Liya Silver SDXL版本v0.5 (ID: 128224)

V1 can be used straight at 1024x1024 or other aspect ratios and gives much better results much easier in my opinion.

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First attempt at training an SDXL LyCoris. Used a ton of high quality images.

Hires. fix 1.25-1,5 starting at 512 x 768 denoising strength: ~0.4

I couldn't train on 1024x due to VRam limitations.

I am very impressed with the stability in her tattoos. No doubt due to the enhanced text capabilities.

Training command:

./venv/bin/accelerate launch ./sdxl_train_network.py \

--enable_bucket \

--min_bucket_reso=256 \

--max_bucket_reso=1024 \

--pretrained_model_name_or_path=sd_xl_base_1.0.safetensors \

--train_data_dir=/img \

--resolution=1024,1024 \

--output_dir=/model \

--logging_dir=/log \

--network_dim=30 \

--network_alpha=15 \

--save_model_as=safetensors \

--network_module=lycoris.kohya \

--network_args conv_dim=30 conv_alpha=15 algo=lora \

--text_encoder_lr=0.5 \

--unet_lr=0.5 \

--output_name=liyasilver_xl \

--lr_scheduler_num_cycles=5 \

--network_dropout=0.1 \

--learning_rate=1.0 \

--lr_scheduler=constant \

--train_batch_size=2 \

--max_train_steps=11300 \

--save_every_n_epochs=1 \

--mixed_precision=bf16 \

--save_precision=bf16 \

--cache_latents \

--optimizer_type=Prodigy \

--max_data_loader_n_workers=0 \

--bucket_reso_steps=64 \

--mem_eff_attn \

--bucket_no_upscale \

--noise_offset=0.0357 \

--sample_sampler=euler_a \

--sample_prompts=/model/sample/prompt.txt \

--sample_every_n_epochs=1 \

--network_train_unet_only \

--gradient_accumulation_steps 10

描述:

训练词语: liyasilver person