import glob from collections import OrderedDict import os from typing import Union from torch.utils.data import DataLoader from toolkit.data_loader import get_dataloader_from_datasets from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO from toolkit.embedding import Embedding from toolkit.lora_special import LoRASpecialNetwork from toolkit.optimizer import get_optimizer from toolkit.paths import CONFIG_ROOT from toolkit.scheduler import get_lr_scheduler from toolkit.stable_diffusion_model import StableDiffusion from jobs.process import BaseTrainProcess from toolkit.metadata import get_meta_for_safetensors, load_metadata_from_safetensors, add_base_model_info_to_meta from toolkit.train_tools import get_torch_dtype import gc import torch from tqdm import tqdm from toolkit.config_modules import SaveConfig, LogingConfig, SampleConfig, NetworkConfig, TrainConfig, ModelConfig, \ GenerateImageConfig, EmbeddingConfig, DatasetConfig, preprocess_dataset_raw_config def flush(): torch.cuda.empty_cache() gc.collect() class BaseSDTrainProcess(BaseTrainProcess): def __init__(self, process_id: int, job, config: OrderedDict, custom_pipeline=None): super().__init__(process_id, job, config) self.sd: StableDiffusion self.embedding: Union[Embedding, None] = None self.custom_pipeline = custom_pipeline self.step_num = 0 self.start_step = 0 self.device = self.get_conf('device', self.job.device) self.device_torch = torch.device(self.device) network_config = self.get_conf('network', None) if network_config is not None: self.network_config = NetworkConfig(**network_config) else: self.network_config = None self.train_config = TrainConfig(**self.get_conf('train', {})) self.model_config = ModelConfig(**self.get_conf('model', {})) self.save_config = SaveConfig(**self.get_conf('save', {})) self.sample_config = SampleConfig(**self.get_conf('sample', {})) first_sample_config = self.get_conf('first_sample', None) if first_sample_config is not None: self.has_first_sample_requested = True self.first_sample_config = SampleConfig(**first_sample_config) else: self.has_first_sample_requested = False self.first_sample_config = self.sample_config self.logging_config = LogingConfig(**self.get_conf('logging', {})) self.optimizer = None self.lr_scheduler = None self.data_loader: Union[DataLoader, None] = None self.data_loader_reg: Union[DataLoader, None] = None self.trigger_word = self.get_conf('trigger_word', None) raw_datasets = self.get_conf('datasets', None) if raw_datasets is not None and len(raw_datasets) > 0: raw_datasets = preprocess_dataset_raw_config(raw_datasets) self.datasets = None self.datasets_reg = None if raw_datasets is not None and len(raw_datasets) > 0: for raw_dataset in raw_datasets: dataset = DatasetConfig(**raw_dataset) if dataset.is_reg: if self.datasets_reg is None: self.datasets_reg = [] self.datasets_reg.append(dataset) else: if self.datasets is None: self.datasets = [] self.datasets.append(dataset) self.embed_config = None embedding_raw = self.get_conf('embedding', None) if embedding_raw is not None: self.embed_config = EmbeddingConfig(**embedding_raw) if self.embed_config is None and self.network_config is None: # get the latest checkpoint # check to see if we have a latest save latest_save_path = self.get_latest_save_path() if latest_save_path is not None: print(f"#### IMPORTANT RESUMING FROM {latest_save_path} ####") self.model_config.name_or_path = latest_save_path meta = load_metadata_from_safetensors(latest_save_path) # if 'training_info' in Orderdict keys if 'training_info' in meta and 'step' in meta['training_info']: self.step_num = meta['training_info']['step'] self.start_step = self.step_num print(f"Found step {self.step_num} in metadata, starting from there") self.sd = StableDiffusion( device=self.device, model_config=self.model_config, dtype=self.train_config.dtype, custom_pipeline=self.custom_pipeline, ) # to hold network if there is one self.network = None self.embedding = None def sample(self, step=None, is_first=False): sample_folder = os.path.join(self.save_root, 'samples') gen_img_config_list = [] sample_config = self.first_sample_config if is_first else self.sample_config start_seed = sample_config.seed current_seed = start_seed for i in range(len(sample_config.prompts)): if sample_config.walk_seed: current_seed = start_seed + i step_num = '' if step is not None: # zero-pad 9 digits step_num = f"_{str(step).zfill(9)}" filename = f"[time]_{step_num}_[count].{self.sample_config.ext}" output_path = os.path.join(sample_folder, filename) prompt = sample_config.prompts[i] # add embedding if there is one # note: diffusers will automatically expand the trigger to the number of added tokens # ie test123 will become test123 test123_1 test123_2 etc. Do not add this yourself here if self.embedding is not None: prompt = self.embedding.inject_embedding_to_prompt( prompt, ) if self.trigger_word is not None: prompt = self.sd.inject_trigger_into_prompt( prompt, self.trigger_word ) gen_img_config_list.append(GenerateImageConfig( prompt=prompt, # it will autoparse the prompt width=sample_config.width, height=sample_config.height, negative_prompt=sample_config.neg, seed=current_seed, guidance_scale=sample_config.guidance_scale, guidance_rescale=sample_config.guidance_rescale, num_inference_steps=sample_config.sample_steps, network_multiplier=sample_config.network_multiplier, output_path=output_path, output_ext=sample_config.ext, )) # send to be generated self.sd.generate_images(gen_img_config_list) def update_training_metadata(self): o_dict = OrderedDict({ "training_info": self.get_training_info() }) if self.model_config.is_v2: o_dict['ss_v2'] = True o_dict['ss_base_model_version'] = 'sd_2.1' elif self.model_config.is_xl: o_dict['ss_base_model_version'] = 'sdxl_1.0' else: o_dict['ss_base_model_version'] = 'sd_1.5' o_dict = add_base_model_info_to_meta( o_dict, is_v2=self.model_config.is_v2, is_xl=self.model_config.is_xl, ) o_dict['ss_output_name'] = self.job.name if self.trigger_word is not None: # just so auto1111 will pick it up o_dict['ss_tag_frequency'] = { 'actfig': { 'actfig': 1 } } self.add_meta(o_dict) def get_training_info(self): info = OrderedDict({ 'step': self.step_num + 1 }) return info def clean_up_saves(self): # remove old saves # get latest saved step if os.path.exists(self.save_root): latest_file = None # pattern is {job_name}_{zero_filles_step}.safetensors but NOT {job_name}.safetensors pattern = f"{self.job.name}_*.safetensors" files = glob.glob(os.path.join(self.save_root, pattern)) if len(files) > self.save_config.max_step_saves_to_keep: # remove all but the latest max_step_saves_to_keep files.sort(key=os.path.getctime) for file in files[:-self.save_config.max_step_saves_to_keep]: self.print(f"Removing old save: {file}") os.remove(file) return latest_file else: return None def save(self, step=None): if not os.path.exists(self.save_root): os.makedirs(self.save_root, exist_ok=True) step_num = '' if step is not None: # zeropad 9 digits step_num = f"_{str(step).zfill(9)}" self.update_training_metadata() filename = f'{self.job.name}{step_num}.safetensors' file_path = os.path.join(self.save_root, filename) # prepare meta save_meta = get_meta_for_safetensors(self.meta, self.job.name) if self.network is not None: prev_multiplier = self.network.multiplier self.network.multiplier = 1.0 if self.network_config.normalize: # apply the normalization self.network.apply_stored_normalizer() self.network.save_weights( file_path, dtype=get_torch_dtype(self.save_config.dtype), metadata=save_meta ) self.network.multiplier = prev_multiplier elif self.embedding is not None: # set current step self.embedding.step = self.step_num # change filename to pt if that is set if self.embed_config.save_format == "pt": # replace extension file_path = os.path.splitext(file_path)[0] + ".pt" self.embedding.save(file_path) else: self.sd.save( file_path, save_meta, get_torch_dtype(self.save_config.dtype) ) self.print(f"Saved to {file_path}") self.clean_up_saves() # Called before the model is loaded def hook_before_model_load(self): # override in subclass pass def hook_add_extra_train_params(self, params): # override in subclass return params def hook_before_train_loop(self): pass def before_dataset_load(self): pass def hook_train_loop(self, batch): # return loss return 0.0 def get_latest_save_path(self): # get latest saved step if os.path.exists(self.save_root): latest_file = None # pattern is {job_name}_{zero_filles_step}.safetensors or {job_name}.safetensors pattern = f"{self.job.name}*.safetensors" files = glob.glob(os.path.join(self.save_root, pattern)) if len(files) > 0: latest_file = max(files, key=os.path.getctime) # try pt pattern = f"{self.job.name}*.pt" files = glob.glob(os.path.join(self.save_root, pattern)) if len(files) > 0: latest_file = max(files, key=os.path.getctime) return latest_file else: return None def load_weights(self, path): if self.network is not None: self.network.load_weights(path) meta = load_metadata_from_safetensors(path) # if 'training_info' in Orderdict keys if 'training_info' in meta and 'step' in meta['training_info']: self.step_num = meta['training_info']['step'] self.start_step = self.step_num print(f"Found step {self.step_num} in metadata, starting from there") else: print("load_weights not implemented for non-network models") def process_general_training_batch(self, batch): with torch.no_grad(): imgs = batch.tensor prompts = batch.get_caption_list() is_reg_list = batch.get_is_reg_list() conditioned_prompts = [] for prompt, is_reg in zip(prompts, is_reg_list): # make sure the embedding is in the prompts if self.embedding is not None: prompt = self.embedding.inject_embedding_to_prompt( prompt, expand_token=True, add_if_not_present=True, ) # make sure trigger is in the prompts if not a regularization run if self.trigger_word is not None and not is_reg: prompt = self.sd.inject_trigger_into_prompt( prompt, trigger=self.trigger_word, add_if_not_present=True, ) conditioned_prompts.append(prompt) batch_size = imgs.shape[0] dtype = get_torch_dtype(self.train_config.dtype) imgs = imgs.to(self.device_torch, dtype=dtype) latents = self.sd.encode_images(imgs) 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, (batch_size,), device=self.device_torch) timesteps = timesteps.long() # get noise noise = self.sd.get_latent_noise( pixel_height=imgs.shape[2], pixel_width=imgs.shape[3], batch_size=batch_size, noise_offset=self.train_config.noise_offset ).to(self.device_torch, dtype=dtype) noisy_latents = self.sd.noise_scheduler.add_noise(latents, noise, timesteps) # remove grads for these noisy_latents.requires_grad = False noisy_latents = noisy_latents.detach() noise.requires_grad = False noise = noise.detach() return noisy_latents, noise, timesteps, conditioned_prompts, imgs def run(self): # run base process run BaseTrainProcess.run(self) ### HOOk ### self.before_dataset_load() # load datasets if passed in the root process if self.datasets is not None: self.data_loader = get_dataloader_from_datasets(self.datasets, self.train_config.batch_size) if self.datasets_reg is not None: self.data_loader_reg = get_dataloader_from_datasets(self.datasets_reg, self.train_config.batch_size) ### HOOK ### self.hook_before_model_load() # run base sd process run self.sd.load_model() if self.train_config.gradient_checkpointing: # may get disabled elsewhere self.sd.unet.enable_gradient_checkpointing() dtype = get_torch_dtype(self.train_config.dtype) # model is loaded from BaseSDProcess unet = self.sd.unet vae = self.sd.vae tokenizer = self.sd.tokenizer text_encoder = self.sd.text_encoder noise_scheduler = self.sd.noise_scheduler if self.train_config.xformers: vae.set_use_memory_efficient_attention_xformers(True) unet.enable_xformers_memory_efficient_attention() if self.train_config.gradient_checkpointing: unet.enable_gradient_checkpointing() # if isinstance(text_encoder, list): # for te in text_encoder: # te.enable_gradient_checkpointing() # else: # text_encoder.enable_gradient_checkpointing() unet.to(self.device_torch, dtype=dtype) unet.requires_grad_(False) unet.eval() vae = vae.to(torch.device('cpu'), dtype=dtype) vae.requires_grad_(False) vae.eval() if self.network_config is not None: self.network = LoRASpecialNetwork( text_encoder=text_encoder, unet=unet, lora_dim=self.network_config.linear, multiplier=1.0, alpha=self.network_config.linear_alpha, train_unet=self.train_config.train_unet, train_text_encoder=self.train_config.train_text_encoder, conv_lora_dim=self.network_config.conv, conv_alpha=self.network_config.conv_alpha, ) self.network.force_to(self.device_torch, dtype=dtype) # give network to sd so it can use it self.sd.network = self.network self.network.apply_to( text_encoder, unet, self.train_config.train_text_encoder, self.train_config.train_unet ) self.network.prepare_grad_etc(text_encoder, unet) params = self.network.prepare_optimizer_params( text_encoder_lr=self.train_config.lr, unet_lr=self.train_config.lr, default_lr=self.train_config.lr ) if self.train_config.gradient_checkpointing: self.network.enable_gradient_checkpointing() # set the network to normalize if we are self.network.is_normalizing = self.network_config.normalize latest_save_path = self.get_latest_save_path() if latest_save_path is not None: self.print(f"#### IMPORTANT RESUMING FROM {latest_save_path} ####") self.print(f"Loading from {latest_save_path}") self.load_weights(latest_save_path) self.network.multiplier = 1.0 elif self.embed_config is not None: self.embedding = Embedding( sd=self.sd, embed_config=self.embed_config ) latest_save_path = self.get_latest_save_path() # load last saved weights if latest_save_path is not None: self.embedding.load_embedding_from_file(latest_save_path, self.device_torch) # resume state from embedding self.step_num = self.embedding.step self.start_step = self.step_num # set trainable params params = self.embedding.get_trainable_params() else: # set them to train or not if self.train_config.train_unet: self.sd.unet.requires_grad_(True) self.sd.unet.train() else: self.sd.unet.requires_grad_(False) self.sd.unet.eval() if self.train_config.train_text_encoder: if isinstance(self.sd.text_encoder, list): for te in self.sd.text_encoder: te.requires_grad_(True) te.train() else: self.sd.text_encoder.requires_grad_(True) self.sd.text_encoder.train() else: if isinstance(self.sd.text_encoder, list): for te in self.sd.text_encoder: te.requires_grad_(False) te.eval() else: self.sd.text_encoder.requires_grad_(False) self.sd.text_encoder.eval() # will only return savable weights and ones with grad params = self.sd.prepare_optimizer_params( unet=self.train_config.train_unet, text_encoder=self.train_config.train_text_encoder, text_encoder_lr=self.train_config.lr, unet_lr=self.train_config.lr, default_lr=self.train_config.lr ) ### HOOK ### params = self.hook_add_extra_train_params(params) optimizer_type = self.train_config.optimizer.lower() optimizer = get_optimizer(params, optimizer_type, learning_rate=self.train_config.lr, optimizer_params=self.train_config.optimizer_params) self.optimizer = optimizer lr_scheduler = get_lr_scheduler( self.train_config.lr_scheduler, optimizer, max_iterations=self.train_config.steps, lr_min=self.train_config.lr / 100, ) self.lr_scheduler = lr_scheduler ### HOOK ### self.hook_before_train_loop() if self.has_first_sample_requested: self.print("Generating first sample from first sample config") self.sample(0, is_first=True) # sample first if self.train_config.skip_first_sample: self.print("Skipping first sample due to config setting") else: self.print("Generating baseline samples before training") self.sample(0) self.progress_bar = tqdm( total=self.train_config.steps, desc=self.job.name, leave=True, initial=self.step_num, iterable=range(0, self.train_config.steps), ) if self.data_loader is not None: dataloader = self.data_loader dataloader_iterator = iter(dataloader) else: dataloader = None dataloader_iterator = None if self.data_loader_reg is not None: dataloader_reg = self.data_loader_reg dataloader_iterator_reg = iter(dataloader_reg) else: dataloader_reg = None dataloader_iterator_reg = None # zero any gradients optimizer.zero_grad() # self.step_num = 0 for step in range(self.step_num, self.train_config.steps): with torch.no_grad(): # if is even step and we have a reg dataset, use that # todo improve this logic to send one of each through if we can buckets and batch size might be an issue is_reg_step = False is_save_step = self.save_config.save_every and self.step_num % self.save_config.save_every == 0 is_sample_step = self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0 # don't do a reg step on sample or save steps as we dont want to normalize on those if step % 2 == 0 and dataloader_reg is not None and not is_save_step and not is_sample_step: try: batch = next(dataloader_iterator_reg) except StopIteration: # hit the end of an epoch, reset dataloader_iterator_reg = iter(dataloader_reg) batch = next(dataloader_iterator_reg) is_reg_step = True elif dataloader is not None: try: batch = next(dataloader_iterator) except StopIteration: # hit the end of an epoch, reset dataloader_iterator = iter(dataloader) batch = next(dataloader_iterator) else: batch = None # turn on normalization if we are using it and it is not on if self.network is not None and self.network_config.normalize and not self.network.is_normalizing: self.network.is_normalizing = True ### HOOK ### loss_dict = self.hook_train_loop(batch) flush() with torch.no_grad(): if self.train_config.optimizer.lower().startswith('dadaptation') or \ self.train_config.optimizer.lower().startswith('prodigy'): learning_rate = ( optimizer.param_groups[0]["d"] * optimizer.param_groups[0]["lr"] ) else: learning_rate = optimizer.param_groups[0]['lr'] prog_bar_string = f"lr: {learning_rate:.1e}" for key, value in loss_dict.items(): prog_bar_string += f" {key}: {value:.3e}" self.progress_bar.set_postfix_str(prog_bar_string) # don't do on first step if self.step_num != self.start_step: # pause progress bar self.progress_bar.unpause() # makes it so doesn't track time if is_sample_step: # print above the progress bar self.sample(self.step_num) if is_save_step: # print above the progress bar self.print(f"Saving at step {self.step_num}") self.save(self.step_num) if self.logging_config.log_every and self.step_num % self.logging_config.log_every == 0: # log to tensorboard if self.writer is not None: for key, value in loss_dict.items(): self.writer.add_scalar(f"{key}", value, self.step_num) self.writer.add_scalar(f"lr", learning_rate, self.step_num) self.progress_bar.refresh() # sets progress bar to match out step self.progress_bar.update(step - self.progress_bar.n) # end of step self.step_num = step # apply network normalizer if we are using it, not on regularization steps if self.network is not None and self.network.is_normalizing and not is_reg_step: self.network.apply_stored_normalizer() # if the batch is a DataLoaderBatchDTO, then we need to clean it up if isinstance(batch, DataLoaderBatchDTO): batch.cleanup() self.sample(self.step_num + 1) print("") self.save() del ( self.sd, unet, noise_scheduler, optimizer, self.network, tokenizer, text_encoder, ) flush()