From 42f6edf3a886287b99770bc7a8c0bafd3fa03f39 Mon Sep 17 00:00:00 2001 From: minux302 Date: Fri, 15 Nov 2024 23:48:51 +0900 Subject: [PATCH] fix for adding controlnet --- flux_train_control_net.py | 1340 +++++++++++++++++++++-------------- flux_train_network.py | 3 - library/flux_train_utils.py | 32 +- library/flux_utils.py | 11 +- 4 files changed, 855 insertions(+), 531 deletions(-) diff --git a/flux_train_control_net.py b/flux_train_control_net.py index 704c4d32..8a7be75f 100644 --- a/flux_train_control_net.py +++ b/flux_train_control_net.py @@ -1,563 +1,860 @@ +# training with captions + +# Swap blocks between CPU and GPU: +# This implementation is inspired by and based on the work of 2kpr. +# Many thanks to 2kpr for the original concept and implementation of memory-efficient offloading. +# The original idea has been adapted and extended to fit the current project's needs. + +# Key features: +# - CPU offloading during forward and backward passes +# - Use of fused optimizer and grad_hook for efficient gradient processing +# - Per-block fused optimizer instances + import argparse +from concurrent.futures import ThreadPoolExecutor import copy import math -import random -from typing import Any, Optional +import os +from multiprocessing import Value +import time +from typing import List, Optional, Tuple, Union +import toml + +from tqdm import tqdm import torch -from accelerate import Accelerator +import torch.nn as nn +from library import utils from library.device_utils import init_ipex, clean_memory_on_device init_ipex() -from library import flux_models, flux_train_utils, flux_utils, sd3_train_utils, strategy_base, strategy_flux, train_util -import train_network -from library.utils import setup_logging +from accelerate.utils import set_seed +from library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux +from library.sd3_train_utils import FlowMatchEulerDiscreteScheduler + +import library.train_util as train_util + +from library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) +import library.config_util as config_util -class FluxNetworkTrainer(train_network.NetworkTrainer): - def __init__(self): - super().__init__() - self.sample_prompts_te_outputs = None - self.is_schnell: Optional[bool] = None - self.is_swapping_blocks: bool = False +# import library.sdxl_train_util as sdxl_train_util +from library.config_util import ( + ConfigSanitizer, + BlueprintGenerator, +) +from library.custom_train_functions import apply_masked_loss, add_custom_train_arguments - def assert_extra_args(self, args, train_dataset_group): - super().assert_extra_args(args, train_dataset_group) - # sdxl_train_util.verify_sdxl_training_args(args) - if args.fp8_base_unet: - args.fp8_base = True # if fp8_base_unet is enabled, fp8_base is also enabled for FLUX.1 +def train(args): + train_util.verify_training_args(args) + train_util.prepare_dataset_args(args, True) + # sdxl_train_util.verify_sdxl_training_args(args) + deepspeed_utils.prepare_deepspeed_args(args) + setup_logging(args, reset=True) - if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: - logger.warning( - "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります" - ) - args.cache_text_encoder_outputs = True + # temporary: backward compatibility for deprecated options. remove in the future + if not args.skip_cache_check: + args.skip_cache_check = args.skip_latents_validity_check - if args.cache_text_encoder_outputs: - assert ( - train_dataset_group.is_text_encoder_output_cacheable() - ), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" + # assert ( + # not args.weighted_captions + # ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません" + if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: + logger.warning( + "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります" + ) + args.cache_text_encoder_outputs = True - # prepare CLIP-L/T5XXL training flags - self.train_clip_l = not args.network_train_unet_only - self.train_t5xxl = False # default is False even if args.network_train_unet_only is False + if args.cpu_offload_checkpointing and not args.gradient_checkpointing: + logger.warning( + "cpu_offload_checkpointing is enabled, so gradient_checkpointing is also enabled / cpu_offload_checkpointingが有効になっているため、gradient_checkpointingも有効になります" + ) + args.gradient_checkpointing = True - if args.max_token_length is not None: - logger.warning("max_token_length is not used in Flux training / max_token_lengthはFluxのトレーニングでは使用されません") + assert ( + args.blocks_to_swap is None or args.blocks_to_swap == 0 + ) or not args.cpu_offload_checkpointing, ( + "blocks_to_swap is not supported with cpu_offload_checkpointing / blocks_to_swapはcpu_offload_checkpointingと併用できません" + ) - assert ( - args.blocks_to_swap is None or args.blocks_to_swap == 0 - ) or not args.cpu_offload_checkpointing, "blocks_to_swap is not supported with cpu_offload_checkpointing / blocks_to_swapはcpu_offload_checkpointingと併用できません" + cache_latents = args.cache_latents + use_dreambooth_method = args.in_json is None - # deprecated split_mode option - if args.split_mode: - if args.blocks_to_swap is not None: + if args.seed is not None: + set_seed(args.seed) # 乱数系列を初期化する + + # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization. + if args.cache_latents: + latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy( + args.cache_latents_to_disk, args.vae_batch_size, args.skip_cache_check + ) + strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) + + # データセットを準備する + if args.dataset_class is None: + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) + if args.dataset_config is not None: + logger.info(f"Load dataset config from {args.dataset_config}") + user_config = config_util.load_user_config(args.dataset_config) + ignored = ["train_data_dir", "in_json"] + if any(getattr(args, attr) is not None for attr in ignored): logger.warning( - "split_mode is deprecated. Because `--blocks_to_swap` is set, `--split_mode` is ignored." - " / split_modeは非推奨です。`--blocks_to_swap`が設定されているため、`--split_mode`は無視されます。" - ) - else: - logger.warning( - "split_mode is deprecated. Please use `--blocks_to_swap` instead. `--blocks_to_swap 18` is automatically set." - " / split_modeは非推奨です。代わりに`--blocks_to_swap`を使用してください。`--blocks_to_swap 18`が自動的に設定されました。" - ) - args.blocks_to_swap = 18 # 18 is safe for most cases - - train_dataset_group.verify_bucket_reso_steps(32) # TODO check this - - def load_target_model(self, args, weight_dtype, accelerator): - # currently offload to cpu for some models - - # if the file is fp8 and we are using fp8_base, we can load it as is (fp8) - loading_dtype = None if args.fp8_base else weight_dtype - - # if we load to cpu, flux.to(fp8) takes a long time, so we should load to gpu in future - self.is_schnell, model = flux_utils.load_flow_model( - args.pretrained_model_name_or_path, loading_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors - ) - if args.fp8_base: - # check dtype of model - if model.dtype == torch.float8_e4m3fnuz or model.dtype == torch.float8_e5m2 or model.dtype == torch.float8_e5m2fnuz: - raise ValueError(f"Unsupported fp8 model dtype: {model.dtype}") - elif model.dtype == torch.float8_e4m3fn: - logger.info("Loaded fp8 FLUX model") - else: - logger.info( - "Cast FLUX model to fp8. This may take a while. You can reduce the time by using fp8 checkpoint." - " / FLUXモデルをfp8に変換しています。これには時間がかかる場合があります。fp8チェックポイントを使用することで時間を短縮できます。" - ) - model.to(torch.float8_e4m3fn) - - # if args.split_mode: - # model = self.prepare_split_model(model, weight_dtype, accelerator) - - self.is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0 - if self.is_swapping_blocks: - # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes. - logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}") - model.enable_block_swap(args.blocks_to_swap, accelerator.device) - - clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors) - clip_l.eval() - - # if the file is fp8 and we are using fp8_base (not unet), we can load it as is (fp8) - if args.fp8_base and not args.fp8_base_unet: - loading_dtype = None # as is - else: - loading_dtype = weight_dtype - - # loading t5xxl to cpu takes a long time, so we should load to gpu in future - t5xxl = flux_utils.load_t5xxl(args.t5xxl, loading_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors) - t5xxl.eval() - if args.fp8_base and not args.fp8_base_unet: - # check dtype of model - if t5xxl.dtype == torch.float8_e4m3fnuz or t5xxl.dtype == torch.float8_e5m2 or t5xxl.dtype == torch.float8_e5m2fnuz: - raise ValueError(f"Unsupported fp8 model dtype: {t5xxl.dtype}") - elif t5xxl.dtype == torch.float8_e4m3fn: - logger.info("Loaded fp8 T5XXL model") - - ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors) - - return flux_utils.MODEL_VERSION_FLUX_V1, [clip_l, t5xxl], ae, model - - def get_tokenize_strategy(self, args): - _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path) - - if args.t5xxl_max_token_length is None: - if is_schnell: - t5xxl_max_token_length = 256 - else: - t5xxl_max_token_length = 512 - else: - t5xxl_max_token_length = args.t5xxl_max_token_length - - logger.info(f"t5xxl_max_token_length: {t5xxl_max_token_length}") - return strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length, args.tokenizer_cache_dir) - - def get_tokenizers(self, tokenize_strategy: strategy_flux.FluxTokenizeStrategy): - return [tokenize_strategy.clip_l, tokenize_strategy.t5xxl] - - def get_latents_caching_strategy(self, args): - latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy(args.cache_latents_to_disk, args.vae_batch_size, False) - return latents_caching_strategy - - def get_text_encoding_strategy(self, args): - return strategy_flux.FluxTextEncodingStrategy(apply_t5_attn_mask=args.apply_t5_attn_mask) - - def post_process_network(self, args, accelerator, network, text_encoders, unet): - # check t5xxl is trained or not - self.train_t5xxl = network.train_t5xxl - - if self.train_t5xxl and args.cache_text_encoder_outputs: - raise ValueError( - "T5XXL is trained, so cache_text_encoder_outputs cannot be used / T5XXL学習時はcache_text_encoder_outputsは使用できません" - ) - - def get_models_for_text_encoding(self, args, accelerator, text_encoders): - if args.cache_text_encoder_outputs: - if self.train_clip_l and not self.train_t5xxl: - return text_encoders[0:1] # only CLIP-L is needed for encoding because T5XXL is cached - else: - return None # no text encoders are needed for encoding because both are cached - else: - return text_encoders # both CLIP-L and T5XXL are needed for encoding - - def get_text_encoders_train_flags(self, args, text_encoders): - return [self.train_clip_l, self.train_t5xxl] - - def get_text_encoder_outputs_caching_strategy(self, args): - if args.cache_text_encoder_outputs: - # if the text encoders is trained, we need tokenization, so is_partial is True - return strategy_flux.FluxTextEncoderOutputsCachingStrategy( - args.cache_text_encoder_outputs_to_disk, - args.text_encoder_batch_size, - args.skip_cache_check, - is_partial=self.train_clip_l or self.train_t5xxl, - apply_t5_attn_mask=args.apply_t5_attn_mask, - ) - else: - return None - - def cache_text_encoder_outputs_if_needed( - self, args, accelerator: Accelerator, unet, vae, text_encoders, dataset: train_util.DatasetGroup, weight_dtype - ): - if args.cache_text_encoder_outputs: - if not args.lowram: - # メモリ消費を減らす - logger.info("move vae and unet to cpu to save memory") - org_vae_device = vae.device - org_unet_device = unet.device - vae.to("cpu") - unet.to("cpu") - clean_memory_on_device(accelerator.device) - - # When TE is not be trained, it will not be prepared so we need to use explicit autocast - logger.info("move text encoders to gpu") - text_encoders[0].to(accelerator.device, dtype=weight_dtype) # always not fp8 - text_encoders[1].to(accelerator.device) - - if text_encoders[1].dtype == torch.float8_e4m3fn: - # if we load fp8 weights, the model is already fp8, so we use it as is - self.prepare_text_encoder_fp8(1, text_encoders[1], text_encoders[1].dtype, weight_dtype) - else: - # otherwise, we need to convert it to target dtype - text_encoders[1].to(weight_dtype) - - with accelerator.autocast(): - dataset.new_cache_text_encoder_outputs(text_encoders, accelerator) - - # cache sample prompts - if args.sample_prompts is not None: - logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}") - - tokenize_strategy: strategy_flux.FluxTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy() - text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy() - - prompts = train_util.load_prompts(args.sample_prompts) - sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs - with accelerator.autocast(), torch.no_grad(): - for prompt_dict in prompts: - for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: - if p not in sample_prompts_te_outputs: - logger.info(f"cache Text Encoder outputs for prompt: {p}") - tokens_and_masks = tokenize_strategy.tokenize(p) - sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens( - tokenize_strategy, text_encoders, tokens_and_masks, args.apply_t5_attn_mask - ) - self.sample_prompts_te_outputs = sample_prompts_te_outputs - - accelerator.wait_for_everyone() - - # move back to cpu - if not self.is_train_text_encoder(args): - logger.info("move CLIP-L back to cpu") - text_encoders[0].to("cpu") - logger.info("move t5XXL back to cpu") - text_encoders[1].to("cpu") - clean_memory_on_device(accelerator.device) - - if not args.lowram: - logger.info("move vae and unet back to original device") - vae.to(org_vae_device) - unet.to(org_unet_device) - else: - # Text Encoderから毎回出力を取得するので、GPUに乗せておく - text_encoders[0].to(accelerator.device, dtype=weight_dtype) - text_encoders[1].to(accelerator.device) - - # def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype): - # noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype - - # # get size embeddings - # orig_size = batch["original_sizes_hw"] - # crop_size = batch["crop_top_lefts"] - # target_size = batch["target_sizes_hw"] - # embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype) - - # # concat embeddings - # encoder_hidden_states1, encoder_hidden_states2, pool2 = text_conds - # vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype) - # text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype) - - # noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) - # return noise_pred - - def sample_images(self, accelerator, args, epoch, global_step, device, ae, tokenizer, text_encoder, flux): - text_encoders = text_encoder # for compatibility - text_encoders = self.get_models_for_text_encoding(args, accelerator, text_encoders) - - flux_train_utils.sample_images( - accelerator, args, epoch, global_step, flux, ae, text_encoders, self.sample_prompts_te_outputs - ) - # return - - """ - class FluxUpperLowerWrapper(torch.nn.Module): - def __init__(self, flux_upper: flux_models.FluxUpper, flux_lower: flux_models.FluxLower, device: torch.device): - super().__init__() - self.flux_upper = flux_upper - self.flux_lower = flux_lower - self.target_device = device - - def prepare_block_swap_before_forward(self): - pass - - def forward(self, img, img_ids, txt, txt_ids, timesteps, y, guidance=None, txt_attention_mask=None): - self.flux_lower.to("cpu") - clean_memory_on_device(self.target_device) - self.flux_upper.to(self.target_device) - img, txt, vec, pe = self.flux_upper(img, img_ids, txt, txt_ids, timesteps, y, guidance, txt_attention_mask) - self.flux_upper.to("cpu") - clean_memory_on_device(self.target_device) - self.flux_lower.to(self.target_device) - return self.flux_lower(img, txt, vec, pe, txt_attention_mask) - - wrapper = FluxUpperLowerWrapper(self.flux_upper, flux, accelerator.device) - clean_memory_on_device(accelerator.device) - flux_train_utils.sample_images( - accelerator, args, epoch, global_step, wrapper, ae, text_encoders, self.sample_prompts_te_outputs - ) - clean_memory_on_device(accelerator.device) - """ - - def get_noise_scheduler(self, args: argparse.Namespace, device: torch.device) -> Any: - noise_scheduler = sd3_train_utils.FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift) - self.noise_scheduler_copy = copy.deepcopy(noise_scheduler) - return noise_scheduler - - def encode_images_to_latents(self, args, accelerator, vae, images): - return vae.encode(images) - - def shift_scale_latents(self, args, latents): - return latents - - def get_noise_pred_and_target( - self, - args, - accelerator, - noise_scheduler, - latents, - batch, - text_encoder_conds, - unet: flux_models.Flux, - network, - weight_dtype, - train_unet, - ): - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - - # get noisy model input and timesteps - noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps( - args, noise_scheduler, latents, noise, accelerator.device, weight_dtype - ) - - # pack latents and get img_ids - packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input) # b, c, h*2, w*2 -> b, h*w, c*4 - packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2 - img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device) - - # get guidance - # ensure guidance_scale in args is float - guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device) - - # ensure the hidden state will require grad - if args.gradient_checkpointing: - noisy_model_input.requires_grad_(True) - for t in text_encoder_conds: - if t is not None and t.dtype.is_floating_point: - t.requires_grad_(True) - img_ids.requires_grad_(True) - guidance_vec.requires_grad_(True) - - # Predict the noise residual - l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds - if not args.apply_t5_attn_mask: - t5_attn_mask = None - - def call_dit(img, img_ids, t5_out, txt_ids, l_pooled, timesteps, guidance_vec, t5_attn_mask): - # if not args.split_mode: - # normal forward - with accelerator.autocast(): - # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing) - model_pred = unet( - img=img, - img_ids=img_ids, - txt=t5_out, - txt_ids=txt_ids, - y=l_pooled, - timesteps=timesteps / 1000, - guidance=guidance_vec, - txt_attention_mask=t5_attn_mask, - ) - """ - else: - # split forward to reduce memory usage - assert network.train_blocks == "single", "train_blocks must be single for split mode" - with accelerator.autocast(): - # move flux lower to cpu, and then move flux upper to gpu - unet.to("cpu") - clean_memory_on_device(accelerator.device) - self.flux_upper.to(accelerator.device) - - # upper model does not require grad - with torch.no_grad(): - intermediate_img, intermediate_txt, vec, pe = self.flux_upper( - img=packed_noisy_model_input, - img_ids=img_ids, - txt=t5_out, - txt_ids=txt_ids, - y=l_pooled, - timesteps=timesteps / 1000, - guidance=guidance_vec, - txt_attention_mask=t5_attn_mask, - ) - - # move flux upper back to cpu, and then move flux lower to gpu - self.flux_upper.to("cpu") - clean_memory_on_device(accelerator.device) - unet.to(accelerator.device) - - # lower model requires grad - intermediate_img.requires_grad_(True) - intermediate_txt.requires_grad_(True) - vec.requires_grad_(True) - pe.requires_grad_(True) - model_pred = unet(img=intermediate_img, txt=intermediate_txt, vec=vec, pe=pe, txt_attention_mask=t5_attn_mask) - """ - - return model_pred - - model_pred = call_dit( - img=packed_noisy_model_input, - img_ids=img_ids, - t5_out=t5_out, - txt_ids=txt_ids, - l_pooled=l_pooled, - timesteps=timesteps, - guidance_vec=guidance_vec, - t5_attn_mask=t5_attn_mask, - ) - - # unpack latents - model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width) - - # apply model prediction type - model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas) - - # flow matching loss: this is different from SD3 - target = noise - latents - - # differential output preservation - if "custom_attributes" in batch: - diff_output_pr_indices = [] - for i, custom_attributes in enumerate(batch["custom_attributes"]): - if "diff_output_preservation" in custom_attributes and custom_attributes["diff_output_preservation"]: - diff_output_pr_indices.append(i) - - if len(diff_output_pr_indices) > 0: - network.set_multiplier(0.0) - with torch.no_grad(): - model_pred_prior = call_dit( - img=packed_noisy_model_input[diff_output_pr_indices], - img_ids=img_ids[diff_output_pr_indices], - t5_out=t5_out[diff_output_pr_indices], - txt_ids=txt_ids[diff_output_pr_indices], - l_pooled=l_pooled[diff_output_pr_indices], - timesteps=timesteps[diff_output_pr_indices], - guidance_vec=guidance_vec[diff_output_pr_indices] if guidance_vec is not None else None, - t5_attn_mask=t5_attn_mask[diff_output_pr_indices] if t5_attn_mask is not None else None, + "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( + ", ".join(ignored) ) - network.set_multiplier(1.0) # may be overwritten by "network_multipliers" in the next step - - model_pred_prior = flux_utils.unpack_latents(model_pred_prior, packed_latent_height, packed_latent_width) - model_pred_prior, _ = flux_train_utils.apply_model_prediction_type( - args, - model_pred_prior, - noisy_model_input[diff_output_pr_indices], - sigmas[diff_output_pr_indices] if sigmas is not None else None, ) - target[diff_output_pr_indices] = model_pred_prior.to(target.dtype) - - return model_pred, target, timesteps, None, weighting - - def post_process_loss(self, loss, args, timesteps, noise_scheduler): - return loss - - def get_sai_model_spec(self, args): - return train_util.get_sai_model_spec(None, args, False, True, False, flux="dev") - - def update_metadata(self, metadata, args): - metadata["ss_apply_t5_attn_mask"] = args.apply_t5_attn_mask - metadata["ss_weighting_scheme"] = args.weighting_scheme - metadata["ss_logit_mean"] = args.logit_mean - metadata["ss_logit_std"] = args.logit_std - metadata["ss_mode_scale"] = args.mode_scale - metadata["ss_guidance_scale"] = args.guidance_scale - metadata["ss_timestep_sampling"] = args.timestep_sampling - metadata["ss_sigmoid_scale"] = args.sigmoid_scale - metadata["ss_model_prediction_type"] = args.model_prediction_type - metadata["ss_discrete_flow_shift"] = args.discrete_flow_shift - - def is_text_encoder_not_needed_for_training(self, args): - return args.cache_text_encoder_outputs and not self.is_train_text_encoder(args) - - def prepare_text_encoder_grad_ckpt_workaround(self, index, text_encoder): - if index == 0: # CLIP-L - return super().prepare_text_encoder_grad_ckpt_workaround(index, text_encoder) - else: # T5XXL - text_encoder.encoder.embed_tokens.requires_grad_(True) - - def prepare_text_encoder_fp8(self, index, text_encoder, te_weight_dtype, weight_dtype): - if index == 0: # CLIP-L - logger.info(f"prepare CLIP-L for fp8: set to {te_weight_dtype}, set embeddings to {weight_dtype}") - text_encoder.to(te_weight_dtype) # fp8 - text_encoder.text_model.embeddings.to(dtype=weight_dtype) - else: # T5XXL - - def prepare_fp8(text_encoder, target_dtype): - def forward_hook(module): - def forward(hidden_states): - hidden_gelu = module.act(module.wi_0(hidden_states)) - hidden_linear = module.wi_1(hidden_states) - hidden_states = hidden_gelu * hidden_linear - hidden_states = module.dropout(hidden_states) - - hidden_states = module.wo(hidden_states) - return hidden_states - - return forward - - for module in text_encoder.modules(): - if module.__class__.__name__ in ["T5LayerNorm", "Embedding"]: - # print("set", module.__class__.__name__, "to", target_dtype) - module.to(target_dtype) - if module.__class__.__name__ in ["T5DenseGatedActDense"]: - # print("set", module.__class__.__name__, "hooks") - module.forward = forward_hook(module) - - if flux_utils.get_t5xxl_actual_dtype(text_encoder) == torch.float8_e4m3fn and text_encoder.dtype == weight_dtype: - logger.info(f"T5XXL already prepared for fp8") + else: + if use_dreambooth_method: + logger.info("Using DreamBooth method.") + user_config = { + "datasets": [ + { + "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( + args.train_data_dir, args.reg_data_dir + ) + } + ] + } else: - logger.info(f"prepare T5XXL for fp8: set to {te_weight_dtype}, set embeddings to {weight_dtype}, add hooks") - text_encoder.to(te_weight_dtype) # fp8 - prepare_fp8(text_encoder, weight_dtype) + logger.info("Training with captions.") + user_config = { + "datasets": [ + { + "subsets": [ + { + "image_dir": args.train_data_dir, + "metadata_file": args.in_json, + } + ] + } + ] + } - def prepare_unet_with_accelerator( - self, args: argparse.Namespace, accelerator: Accelerator, unet: torch.nn.Module - ) -> torch.nn.Module: - if not self.is_swapping_blocks: - return super().prepare_unet_with_accelerator(args, accelerator, unet) + blueprint = blueprint_generator.generate(user_config, args) + train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) + else: + train_dataset_group = train_util.load_arbitrary_dataset(args) + current_epoch = Value("i", 0) + current_step = Value("i", 0) + ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None + collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) + + train_dataset_group.verify_bucket_reso_steps(16) # TODO これでいいか確認 + + _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path) + if args.debug_dataset: + if args.cache_text_encoder_outputs: + strategy_base.TextEncoderOutputsCachingStrategy.set_strategy( + strategy_flux.FluxTextEncoderOutputsCachingStrategy( + args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, False + ) + ) + t5xxl_max_token_length = ( + args.t5xxl_max_token_length if args.t5xxl_max_token_length is not None else (256 if is_schnell else 512) + ) + strategy_base.TokenizeStrategy.set_strategy(strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)) + + train_dataset_group.set_current_strategies() + train_util.debug_dataset(train_dataset_group, True) + return + if len(train_dataset_group) == 0: + logger.error( + "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。" + ) + return + + if cache_latents: + assert ( + train_dataset_group.is_latent_cacheable() + ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" + + if args.cache_text_encoder_outputs: + assert ( + train_dataset_group.is_text_encoder_output_cacheable() + ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" + + # acceleratorを準備する + logger.info("prepare accelerator") + accelerator = train_util.prepare_accelerator(args) + + # mixed precisionに対応した型を用意しておき適宜castする + weight_dtype, save_dtype = train_util.prepare_dtype(args) + + # モデルを読み込む + + # load VAE for caching latents + ae = None + if cache_latents: + ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", args.disable_mmap_load_safetensors) + ae.to(accelerator.device, dtype=weight_dtype) + ae.requires_grad_(False) + ae.eval() + + train_dataset_group.new_cache_latents(ae, accelerator) + + ae.to("cpu") # if no sampling, vae can be deleted + clean_memory_on_device(accelerator.device) + + accelerator.wait_for_everyone() + + # prepare tokenize strategy + if args.t5xxl_max_token_length is None: + if is_schnell: + t5xxl_max_token_length = 256 + else: + t5xxl_max_token_length = 512 + else: + t5xxl_max_token_length = args.t5xxl_max_token_length + + flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length) + strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy) + + # load clip_l, t5xxl for caching text encoder outputs + clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", args.disable_mmap_load_safetensors) + t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu", args.disable_mmap_load_safetensors) + clip_l.eval() + t5xxl.eval() + clip_l.requires_grad_(False) + t5xxl.requires_grad_(False) + + text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask) + strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy) + + # cache text encoder outputs + sample_prompts_te_outputs = None + if args.cache_text_encoder_outputs: + # Text Encodes are eval and no grad here + clip_l.to(accelerator.device) + t5xxl.to(accelerator.device) + + text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy( + args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask + ) + strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy) + + with accelerator.autocast(): + train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator) + + # cache sample prompt's embeddings to free text encoder's memory + if args.sample_prompts is not None: + logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}") + + text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy() + + prompts = train_util.load_prompts(args.sample_prompts) + sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs + with accelerator.autocast(), torch.no_grad(): + for prompt_dict in prompts: + for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: + if p not in sample_prompts_te_outputs: + logger.info(f"cache Text Encoder outputs for prompt: {p}") + tokens_and_masks = flux_tokenize_strategy.tokenize(p) + sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens( + flux_tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask + ) + + accelerator.wait_for_everyone() + + # now we can delete Text Encoders to free memory + clip_l = None + t5xxl = None + clean_memory_on_device(accelerator.device) + + # load FLUX + _, flux = flux_utils.load_flow_model( + args.pretrained_model_name_or_path, weight_dtype, "cpu", args.disable_mmap_load_safetensors + ) + flux.requires_grad_(False) + + # load controlnet + controlnet = flux_utils.load_controlnet() + controlnet.requires_grad_(True) + + if args.gradient_checkpointing: + controlnet.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing) + + # block swap + + # backward compatibility + if args.blocks_to_swap is None: + blocks_to_swap = args.double_blocks_to_swap or 0 + if args.single_blocks_to_swap is not None: + blocks_to_swap += args.single_blocks_to_swap // 2 + if blocks_to_swap > 0: + logger.warning( + "double_blocks_to_swap and single_blocks_to_swap are deprecated. Use blocks_to_swap instead." + " / double_blocks_to_swapとsingle_blocks_to_swapは非推奨です。blocks_to_swapを使ってください。" + ) + logger.info( + f"double_blocks_to_swap={args.double_blocks_to_swap} and single_blocks_to_swap={args.single_blocks_to_swap} are converted to blocks_to_swap={blocks_to_swap}." + ) + args.blocks_to_swap = blocks_to_swap + del blocks_to_swap + + is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0 + if is_swapping_blocks: + # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes. + # This idea is based on 2kpr's great work. Thank you! + logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}") + flux.enable_block_swap(args.blocks_to_swap, accelerator.device) + controlnet.enable_block_swap(args.blocks_to_swap, accelerator.device) + + if not cache_latents: + # load VAE here if not cached + ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu") + ae.requires_grad_(False) + ae.eval() + ae.to(accelerator.device, dtype=weight_dtype) + + training_models = [] + params_to_optimize = [] + training_models.append(controlnet) + name_and_params = list(controlnet.named_parameters()) + # single param group for now + params_to_optimize.append({"params": [p for _, p in name_and_params], "lr": args.learning_rate}) + param_names = [[n for n, _ in name_and_params]] + + # calculate number of trainable parameters + n_params = 0 + for group in params_to_optimize: + for p in group["params"]: + n_params += p.numel() + + accelerator.print(f"number of trainable parameters: {n_params}") + + # 学習に必要なクラスを準備する + accelerator.print("prepare optimizer, data loader etc.") + + if args.blockwise_fused_optimizers: + # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html + # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters. + # This balances memory usage and management complexity. + + # split params into groups. currently different learning rates are not supported + grouped_params = [] + param_group = {} + for group in params_to_optimize: + named_parameters = list(controlnet.named_parameters()) + assert len(named_parameters) == len(group["params"]), "number of parameters does not match" + for p, np in zip(group["params"], named_parameters): + # determine target layer and block index for each parameter + block_type = "other" # double, single or other + if np[0].startswith("double_blocks"): + block_index = int(np[0].split(".")[1]) + block_type = "double" + elif np[0].startswith("single_blocks"): + block_index = int(np[0].split(".")[1]) + block_type = "single" + else: + block_index = -1 + + param_group_key = (block_type, block_index) + if param_group_key not in param_group: + param_group[param_group_key] = [] + param_group[param_group_key].append(p) + + block_types_and_indices = [] + for param_group_key, param_group in param_group.items(): + block_types_and_indices.append(param_group_key) + grouped_params.append({"params": param_group, "lr": args.learning_rate}) + + num_params = 0 + for p in param_group: + num_params += p.numel() + accelerator.print(f"block {param_group_key}: {num_params} parameters") + + # prepare optimizers for each group + optimizers = [] + for group in grouped_params: + _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group]) + optimizers.append(optimizer) + optimizer = optimizers[0] # avoid error in the following code + + logger.info(f"using {len(optimizers)} optimizers for blockwise fused optimizers") + + if train_util.is_schedulefree_optimizer(optimizers[0], args): + raise ValueError("Schedule-free optimizer is not supported with blockwise fused optimizers") + optimizer_train_fn = lambda: None # dummy function + optimizer_eval_fn = lambda: None # dummy function + else: + _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) + optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args) + + # prepare dataloader + # strategies are set here because they cannot be referenced in another process. Copy them with the dataset + # some strategies can be None + train_dataset_group.set_current_strategies() + + # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意 + n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers + train_dataloader = torch.utils.data.DataLoader( + train_dataset_group, + batch_size=1, + shuffle=True, + collate_fn=collator, + num_workers=n_workers, + persistent_workers=args.persistent_data_loader_workers, + ) + + # 学習ステップ数を計算する + if args.max_train_epochs is not None: + args.max_train_steps = args.max_train_epochs * math.ceil( + len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps + ) + accelerator.print( + f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}" + ) + + # データセット側にも学習ステップを送信 + train_dataset_group.set_max_train_steps(args.max_train_steps) + + # lr schedulerを用意する + if args.blockwise_fused_optimizers: + # prepare lr schedulers for each optimizer + lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers] + lr_scheduler = lr_schedulers[0] # avoid error in the following code + else: + lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) + + # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする + if args.full_fp16: + assert ( + args.mixed_precision == "fp16" + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + accelerator.print("enable full fp16 training.") + flux.to(weight_dtype) + controlnet.to(weight_dtype) + if clip_l is not None: + clip_l.to(weight_dtype) + t5xxl.to(weight_dtype) # TODO check works with fp16 or not + elif args.full_bf16: + assert ( + args.mixed_precision == "bf16" + ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + accelerator.print("enable full bf16 training.") + flux.to(weight_dtype) + controlnet.to(weight_dtype) + if clip_l is not None: + clip_l.to(weight_dtype) + t5xxl.to(weight_dtype) + + # if we don't cache text encoder outputs, move them to device + if not args.cache_text_encoder_outputs: + clip_l.to(accelerator.device) + t5xxl.to(accelerator.device) + + clean_memory_on_device(accelerator.device) + + if args.deepspeed: + ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=controlnet) + # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler + ) + training_models = [ds_model] + + else: + # accelerator does some magic # if we doesn't swap blocks, we can move the model to device - flux: flux_models.Flux = unet - flux = accelerator.prepare(flux, device_placement=[not self.is_swapping_blocks]) - accelerator.unwrap_model(flux).move_to_device_except_swap_blocks(accelerator.device) # reduce peak memory usage - accelerator.unwrap_model(flux).prepare_block_swap_before_forward() + controlnet = accelerator.prepare(controlnet, device_placement=[not is_swapping_blocks]) + if is_swapping_blocks: + accelerator.unwrap_model(controlnet).move_to_device_except_swap_blocks(accelerator.device) # reduce peak memory usage + optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) - return flux + # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする + if args.full_fp16: + # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. + # -> But we think it's ok to patch accelerator even if deepspeed is enabled. + train_util.patch_accelerator_for_fp16_training(accelerator) + + # resumeする + train_util.resume_from_local_or_hf_if_specified(accelerator, args) + + if args.fused_backward_pass: + # use fused optimizer for backward pass: other optimizers will be supported in the future + import library.adafactor_fused + + library.adafactor_fused.patch_adafactor_fused(optimizer) + + for param_group, param_name_group in zip(optimizer.param_groups, param_names): + for parameter, param_name in zip(param_group["params"], param_name_group): + if parameter.requires_grad: + + def create_grad_hook(p_name, p_group): + def grad_hook(tensor: torch.Tensor): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + accelerator.clip_grad_norm_(tensor, args.max_grad_norm) + optimizer.step_param(tensor, p_group) + tensor.grad = None + + return grad_hook + + parameter.register_post_accumulate_grad_hook(create_grad_hook(param_name, param_group)) + + elif args.blockwise_fused_optimizers: + # prepare for additional optimizers and lr schedulers + for i in range(1, len(optimizers)): + optimizers[i] = accelerator.prepare(optimizers[i]) + lr_schedulers[i] = accelerator.prepare(lr_schedulers[i]) + + # counters are used to determine when to step the optimizer + global optimizer_hooked_count + global num_parameters_per_group + global parameter_optimizer_map + + optimizer_hooked_count = {} + num_parameters_per_group = [0] * len(optimizers) + parameter_optimizer_map = {} + + for opt_idx, optimizer in enumerate(optimizers): + for param_group in optimizer.param_groups: + for parameter in param_group["params"]: + if parameter.requires_grad: + + def grad_hook(parameter: torch.Tensor): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + accelerator.clip_grad_norm_(parameter, args.max_grad_norm) + + i = parameter_optimizer_map[parameter] + optimizer_hooked_count[i] += 1 + if optimizer_hooked_count[i] == num_parameters_per_group[i]: + optimizers[i].step() + optimizers[i].zero_grad(set_to_none=True) + + parameter.register_post_accumulate_grad_hook(grad_hook) + parameter_optimizer_map[parameter] = opt_idx + num_parameters_per_group[opt_idx] += 1 + + # epoch数を計算する + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0): + args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1 + + # 学習する + # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + accelerator.print("running training / 学習開始") + accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_images}") + accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") + accelerator.print(f" num epochs / epoch数: {num_train_epochs}") + accelerator.print( + f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" + ) + # accelerator.print( + # f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}" + # ) + accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") + accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}") + + progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps") + global_step = 0 + + noise_scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift) + noise_scheduler_copy = copy.deepcopy(noise_scheduler) + + if accelerator.is_main_process: + init_kwargs = {} + if args.wandb_run_name: + init_kwargs["wandb"] = {"name": args.wandb_run_name} + if args.log_tracker_config is not None: + init_kwargs = toml.load(args.log_tracker_config) + accelerator.init_trackers( + "finetuning" if args.log_tracker_name is None else args.log_tracker_name, + config=train_util.get_sanitized_config_or_none(args), + init_kwargs=init_kwargs, + ) + + if is_swapping_blocks: + accelerator.unwrap_model(controlnet).prepare_block_swap_before_forward() + + # For --sample_at_first + optimizer_eval_fn() + flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs) + optimizer_train_fn() + if len(accelerator.trackers) > 0: + # log empty object to commit the sample images to wandb + accelerator.log({}, step=0) + + loss_recorder = train_util.LossRecorder() + epoch = 0 # avoid error when max_train_steps is 0 + for epoch in range(num_train_epochs): + accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") + current_epoch.value = epoch + 1 + + for m in training_models: + m.train() + + for step, batch in enumerate(train_dataloader): + current_step.value = global_step + + if args.blockwise_fused_optimizers: + optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step + + with accelerator.accumulate(*training_models): + if "latents" in batch and batch["latents"] is not None: + latents = batch["latents"].to(accelerator.device, dtype=weight_dtype) + else: + with torch.no_grad(): + # encode images to latents. images are [-1, 1] + latents = ae.encode(batch["images"].to(ae.dtype)).to(accelerator.device, dtype=weight_dtype) + + # NaNが含まれていれば警告を表示し0に置き換える + if torch.any(torch.isnan(latents)): + accelerator.print("NaN found in latents, replacing with zeros") + latents = torch.nan_to_num(latents, 0, out=latents) + + text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None) + if text_encoder_outputs_list is not None: + text_encoder_conds = text_encoder_outputs_list + else: + # not cached or training, so get from text encoders + tokens_and_masks = batch["input_ids_list"] + with torch.no_grad(): + input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]] + text_encoder_conds = text_encoding_strategy.encode_tokens( + flux_tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask + ) + if args.full_fp16: + text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds] + + # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + + # get noisy model input and timesteps + noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps( + args, noise_scheduler_copy, latents, noise, accelerator.device, weight_dtype + ) + + # pack latents and get img_ids + packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input) # b, c, h*2, w*2 -> b, h*w, c*4 + packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2 + img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device) + + # get guidance: ensure args.guidance_scale is float + guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device) + + # call model + l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds + if not args.apply_t5_attn_mask: + t5_attn_mask = None + + with accelerator.autocast(): + block_samples, block_single_samples = controlnet( + img=packed_noisy_model_input, + img_ids=img_ids, + controlnet_cond=batch["control_image"].to(accelerator.device), + txt=t5_out, + txt_ids=txt_ids, + y=l_pooled, + timesteps=timesteps / 1000, + guidance=guidance_vec, + txt_attention_mask=t5_attn_mask, + ) + # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing) + model_pred = flux( + img=packed_noisy_model_input, + img_ids=img_ids, + txt=t5_out, + txt_ids=txt_ids, + y=l_pooled, + block_controlnet_hidden_states=block_samples, + block_controlnet_single_hidden_states=block_single_samples, + timesteps=timesteps / 1000, + guidance=guidance_vec, + txt_attention_mask=t5_attn_mask, + ) + + # unpack latents + model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width) + + # apply model prediction type + model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas) + + # flow matching loss: this is different from SD3 + target = noise - latents + + # calculate loss + loss = train_util.conditional_loss( + model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None + ) + if weighting is not None: + loss = loss * weighting + if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): + loss = apply_masked_loss(loss, batch) + loss = loss.mean([1, 2, 3]) + + loss_weights = batch["loss_weights"] # 各sampleごとのweight + loss = loss * loss_weights + loss = loss.mean() + + # backward + accelerator.backward(loss) + + if not (args.fused_backward_pass or args.blockwise_fused_optimizers): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + params_to_clip = [] + for m in training_models: + params_to_clip.extend(m.parameters()) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + else: + # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook + lr_scheduler.step() + if args.blockwise_fused_optimizers: + for i in range(1, len(optimizers)): + lr_schedulers[i].step() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + optimizer_eval_fn() + flux_train_utils.sample_images( + accelerator, args, None, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs + ) + + # 指定ステップごとにモデルを保存 + if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + flux_train_utils.save_flux_model_on_epoch_end_or_stepwise( + args, + False, + accelerator, + save_dtype, + epoch, + num_train_epochs, + global_step, + accelerator.unwrap_model(flux), + ) + optimizer_train_fn() + + current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず + if len(accelerator.trackers) > 0: + logs = {"loss": current_loss} + train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True) + + accelerator.log(logs, step=global_step) + + loss_recorder.add(epoch=epoch, step=step, loss=current_loss) + avr_loss: float = loss_recorder.moving_average + logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + if len(accelerator.trackers) > 0: + logs = {"loss/epoch": loss_recorder.moving_average} + accelerator.log(logs, step=epoch + 1) + + accelerator.wait_for_everyone() + + optimizer_eval_fn() + if args.save_every_n_epochs is not None: + if accelerator.is_main_process: + flux_train_utils.save_flux_model_on_epoch_end_or_stepwise( + args, + True, + accelerator, + save_dtype, + epoch, + num_train_epochs, + global_step, + accelerator.unwrap_model(flux), + ) + + flux_train_utils.sample_images( + accelerator, args, epoch + 1, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs + ) + optimizer_train_fn() + + is_main_process = accelerator.is_main_process + # if is_main_process: + controlnet = accelerator.unwrap_model(controlnet) + + accelerator.end_training() + optimizer_eval_fn() + + if args.save_state or args.save_state_on_train_end: + train_util.save_state_on_train_end(args, accelerator) + + del accelerator # この後メモリを使うのでこれは消す + + if is_main_process: + flux_train_utils.save_flux_model_on_train_end(args, save_dtype, epoch, global_step, flux) + logger.info("model saved.") def setup_parser() -> argparse.ArgumentParser: - parser = train_network.setup_parser() + parser = argparse.ArgumentParser() + + add_logging_arguments(parser) + train_util.add_sd_models_arguments(parser) # TODO split this + train_util.add_dataset_arguments(parser, True, True, True) + train_util.add_training_arguments(parser, False) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) + train_util.add_sd_saving_arguments(parser) + train_util.add_optimizer_arguments(parser) + config_util.add_config_arguments(parser) + add_custom_train_arguments(parser) # TODO remove this from here train_util.add_dit_training_arguments(parser) flux_train_utils.add_flux_train_arguments(parser) parser.add_argument( - "--split_mode", + "--mem_eff_save", action="store_true", - # help="[EXPERIMENTAL] use split mode for Flux model, network arg `train_blocks=single` is required" - # + "/[実験的] Fluxモデルの分割モードを使用する。ネットワーク引数`train_blocks=single`が必要", - help="[Deprecated] This option is deprecated. Please use `--blocks_to_swap` instead." - " / このオプションは非推奨です。代わりに`--blocks_to_swap`を使用してください。", + help="[EXPERIMENTAL] use memory efficient custom model saving method / メモリ効率の良い独自のモデル保存方法を使う", + ) + + parser.add_argument( + "--fused_optimizer_groups", + type=int, + default=None, + help="**this option is not working** will be removed in the future / このオプションは動作しません。将来削除されます", + ) + parser.add_argument( + "--blockwise_fused_optimizers", + action="store_true", + help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする", + ) + parser.add_argument( + "--skip_latents_validity_check", + action="store_true", + help="[Deprecated] use 'skip_cache_check' instead / 代わりに 'skip_cache_check' を使用してください", + ) + parser.add_argument( + "--double_blocks_to_swap", + type=int, + default=None, + help="[Deprecated] use 'blocks_to_swap' instead / 代わりに 'blocks_to_swap' を使用してください", + ) + parser.add_argument( + "--single_blocks_to_swap", + type=int, + default=None, + help="[Deprecated] use 'blocks_to_swap' instead / 代わりに 'blocks_to_swap' を使用してください", + ) + parser.add_argument( + "--cpu_offload_checkpointing", + action="store_true", + help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing / チェックポイント時にテンソルをCPUにオフロードする", ) return parser @@ -569,5 +866,4 @@ if __name__ == "__main__": train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) - trainer = FluxNetworkTrainer() - trainer.train(args) + train(args) diff --git a/flux_train_network.py b/flux_train_network.py index 0feb9b01..6668012e 100644 --- a/flux_train_network.py +++ b/flux_train_network.py @@ -125,9 +125,6 @@ class FluxNetworkTrainer(train_network.NetworkTrainer): ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors) - controlnet = flux_utils.load_controlnet() - controlnet.train() - return flux_utils.MODEL_VERSION_FLUX_V1, [clip_l, t5xxl], ae, model, controlnet def get_tokenize_strategy(self, args): diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py index d90644a2..cc3bcb0e 100644 --- a/library/flux_train_utils.py +++ b/library/flux_train_utils.py @@ -40,6 +40,7 @@ def sample_images( text_encoders, sample_prompts_te_outputs, prompt_replacement=None, + controlnet=None ): if steps == 0: if not args.sample_at_first: @@ -67,6 +68,8 @@ def sample_images( flux = accelerator.unwrap_model(flux) if text_encoders is not None: text_encoders = [accelerator.unwrap_model(te) for te in text_encoders] + if controlnet is not None: + controlnet = accelerator.unwrap_model(controlnet) # print([(te.parameters().__next__().device if te is not None else None) for te in text_encoders]) prompts = train_util.load_prompts(args.sample_prompts) @@ -98,6 +101,7 @@ def sample_images( steps, sample_prompts_te_outputs, prompt_replacement, + controlnet ) else: # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) @@ -121,6 +125,7 @@ def sample_images( steps, sample_prompts_te_outputs, prompt_replacement, + controlnet ) torch.set_rng_state(rng_state) @@ -142,6 +147,7 @@ def sample_image_inference( steps, sample_prompts_te_outputs, prompt_replacement, + controlnet ): assert isinstance(prompt_dict, dict) # negative_prompt = prompt_dict.get("negative_prompt") @@ -150,7 +156,7 @@ def sample_image_inference( height = prompt_dict.get("height", 512) scale = prompt_dict.get("scale", 3.5) seed = prompt_dict.get("seed") - # controlnet_image = prompt_dict.get("controlnet_image") + controlnet_image = prompt_dict.get("controlnet_image") prompt: str = prompt_dict.get("prompt", "") # sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) @@ -169,6 +175,9 @@ def sample_image_inference( # if negative_prompt is None: # negative_prompt = "" + if controlnet_image is not None: + controlnet_image = Image.open(controlnet_image).convert("RGB") + controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS) height = max(64, height - height % 16) # round to divisible by 16 width = max(64, width - width % 16) # round to divisible by 16 @@ -224,7 +233,7 @@ def sample_image_inference( t5_attn_mask = t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask else None with accelerator.autocast(), torch.no_grad(): - x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask) + x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask, controlnet=controlnet, controlnet_img=controlnet_image) x = x.float() x = flux_utils.unpack_latents(x, packed_latent_height, packed_latent_width) @@ -301,18 +310,37 @@ def denoise( timesteps: list[float], guidance: float = 4.0, t5_attn_mask: Optional[torch.Tensor] = None, + controlnet: Optional[flux_models.ControlNetFlux] = None, + controlnet_img: Optional[torch.Tensor] = None, ): # this is ignored for schnell guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]): t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device) model.prepare_block_swap_before_forward() + if controlnet is not None: + block_samples, block_single_samples = controlnet( + img=img, + img_ids=img_ids, + controlnet_cond=controlnet_img, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t_vec, + guidance=guidance_vec, + txt_attention_mask=t5_attn_mask, + ) + else: + block_samples = None + block_single_samples = None pred = model( img=img, img_ids=img_ids, txt=txt, txt_ids=txt_ids, y=vec, + block_controlnet_hidden_states=block_samples, + block_controlnet_single_hidden_states=block_single_samples, timesteps=t_vec, guidance=guidance_vec, txt_attention_mask=t5_attn_mask, diff --git a/library/flux_utils.py b/library/flux_utils.py index 678efbc8..7b538d13 100644 --- a/library/flux_utils.py +++ b/library/flux_utils.py @@ -153,11 +153,14 @@ def load_ae( return ae -def load_controlnet(name, device, transformer=None): - with torch.device(device): +def load_controlnet(): + # TODO + is_schnell = False + name = MODEL_NAME_DEV if not is_schnell else MODEL_NAME_SCHNELL + with torch.device("meta"): controlnet = flux_models.ControlNetFlux(flux_models.configs[name].params) - if transformer is not None: - controlnet.load_state_dict(transformer.state_dict(), strict=False) + # if transformer is not None: + # controlnet.load_state_dict(transformer.state_dict(), strict=False) return controlnet