sdxl_train_network.py

import argparse
import torch
from library import sdxl_model_util, sdxl_train_util, train_util
import train_network


class SdxlNetworkTrainer(train_network.NetworkTrainer):
    def __init__(self):
        super().__init__()
        self.vae_scale_factor = sdxl_model_util.VAE_SCALE_FACTOR

    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.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 (
            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"

    def load_target_model(self, args, weight_dtype, accelerator):
        (
            load_stable_diffusion_format,
            text_encoder1,
            text_encoder2,
            vae,
            unet,
            logit_scale,
            ckpt_info,
        ) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, weight_dtype)

        self.load_stable_diffusion_format = load_stable_diffusion_format
        self.logit_scale = logit_scale
        self.ckpt_info = ckpt_info

        return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, [text_encoder1, text_encoder2], vae, unet

    def load_tokenizer(self, args):
        tokenizer = sdxl_train_util.load_tokenizers(args)
        return tokenizer

    def is_text_encoder_outputs_cached(self, args):
        return args.cache_text_encoder_outputs

    def cache_text_encoder_outputs_if_needed(
        self, args, accelerator, unet, vae, tokenizers, text_encoders, dataset, weight_dtype
    ):
        if args.cache_text_encoder_outputs:
            if not args.lowram:
                # メモリ消費を減らす
                print("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")
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()

            text_encoder1_cache, text_encoder2_cache = sdxl_train_util.cache_text_encoder_outputs(
                args, accelerator, tokenizers, text_encoders, dataset, weight_dtype
            )
            accelerator.wait_for_everyone()
            text_encoders[0].to("cpu", dtype=torch.float32) # Text Encoder doesn't work with fp16 on CPU
            text_encoders[1].to("cpu", dtype=torch.float32)
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

            self.text_encoder1_cache = text_encoder1_cache
            self.text_encoder2_cache = text_encoder2_cache

            if not args.lowram:
                print("move vae and unet back to original device")
                vae.to(org_vae_device)
                unet.to(org_unet_device)
        else:
            self.text_encoder1_cache = None
            self.text_encoder2_cache = None

            # 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)
            text_encoders[1].to(accelerator.device)

    def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
        input_ids1 = batch["input_ids"]
        input_ids2 = batch["input_ids2"]
        if not args.cache_text_encoder_outputs:
            with torch.enable_grad():
                # Get the text embedding for conditioning
                # TODO support weighted captions
                # if args.weighted_captions:
                #     encoder_hidden_states = get_weighted_text_embeddings(
                #         tokenizer,
                #         text_encoder,
                #         batch["captions"],
                #         accelerator.device,
                #         args.max_token_length // 75 if args.max_token_length else 1,
                #         clip_skip=args.clip_skip,
                #     )
                # else:
                input_ids1 = input_ids1.to(accelerator.device)
                input_ids2 = input_ids2.to(accelerator.device)
                encoder_hidden_states1, encoder_hidden_states2, pool2 = sdxl_train_util.get_hidden_states(
                    args,
                    input_ids1,
                    input_ids2,
                    tokenizers[0],
                    tokenizers[1],
                    text_encoders[0],
                    text_encoders[1],
                    None if not args.full_fp16 else weight_dtype,
                )
        else:
            encoder_hidden_states1 = []
            encoder_hidden_states2 = []
            pool2 = []
            for input_id1, input_id2 in zip(input_ids1, input_ids2):
                input_id1_cache_key = tuple(input_id1.flatten().tolist())
                input_id2_cache_key = tuple(input_id2.flatten().tolist())
                encoder_hidden_states1.append(self.text_encoder1_cache[input_id1_cache_key])
                hidden_states2, p2 = self.text_encoder2_cache[input_id2_cache_key]
                encoder_hidden_states2.append(hidden_states2)
                pool2.append(p2)
            encoder_hidden_states1 = torch.stack(encoder_hidden_states1).to(accelerator.device).to(weight_dtype)
            encoder_hidden_states2 = torch.stack(encoder_hidden_states2).to(accelerator.device).to(weight_dtype)
            pool2 = torch.stack(pool2).to(accelerator.device).to(weight_dtype)

        return encoder_hidden_states1, encoder_hidden_states2, pool2

    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, vae, tokenizer, text_encoder, unet):
        sdxl_train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet)


def setup_parser() -> argparse.ArgumentParser:
    parser = train_network.setup_parser()
    sdxl_train_util.add_sdxl_training_arguments(parser)
    return parser


if __name__ == "__main__":
    parser = setup_parser()

    args = parser.parse_args()
    args = train_util.read_config_from_file(args, parser)

    trainer = SdxlNetworkTrainer()
    trainer.train(args)