Upload lora-scripts/sd-scripts/sdxl_train_textual_inversion.py with huggingface_hub
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lora-scripts/sd-scripts/sdxl_train_textual_inversion.py
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import argparse
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import os
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import regex
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import torch
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from library.device_utils import init_ipex
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init_ipex()
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from library import sdxl_model_util, sdxl_train_util, train_util
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import train_textual_inversion
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class SdxlTextualInversionTrainer(train_textual_inversion.TextualInversionTrainer):
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def __init__(self):
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super().__init__()
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self.vae_scale_factor = sdxl_model_util.VAE_SCALE_FACTOR
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self.is_sdxl = True
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def assert_extra_args(self, args, train_dataset_group):
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super().assert_extra_args(args, train_dataset_group)
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sdxl_train_util.verify_sdxl_training_args(args, supportTextEncoderCaching=False)
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train_dataset_group.verify_bucket_reso_steps(32)
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def load_target_model(self, args, weight_dtype, accelerator):
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(
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load_stable_diffusion_format,
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text_encoder1,
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text_encoder2,
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vae,
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unet,
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logit_scale,
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ckpt_info,
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) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
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self.load_stable_diffusion_format = load_stable_diffusion_format
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self.logit_scale = logit_scale
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self.ckpt_info = ckpt_info
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return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, [text_encoder1, text_encoder2], vae, unet
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def load_tokenizer(self, args):
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tokenizer = sdxl_train_util.load_tokenizers(args)
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return tokenizer
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def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
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input_ids1 = batch["input_ids"]
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input_ids2 = batch["input_ids2"]
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with torch.enable_grad():
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input_ids1 = input_ids1.to(accelerator.device)
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input_ids2 = input_ids2.to(accelerator.device)
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encoder_hidden_states1, encoder_hidden_states2, pool2 = train_util.get_hidden_states_sdxl(
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args.max_token_length,
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input_ids1,
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input_ids2,
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tokenizers[0],
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tokenizers[1],
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text_encoders[0],
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text_encoders[1],
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None if not args.full_fp16 else weight_dtype,
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accelerator=accelerator,
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)
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return encoder_hidden_states1, encoder_hidden_states2, pool2
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def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
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noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype
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# get size embeddings
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orig_size = batch["original_sizes_hw"]
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crop_size = batch["crop_top_lefts"]
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target_size = batch["target_sizes_hw"]
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embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype)
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# concat embeddings
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encoder_hidden_states1, encoder_hidden_states2, pool2 = text_conds
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vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)
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text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)
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noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
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return noise_pred
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def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet, prompt_replacement):
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sdxl_train_util.sample_images(
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accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet, prompt_replacement
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)
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def save_weights(self, file, updated_embs, save_dtype, metadata):
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state_dict = {"clip_l": updated_embs[0], "clip_g": updated_embs[1]}
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if save_dtype is not None:
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for key in list(state_dict.keys()):
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v = state_dict[key]
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v = v.detach().clone().to("cpu").to(save_dtype)
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state_dict[key] = v
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if os.path.splitext(file)[1] == ".safetensors":
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from safetensors.torch import save_file
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save_file(state_dict, file, metadata)
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else:
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torch.save(state_dict, file)
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def load_weights(self, file):
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if os.path.splitext(file)[1] == ".safetensors":
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from safetensors.torch import load_file
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data = load_file(file)
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else:
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data = torch.load(file, map_location="cpu")
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emb_l = data.get("clip_l", None) # ViT-L text encoder 1
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emb_g = data.get("clip_g", None) # BiG-G text encoder 2
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assert (
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emb_l is not None or emb_g is not None
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), f"weight file does not contains weights for text encoder 1 or 2 / 重みファイルにテキストエンコーダー1または2の重みが含まれていません: {file}"
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return [emb_l, emb_g]
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def setup_parser() -> argparse.ArgumentParser:
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parser = train_textual_inversion.setup_parser()
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# don't add sdxl_train_util.add_sdxl_training_arguments(parser): because it only adds text encoder caching
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# sdxl_train_util.add_sdxl_training_arguments(parser)
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return parser
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if __name__ == "__main__":
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parser = setup_parser()
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args = parser.parse_args()
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train_util.verify_command_line_training_args(args)
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args = train_util.read_config_from_file(args, parser)
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trainer = SdxlTextualInversionTrainer()
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trainer.train(args)
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