapter.last_unconditional = None
if self.adapter and isinstance(self.adapter, ReferenceAdapter):
# pass in our scheduler
self.adapter.noise_scheduler = self.lr_scheduler
if has_clip_image or has_adapter_img:
img_to_use = clip_images if has_clip_image else adapter_images
# currently 0-1 needs to be -1 to 1
reference_images = ((img_to_use - 0.5) * 2).detach().to(self.device_torch, dtype=dtype)
self.adapter.set_reference_images(reference_images)
self.adapter.noise_scheduler = self.sd.noise_scheduler
elif is_reg:
self.adapter.set_blank_reference_images(noisy_latents.shape[0])
else:
self.adapter.set_reference_images(None)
prior_pred = None
do_reg_prior = False
# if is_reg and (self.network is not None or self.adapter is not None):
# # we are doing a reg image and we have a network or adapter
# do_reg_prior = True
do_inverted_masked_prior = False
if self.train_config.inverted_mask_prior and batch.mask_tensor is not None:
do_inverted_masked_prior = True
do_correct_pred_norm_prior = self.train_config.correct_pred_norm
do_guidance_prior = False
if batch.unconditional_latents is not None:
# for this not that, we need a prior pred to normalize
guidance_type: GuidanceType = batch.file_items[0].dataset_config.guidance_type
if guidance_type == 'tnt':
do_guidance_prior = True
if ((
has_adapter_img and self.assistant_adapter and match_adapter_assist) or self.do_prior_prediction or do_guidance_prior or do_reg_prior or do_inverted_masked_prior or self.train_config.correct_pred_norm):
with self.timer('prior predict'):
prior_pred = self.get_prior_prediction(
noisy_latents=noisy_latents,
conditional_embeds=conditional_embeds,
match_adapter_assist=match_adapter_assist,
network_weight_list=network_weight_list,
timesteps=timesteps,
pred_kwargs=pred_kwargs,
noise=noise,
batch=batch,
unconditional_embeds=unconditional_embeds,
conditioned_prompts=conditioned_prompts
)
if prior_pred is not None:
prior_pred = prior_pred.detach()
# do the custom adapter after the prior prediction
if self.adapter and isinstance(self.adapter, CustomAdapter) and has_clip_image:
quad_count = random.randint(1, 4)
self.adapter.train()
conditional_embeds = self.adapter.condition_encoded_embeds(
tensors_0_1=clip_images,
prompt_embeds=conditional_embeds,
is_training=True,
has_been_preprocessed=True,
quad_count=quad_count
)
if self.train_config.do_cfg and unconditional_embeds is not None:
unconditional_embeds = self.adapter.condition_encoded_embeds(
tensors_0_1=clip_images,
prompt_embeds=unconditional_embeds,
is_training=True,
has_been_preprocessed=True,
is_unconditional=True,
quad_count=quad_count
)
if self.adapter and isinstance(self.adapter, CustomAdapter) and batch.extra_values is not None:
self.adapter.add_extra_values(batch.extra_values.detach())
if self.train_config.do_cfg:
self.adapter.add_extra_values(torch.zeros_like(batch.extra_values.detach()),
is_unconditional=True)
if has_adapter_img:
if (self.adapter and isinstance(self.adapter, ControlNetModel)) or (
self.assistant_adapter and isinstance(self.assistant_adapter, ControlNetModel)):
if self.train_config.do_cfg:
raise ValueError("ControlNetModel is not supported with CFG")
with torch.set_grad_enabled(self.adapter is not None):
adapter: ControlNetModel = self.assistant_adapter if self.assistant_adapter is not None else self.adapter
adapter_multiplier = get_adapter_multiplier()
with self.timer('encode_adapter'):
# add_text_embeds is pooled_prompt_embeds for sdxl
added_cond_kwargs = {}
if self.sd.is_xl:
added_cond_kwargs["text_embeds"] = conditional_embeds.pooled_embeds
added_cond_kwargs['time_ids'] = self.sd.get_time_ids_from_latents(noisy_latents)
down_block_res_samples, mid_block_res_sample = adapter(
noisy_latents,
timesteps,
encoder_hidden_states=conditional_embeds.text_embeds,
controlnet_cond=adapter_images,
conditioning_scale=1.0,
guess_mode=False,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)
pred_kwargs['down_block_additional_residuals'] = down_block_res_samples
pred_kwargs['mid_block_additional_residual'] = mid_block_res_sample
self.before_unet_predict()
# do a prior pred if we have an unconditional image, we will swap out the giadance later
if batch.unconditional_latents is not None or self.do_guided_loss:
# do guided loss
loss = self.get_guided_loss(
noisy_latents=noisy_latents,
conditional_embeds=conditional_embeds,
match_adapter_assist=match_adapter_assist,
network_weight_list=network_weight_list,
timesteps=timesteps,
pred_kwargs=pred_kwargs,
batch=batch,
noise=noise,
unconditional_embeds=unconditional_embeds,
mask_multiplier=mask_multiplier,
prior_pred=prior_pred,
)
else:
with self.timer('predict_unet'):
if unconditional_embeds is not None:
unconditional_embeds = unconditional_embeds.to(self.device_torch, dtype=dtype).detach()
noise_pred = self.predict_noise(
noisy_latents=noisy_latents.to(self.device_torch, dtype=dtype),
timesteps=timesteps,
conditional_embeds=conditional_embeds.to(self.device_torch, dtype=dtype),
unconditional_embeds=unconditional_embeds,
**pred_kwargs
)
self.after_unet_predict()
with self.timer('calculate_loss'):
noise = noise.to(self.device_torch, dtype=dtype).detach()
loss = self.calculate_loss(
noise_pred=noise_pred,
noise=noise,
noisy_latents=noisy_latents,
timesteps=timesteps,
batch=batch,
mask_multiplier=mask_multiplier,
prior_pred=prior_pred,
)
# check if nan
if torch.isnan(loss):
print("loss is nan")
loss = torch.zeros_like(loss).requires_grad_(True)
with self.timer('backward'):
# todo we have multiplier seperated. works for now as res are not in same batch, but need to change
loss = loss * loss_multiplier.mean()
# IMPORTANT if gradient checkpointing do not leave with network when doing backward
# it will destroy the gradients. This is because the network is a context manager
# and will change the multipliers back to 0.0 when exiting. They will be
# 0.0 for the backward pass and the gradients will be 0.0
# I spent weeks on fighting this. DON'T DO IT
# with fsdp_overlap_step_with_backward():
# if self.is_bfloat:
# loss.backward()
# else:
if not self.do_grad_scale:
loss.backward()
else:
self.scaler.scale(loss).backward()
return loss.detach()
# flush()
def hook_train_loop(self, batch: Union[DataLoaderBatchDTO, List[DataLoaderBatchDTO]]):
if isinstance(batch, list):
batch_list = batch
else:
batch_list = [batch]
total_loss = None
self.optimizer.zero_grad()
for batch in batch_list:
loss = self.train_single_accumulation(batch)
if total_loss is None:
total_loss = loss
else:
total_loss += loss
if len(batch_list) > 1 and self.model_config.low_vram:
torch.cuda.empty_cache()
if not self.is_grad_accumulation_step:
# fix this for multi params
if self.train_config.optimizer != 'adafactor':
if self.do_grad_scale:
self.scaler.unscale_(self.optimizer)
if isinstance(self.params[0], dict):
for i in range(len(self.params)):
torch.nn.utils.clip_grad_norm_(self.params[i]['params'], self.train_config.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(self.params, self.train_config.max_grad_norm)
# only step if we are not accumulating
with self.timer('optimizer_step'):
# self.optimizer.step()
if not self.do_grad_scale:
self.optimizer.step()
else:
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad(set_to_none=True)
if self.adapter and isinstance(self.adapter, CustomAdapter):
self.adapter.post_weight_update()
if self.ema is not None:
with self.timer('ema_update'):
self.ema.update()
else:
# gradient accumulation. Just a place for breakpoint
pass
# TODO Should we only step scheduler on grad step? If so, need to recalculate last step
with self.timer('scheduler_step'):
self.lr_scheduler.step()
if self.embedding is not None:
with self.timer('restore_embeddings'):
# Let's make sure we don't update any embedding weights besides the newly added token
self.embedding.restore_embeddings()
if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter):
with self.timer('restore_adapter'):
# Let's make sure we don't update any embedding weights besides the newly added token
self.adapter.restore_embeddings()
loss_dict = OrderedDict(
{'loss': loss.item()}
)
self.end_of_training_loop()
return loss_dict
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