import time from collections import OrderedDict import os from toolkit.kohya_model_util import load_vae from toolkit.lora_special import LoRASpecialNetwork from toolkit.optimizer import get_optimizer from toolkit.paths import REPOS_ROOT import sys sys.path.append(REPOS_ROOT) sys.path.append(os.path.join(REPOS_ROOT, 'leco')) from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline from jobs.process import BaseTrainProcess from toolkit.metadata import get_meta_for_safetensors from toolkit.train_tools import get_torch_dtype, apply_noise_offset import gc import torch from tqdm import tqdm from leco import train_util, model_util from toolkit.config_modules import SaveConfig, LogingConfig, SampleConfig, NetworkConfig, TrainConfig, ModelConfig from toolkit.stable_diffusion_model import StableDiffusion, PromptEmbeds def flush(): torch.cuda.empty_cache() gc.collect() UNET_IN_CHANNELS = 4 # Stable Diffusion の in_channels は 4 で固定。XLも同じ。 VAE_SCALE_FACTOR = 8 # 2 ** (len(vae.config.block_out_channels) - 1) = 8 class BaseSDTrainProcess(BaseTrainProcess): def __init__(self, process_id: int, job, config: OrderedDict): 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.network_config = NetworkConfig(**self.get_conf('network', None)) self.training_folder = self.get_conf('training_folder', self.job.training_folder) 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', {})) self.logging_config = LogingConfig(**self.get_conf('logging', {})) self.optimizer = None self.lr_scheduler = None self.sd: 'StableDiffusion' = None # added later self.network = None def sample(self, step=None): sample_folder = os.path.join(self.save_root, 'samples') if not os.path.exists(sample_folder): os.makedirs(sample_folder, exist_ok=True) if self.network is not None: self.network.eval() # 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 original_device_dict = { 'vae': self.sd.vae.device, 'unet': self.sd.unet.device, # 'tokenizer': self.sd.tokenizer.device, } # handle sdxl text encoder if isinstance(self.sd.text_encoder, list): for encoder, i in zip(self.sd.text_encoder, range(len(self.sd.text_encoder))): original_device_dict[f'text_encoder_{i}'] = encoder.device encoder.to(self.device_torch) else: original_device_dict['text_encoder'] = self.sd.text_encoder.device self.sd.text_encoder.to(self.device_torch) self.sd.vae.to(self.device_torch) self.sd.unet.to(self.device_torch) # self.sd.text_encoder.to(self.device_torch) # self.sd.tokenizer.to(self.device_torch) # TODO add clip skip if self.sd.is_xl: pipeline = StableDiffusionXLPipeline( vae=self.sd.vae, unet=self.sd.unet, text_encoder=self.sd.text_encoder[0], text_encoder_2=self.sd.text_encoder[1], tokenizer=self.sd.tokenizer[0], tokenizer_2=self.sd.tokenizer[1], scheduler=self.sd.noise_scheduler, ) else: pipeline = StableDiffusionPipeline( vae=self.sd.vae, unet=self.sd.unet, text_encoder=self.sd.text_encoder, tokenizer=self.sd.tokenizer, scheduler=self.sd.noise_scheduler, safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) # disable progress bar pipeline.set_progress_bar_config(disable=True) start_seed = self.sample_config.seed start_multiplier = self.network.multiplier current_seed = start_seed pipeline.to(self.device_torch) with self.network: with torch.no_grad(): if self.network is not None: assert self.network.is_active if self.logging_config.verbose: print("network_state", { 'is_active': self.network.is_active, 'multiplier': self.network.multiplier, }) for i in tqdm(range(len(self.sample_config.prompts)), desc=f"Generating Samples - step: {step}", leave=False): raw_prompt = self.sample_config.prompts[i] neg = self.sample_config.neg multiplier = self.sample_config.network_multiplier p_split = raw_prompt.split('--') prompt = p_split[0].strip() if len(p_split) > 1: for split in p_split: flag = split[:1] content = split[1:].strip() if flag == 'n': neg = content elif flag == 'm': # multiplier multiplier = float(content) height = self.sample_config.height width = self.sample_config.width height = max(64, height - height % 8) # round to divisible by 8 width = max(64, width - width % 8) # round to divisible by 8 if self.sample_config.walk_seed: current_seed += i if self.network is not None: self.network.multiplier = multiplier torch.manual_seed(current_seed) torch.cuda.manual_seed(current_seed) img = pipeline( prompt, height=height, width=width, num_inference_steps=self.sample_config.sample_steps, guidance_scale=self.sample_config.guidance_scale, negative_prompt=neg, ).images[0] step_num = '' if step is not None: # zero-pad 9 digits step_num = f"_{str(step).zfill(9)}" seconds_since_epoch = int(time.time()) # zero-pad 2 digits i_str = str(i).zfill(2) filename = f"{seconds_since_epoch}{step_num}_{i_str}.png" output_path = os.path.join(sample_folder, filename) img.save(output_path) # clear pipeline and cache to reduce vram usage del pipeline torch.cuda.empty_cache() # restore training state torch.set_rng_state(rng_state) if cuda_rng_state is not None: torch.cuda.set_rng_state(cuda_rng_state) self.sd.vae.to(original_device_dict['vae']) self.sd.unet.to(original_device_dict['unet']) if isinstance(self.sd.text_encoder, list): for encoder, i in zip(self.sd.text_encoder, range(len(self.sd.text_encoder))): encoder.to(original_device_dict[f'text_encoder_{i}']) else: self.sd.text_encoder.to(original_device_dict['text_encoder']) if self.network is not None: self.network.train() self.network.multiplier = start_multiplier # self.sd.tokenizer.to(original_device_dict['tokenizer']) def update_training_metadata(self): self.add_meta(OrderedDict({"training_info": self.get_training_info()})) def get_training_info(self): info = OrderedDict({ 'step': self.step_num + 1 }) return info 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: # TODO handle dreambooth, fine tuning, etc self.network.save_weights( file_path, dtype=get_torch_dtype(self.save_config.dtype), metadata=save_meta ) else: # TODO handle dreambooth, fine tuning, etc # will probably have to convert dict back to LDM ValueError("Non network training is not currently supported") self.print(f"Saved to {file_path}") # 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 get_latent_noise( self, height=None, width=None, pixel_height=None, pixel_width=None, ): if height is None and pixel_height is None: raise ValueError("height or pixel_height must be specified") if width is None and pixel_width is None: raise ValueError("width or pixel_width must be specified") if height is None: height = pixel_height // VAE_SCALE_FACTOR if width is None: width = pixel_width // VAE_SCALE_FACTOR noise = torch.randn( ( self.train_config.batch_size, UNET_IN_CHANNELS, height, width, ), device="cpu", ) noise = apply_noise_offset(noise, self.train_config.noise_offset) return noise def hook_train_loop(self): # return loss return 0.0 def get_time_ids_from_latents(self, latents): bs, ch, h, w = list(latents.shape) height = h * VAE_SCALE_FACTOR width = w * VAE_SCALE_FACTOR dtype = get_torch_dtype(self.train_config.dtype) if self.sd.is_xl: prompt_ids = train_util.get_add_time_ids( height, width, dynamic_crops=False, # look into this dtype=dtype, ).to(self.device_torch, dtype=dtype) return train_util.concat_embeddings( prompt_ids, prompt_ids, bs ) else: return None def predict_noise( self, latents: torch.FloatTensor, text_embeddings: PromptEmbeds, timestep: int, guidance_scale=7.5, guidance_rescale=0.7, add_time_ids=None, **kwargs, ): if self.sd.is_xl: if add_time_ids is None: add_time_ids = self.get_time_ids_from_latents(latents) # todo LECOs code looks like it is omitting noise_pred noise_pred = train_util.predict_noise_xl( self.sd.unet, self.sd.noise_scheduler, timestep, latents, text_embeddings.text_embeds, text_embeddings.pooled_embeds, add_time_ids, guidance_scale=guidance_scale, guidance_rescale=guidance_rescale ) # compute the previous noisy sample x_t -> x_t-1 latents = self.sd.noise_scheduler.step(noise_pred, timestep, latents).prev_sample else: noise_pred = train_util.predict_noise( self.sd.unet, self.sd.noise_scheduler, timestep, latents, text_embeddings.text_embeds, guidance_scale=guidance_scale ) # compute the previous noisy sample x_t -> x_t-1 latents = self.sd.noise_scheduler.step(noise_pred, timestep, latents).prev_sample return latents # ref: https://github.com/huggingface/diffusers/blob/0bab447670f47c28df60fbd2f6a0f833f75a16f5/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L746 def diffuse_some_steps( self, latents: torch.FloatTensor, text_embeddings: PromptEmbeds, total_timesteps: int = 1000, start_timesteps=0, guidance_scale=1, add_time_ids=None, **kwargs, ): for timestep in tqdm(self.sd.noise_scheduler.timesteps[start_timesteps:total_timesteps], leave=False): latents = self.predict_noise( latents, text_embeddings, timestep, guidance_scale=guidance_scale, add_time_ids=add_time_ids, **kwargs, ) # return latents_steps return latents def run(self): super().run() ### HOOK ### self.hook_before_model_load() dtype = get_torch_dtype(self.train_config.dtype) if self.model_config.is_xl: tokenizer, text_encoders, unet, noise_scheduler = model_util.load_models_xl( self.model_config.name_or_path, scheduler_name=self.train_config.noise_scheduler, weight_dtype=dtype, ) for text_encoder in text_encoders: text_encoder.to(self.device_torch, dtype=dtype) text_encoder.requires_grad_(False) text_encoder.eval() text_encoder = text_encoders else: tokenizer, text_encoder, unet, noise_scheduler = model_util.load_models( self.model_config.name_or_path, scheduler_name=self.train_config.noise_scheduler, v2=self.model_config.is_v2, v_pred=self.model_config.is_v_pred, ) text_encoder.to(self.device_torch, dtype=dtype) text_encoder.eval() # just for now or of we want to load a custom one # put on cpu for now, we only need it when sampling vae = load_vae(self.model_config.name_or_path, dtype=dtype).to('cpu', dtype=dtype) vae.eval() self.sd = StableDiffusion(vae, tokenizer, text_encoder, unet, noise_scheduler, is_xl=self.model_config.is_xl) unet.to(self.device_torch, dtype=dtype) if self.train_config.xformers: unet.enable_xformers_memory_efficient_attention() unet.requires_grad_(False) unet.eval() if self.network_config is not None: self.network = LoRASpecialNetwork( text_encoder=text_encoder, unet=unet, lora_dim=self.network_config.rank, multiplier=1.0, alpha=self.network_config.alpha, train_unet=self.train_config.train_unet, train_text_encoder=self.train_config.train_text_encoder, ) self.network.force_to(self.device_torch, dtype=dtype) 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 ) else: params = [] # assume dreambooth/finetune if self.train_config.train_text_encoder: text_encoder.requires_grad_(True) text_encoder.train() params += text_encoder.parameters() if self.train_config.train_unet: unet.requires_grad_(True) unet.train() params += unet.parameters() ### 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 = train_util.get_lr_scheduler( self.train_config.lr_scheduler, optimizer, max_iterations=self.train_config.steps, lr_min=self.train_config.lr / 100, # not sure why leco did this, but ill do it to ) self.lr_scheduler = lr_scheduler ### HOOK ### self.hook_before_train_loop() # 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 ) self.step_num = 0 for step in range(self.train_config.steps): # todo handle dataloader here maybe, not sure ### HOOK ### loss_dict = self.hook_train_loop() if self.train_config.optimizer.startswith('dadaptation'): 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 self.sample_config.sample_every and self.step_num % self.sample_config.sample_every == 0: # print above the progress bar self.sample(self.step_num) if self.save_config.save_every and self.step_num % self.save_config.save_every == 0: # 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 self.sample(self.step_num + 1) print("") self.save() del ( self.sd, unet, noise_scheduler, optimizer, self.network, tokenizer, text_encoder, ) flush()