# coding=utf-8 import argparse import copy import math import random from typing import Any, Optional import torch from accelerate import Accelerator 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_asylora from library.utils import setup_logging setup_logging() import logging import re logger = logging.getLogger(__name__) class FluxNetworkTrainer(train_network_asylora.NetworkTrainer): def __init__(self): super().__init__() self.sample_prompts_te_outputs = None self.is_schnell: Optional[bool] = None self.is_swapping_blocks: bool = False 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 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" ) args.cache_text_encoder_outputs = True 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" # 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.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" # deprecated split_mode option if args.split_mode: if args.blocks_to_swap is not None: 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 # 获取数据集分类编号 文本 # lora_category = batch["captions"][0].split(",")[0][3:] # assert lora_category.isdigit(), f"lora_category 不是整数，值为: {lora_category}, {batch['captions'][0]}" # lora_category = int(lora_category) prompt_cur = batch["captions"][0] match = re.search(r'--lora_up_cur (\d+)', prompt_cur) assert match, "Pattern '--lora_up_cur' not found" lora_category = int(match.group(1)) for lora in network.unet_loras: lora.set_lora_up_cur(lora_category-1) 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) unet.prepare_block_swap_before_forward() 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, ) 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: 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) 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) # 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() return flux def setup_parser() -> argparse.ArgumentParser: parser = train_network_asylora.setup_parser() train_util.add_dit_training_arguments(parser) flux_train_utils.add_flux_train_arguments(parser) parser.add_argument( "--split_mode", 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。", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = FluxNetworkTrainer() trainer.train(args)