# Anima LoRA training script import argparse from typing import Any, Optional, Union import torch import torch.nn as nn from accelerate import Accelerator from library.device_utils import init_ipex, clean_memory_on_device init_ipex() from library import ( anima_models, anima_train_utils, anima_utils, flux_train_utils, qwen_image_autoencoder_kl, sd3_train_utils, strategy_anima, strategy_base, train_util, ) import train_network from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) class AnimaNetworkTrainer(train_network.NetworkTrainer): def __init__(self): super().__init__() self.sample_prompts_te_outputs = None def assert_extra_args( self, args, train_dataset_group: Union[train_util.DatasetGroup, train_util.MinimalDataset], val_dataset_group: Optional[train_util.DatasetGroup], ): if args.fp8_base or args.fp8_base_unet: logger.warning("fp8_base and fp8_base_unet are not supported. / fp8_baseとfp8_base_unetはサポートされていません。") args.fp8_base = False args.fp8_base_unet = False args.fp8_scaled = False # Anima DiT does not support fp8_scaled 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( cache_supports_dropout=True ), "when caching Text Encoder output, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used" assert ( args.network_train_unet_only or not args.cache_text_encoder_outputs ), "network for Text Encoder cannot be trained with caching Text Encoder outputs / Text Encoderの出力をキャッシュしながらText Encoderのネットワークを学習することはできません" 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" if args.unsloth_offload_checkpointing: if not args.gradient_checkpointing: logger.warning("unsloth_offload_checkpointing is enabled, so gradient_checkpointing is also enabled") args.gradient_checkpointing = True assert ( not args.cpu_offload_checkpointing ), "Cannot use both --unsloth_offload_checkpointing and --cpu_offload_checkpointing" assert ( args.blocks_to_swap is None or args.blocks_to_swap == 0 ), "blocks_to_swap is not supported with unsloth_offload_checkpointing" train_dataset_group.verify_bucket_reso_steps(16) # WanVAE spatial downscale = 8 and patch size = 2 if val_dataset_group is not None: val_dataset_group.verify_bucket_reso_steps(16) def load_target_model(self, args, weight_dtype, accelerator): self.is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0 # Load Qwen3 text encoder (tokenizers already loaded in get_tokenize_strategy) logger.info("Loading Qwen3 text encoder...") qwen3_text_encoder, _ = anima_utils.load_qwen3_text_encoder(args.qwen3, dtype=weight_dtype, device="cpu") qwen3_text_encoder.eval() # Load VAE logger.info("Loading Anima VAE...") vae = qwen_image_autoencoder_kl.load_vae( args.vae, device="cpu", disable_mmap=True, spatial_chunk_size=args.vae_chunk_size, disable_cache=args.vae_disable_cache ) vae.to(weight_dtype) vae.eval() # Return format: (model_type, text_encoders, vae, unet) return "anima", [qwen3_text_encoder], vae, None # unet loaded lazily def load_unet_lazily(self, args, weight_dtype, accelerator, text_encoders) -> tuple[nn.Module, list[nn.Module]]: loading_dtype = None if args.fp8_scaled else weight_dtype loading_device = "cpu" if self.is_swapping_blocks else accelerator.device attn_mode = "torch" if args.xformers: attn_mode = "xformers" if args.attn_mode is not None: attn_mode = args.attn_mode # Load DiT logger.info(f"Loading Anima DiT model with attn_mode={attn_mode}, split_attn: {args.split_attn}...") model = anima_utils.load_anima_model( accelerator.device, args.pretrained_model_name_or_path, attn_mode, args.split_attn, loading_device, loading_dtype, args.fp8_scaled, ) # Store unsloth preference so that when the base NetworkTrainer calls # dit.enable_gradient_checkpointing(cpu_offload=...), we can override to use unsloth. # The base trainer only passes cpu_offload, so we store the flag on the model. self._use_unsloth_offload_checkpointing = args.unsloth_offload_checkpointing # Block swap self.is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0 if self.is_swapping_blocks: logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}") model.enable_block_swap(args.blocks_to_swap, accelerator.device) return model, text_encoders def get_tokenize_strategy(self, args): # Load tokenizers from paths (called before load_target_model, so self.qwen3_tokenizer isn't set yet) tokenize_strategy = strategy_anima.AnimaTokenizeStrategy( qwen3_path=args.qwen3, t5_tokenizer_path=args.t5_tokenizer_path, qwen3_max_length=args.qwen3_max_token_length, t5_max_length=args.t5_max_token_length, ) return tokenize_strategy def get_tokenizers(self, tokenize_strategy: strategy_anima.AnimaTokenizeStrategy): return [tokenize_strategy.qwen3_tokenizer] def get_latents_caching_strategy(self, args): return strategy_anima.AnimaLatentsCachingStrategy(args.cache_latents_to_disk, args.vae_batch_size, args.skip_cache_check) def get_text_encoding_strategy(self, args): return strategy_anima.AnimaTextEncodingStrategy() def post_process_network(self, args, accelerator, network, text_encoders, unet): pass def get_models_for_text_encoding(self, args, accelerator, text_encoders): if args.cache_text_encoder_outputs: return None # no text encoders needed for encoding return text_encoders def get_text_encoder_outputs_caching_strategy(self, args): if args.cache_text_encoder_outputs: return strategy_anima.AnimaTextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, False ) 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: # We cannot move DiT to CPU because of block swap, so only move VAE logger.info("move vae to cpu to save memory") org_vae_device = vae.device vae.to("cpu") clean_memory_on_device(accelerator.device) logger.info("move text encoder to gpu") text_encoders[0].to(accelerator.device) 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 prompts: {args.sample_prompts}") tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() text_encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() prompts = train_util.load_prompts(args.sample_prompts) sample_prompts_te_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 TE outputs for: {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 ) self.sample_prompts_te_outputs = sample_prompts_te_outputs accelerator.wait_for_everyone() # move text encoder back to cpu logger.info("move text encoder back to cpu") text_encoders[0].to("cpu") if not args.lowram: logger.info("move vae back to original device") vae.to(org_vae_device) clean_memory_on_device(accelerator.device) else: # move text encoder to device for encoding during training/validation text_encoders[0].to(accelerator.device) def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet): text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] # compatibility te = self.get_models_for_text_encoding(args, accelerator, text_encoders) qwen3_te = te[0] if te is not None else None text_encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy() tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy() anima_train_utils.sample_images( accelerator, args, epoch, global_step, unet, vae, qwen3_te, tokenize_strategy, text_encoding_strategy, self.sample_prompts_te_outputs, ) 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) return noise_scheduler def encode_images_to_latents(self, args, vae, images): vae: qwen_image_autoencoder_kl.AutoencoderKLQwenImage return vae.encode_pixels_to_latents(images) # Keep 4D for input/output def shift_scale_latents(self, args, latents): # Latents already normalized by vae.encode with scale return latents def get_noise_pred_and_target( self, args, accelerator, noise_scheduler, latents, batch, text_encoder_conds, unet, network, weight_dtype, train_unet, is_train=True, ): anima: anima_models.Anima = unet # Sample noise if latents.ndim == 5: # Fallback for 5D latents (old cache) latents = latents.squeeze(2) # [B, C, 1, H, W] -> [B, C, H, W] noise = torch.randn_like(latents) # 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 ) timesteps = timesteps / 1000.0 # scale to [0, 1] range. timesteps is float32 # Gradient checkpointing support 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) # Unpack text encoder conditions prompt_embeds, attn_mask, t5_input_ids, t5_attn_mask = text_encoder_conds[ :4 ] # ignore caption_dropout_rate which is not needed for training step # Move to device prompt_embeds = prompt_embeds.to(accelerator.device, dtype=weight_dtype) attn_mask = attn_mask.to(accelerator.device) t5_input_ids = t5_input_ids.to(accelerator.device, dtype=torch.long) t5_attn_mask = t5_attn_mask.to(accelerator.device) # Create padding mask bs = latents.shape[0] h_latent = latents.shape[-2] w_latent = latents.shape[-1] padding_mask = torch.zeros(bs, 1, h_latent, w_latent, dtype=weight_dtype, device=accelerator.device) # Call model noisy_model_input = noisy_model_input.unsqueeze(2) # 4D to 5D, [B, C, H, W] -> [B, C, 1, H, W] with torch.set_grad_enabled(is_train), accelerator.autocast(): model_pred = anima( noisy_model_input, timesteps, prompt_embeds, padding_mask=padding_mask, target_input_ids=t5_input_ids, target_attention_mask=t5_attn_mask, source_attention_mask=attn_mask, ) model_pred = model_pred.squeeze(2) # 5D to 4D, [B, C, 1, H, W] -> [B, C, H, W] # Rectified flow target: noise - latents target = noise - latents # Loss weighting weighting = anima_train_utils.compute_loss_weighting_for_anima(weighting_scheme=args.weighting_scheme, sigmas=sigmas) return model_pred, target, timesteps, weighting def process_batch( self, batch, text_encoders, unet, network, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy, tokenize_strategy, is_train=True, train_text_encoder=True, train_unet=True, ) -> torch.Tensor: """Override base process_batch for caption dropout with cached text encoder outputs.""" # Text encoder conditions text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None) anima_text_encoding_strategy: strategy_anima.AnimaTextEncodingStrategy = text_encoding_strategy if text_encoder_outputs_list is not None: caption_dropout_rates = text_encoder_outputs_list[-1] text_encoder_outputs_list = text_encoder_outputs_list[:-1] # Apply caption dropout to cached outputs text_encoder_outputs_list = anima_text_encoding_strategy.drop_cached_text_encoder_outputs( *text_encoder_outputs_list, caption_dropout_rates=caption_dropout_rates ) # Add the caption dropout rates back to the list for validation dataset (which is re-used batch items) batch["text_encoder_outputs_list"] = text_encoder_outputs_list + [caption_dropout_rates] return super().process_batch( batch, text_encoders, unet, network, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy, tokenize_strategy, is_train, train_text_encoder, train_unet, ) 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_dataclass(None, args, False, True, False, anima="preview").to_metadata_dict() def update_metadata(self, metadata, args): 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_timestep_sampling"] = args.timestep_sampling metadata["ss_sigmoid_scale"] = args.sigmoid_scale 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): # Set first parameter's requires_grad to True to workaround Accelerate gradient checkpointing bug first_param = next(text_encoder.parameters()) first_param.requires_grad_(True) def prepare_unet_with_accelerator( self, args: argparse.Namespace, accelerator: Accelerator, unet: torch.nn.Module ) -> torch.nn.Module: # The base NetworkTrainer only calls enable_gradient_checkpointing(cpu_offload=True/False), # so we re-apply with unsloth_offload if needed (after base has already enabled it). if self._use_unsloth_offload_checkpointing and args.gradient_checkpointing: unet.enable_gradient_checkpointing(unsloth_offload=True) if not self.is_swapping_blocks: return super().prepare_unet_with_accelerator(args, accelerator, unet) model = unet model = accelerator.prepare(model, device_placement=[not self.is_swapping_blocks]) accelerator.unwrap_model(model).move_to_device_except_swap_blocks(accelerator.device) accelerator.unwrap_model(model).prepare_block_swap_before_forward() return model def on_validation_step_end(self, args, accelerator, network, text_encoders, unet, batch, weight_dtype): if self.is_swapping_blocks: # prepare for next forward: because backward pass is not called, we need to prepare it here accelerator.unwrap_model(unet).prepare_block_swap_before_forward() def setup_parser() -> argparse.ArgumentParser: parser = train_network.setup_parser() train_util.add_dit_training_arguments(parser) anima_train_utils.add_anima_training_arguments(parser) # parser.add_argument("--fp8_scaled", action="store_true", help="Use scaled fp8 for DiT / DiTにスケーリングされたfp8を使う") parser.add_argument( "--unsloth_offload_checkpointing", action="store_true", help="offload activations to CPU RAM using async non-blocking transfers (faster than --cpu_offload_checkpointing). " "Cannot be used with --cpu_offload_checkpointing or --blocks_to_swap.", ) 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) if args.attn_mode == "sdpa": args.attn_mode = "torch" # backward compatibility trainer = AnimaNetworkTrainer() trainer.train(args)