Anime Composition XL

在 L20 48G 上 训练了超过70个GPU小时我想从零开始训练一个动漫通用模型，这个模型是我积累经验的产物。使用10K图片，从Animagine-xl-3.0-base 上训练微调在训练中使用高品质数据集和质量词进行微调。每一张图片都具有质量词。质量词分别是：best,hight,amazing, 但遗憾的是，他们并不生效--Training model: Animagine-xl-3.0-baseTraining resolution: 1024*1024Recommended output resolution: 1024特别鸣谢：算力赞助 nieta.art / GPU Sponsor: NetaDiscord : https://discord.com/invite/CFpyHceF52Recommended dataset collection and processing tools: https://github.com/deepghs/waifucPossibly the most user-friendly dataset processing UI currently in use：https://github.com/Eugeoter/waifuset在训练参数上，此模型大部分参考了Animagine XL 3.0的训练配置Training configuration[sdxl_arguments] cache_text_encoder_outputs = false no_half_vae = false min_timestep = 0 max_timestep = 1000

[model_arguments] pretrained_model_name_or_path = "/root/autodl-tmp/stable-diffusion-webui/models/Stable-diffusion/animagine-xl-3.0-base.safetensors" vae = "/root/vae/sdxl_vae.safetensors"

[dataset_arguments] shuffle_caption = true debug_dataset = false train_data_dir = "/root/autodl-tmp/train/quany" dataset_repeats = 1 keep_tokens_separator = "|||" resolution = "1024, 1024" caption_dropout_rate = 0 caption_tag_dropout_rate = 0 caption_dropout_every_n_epochs = 0 token_warmup_min = 1 token_warmup_step = 0 enable_bucket = true min_bucket_reso=512 max_bucket_reso=2048 bucket_reso_steps=64 cache_latents = true cache_latents_to_disk = true

[training_arguments] output_dir = "/root/autodl-tmp/stable-diffusion-webui/models/Stable-diffusion/quany" output_name = "composition" save_precision = "fp16" train_batch_size=8
vae_batch_size=4 max_train_epochs=20 save_every_n_epochs=2 max_token_length = 225 mem_eff_attn = false xformers = true sdpa = false max_data_loader_n_workers = 8 persistent_data_loader_workers = true gradient_checkpointing = true gradient_accumulation_steps = 1 mixed_precision = "fp16"

[sample_prompt_arguments] sample_every_n_steps = 200 sample_sampler = "euler_a" sample_prompts="/root/example.txt"

[saving_arguments] save_model_as = "safetensors"

[optimizer_arguments] optimizer_type = "AdaFactor" learning_rate = 7.5e-7 train_text_encoder = true learning_rate_te1 = 3.75e-7 learning_rate_te2 = 3.75e-7 optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",] lr_scheduler = "constant_with_warmup" lr_warmup_steps = 100 max_grad_norm = 0