Character:yumemizuki mizuki, genshin imacpt,
trained base on noobaiXLNAIXL_epsilonPred11
weight: 1, <= 1,
official costume:
trigger word:
yumemizuki mizuki,1girl,official outfit,official hair ornament,genshin impact,
others tag:
1girl, bell, black bow, black choker, bow, choker, closed mouth, frilled hairband, frilled sleeves, frills, hairband, hand on lap, hand up, japanese clothes, kimono, kneeling, light blush, long sleeves, neck bell, nihongami, pink kimono, pointy ears, short kimono, sleeve bow, sleeveless, sleeveless kimono, smile, socks, solo, tail, tapir tail, white hairband, white socks, wide sleeves, yumemizuki mizuki, genshin impact,
Description
Character:yumemizuki mizuki, genshin imacpt,
trained base on noobaiXLNAIXL_epsilonPred11
weight: 1, <= 1,
official costume:
trigger word:
yumemizuki mizuki,1girl,official outfit,official hair ornament,genshin impact,
others tag:
1girl, bell, black bow, black choker, bow, choker, closed mouth, frilled hairband, frilled sleeves, frills, hairband, hand on lap, hand up, japanese clothes, kimono, kneeling, light blush, long sleeves, neck bell, nihongami, pink kimono, pointy ears, short kimono, sleeve bow, sleeveless, sleeveless kimono, smile, socks, solo, tail, tapir tail, white hairband, white socks, wide sleeves, yumemizuki mizuki, genshin impact,
FAQ
Comments (4)
Could you share your training parameters, expert? My training results are always poor. The loss of my LoRA training stabilizes around 0.05, but after saving the seventh checkpoint, the gradient explodes, and the loss stops decreasing and starts increasing instead. Additionally, when the LoRA weight is set to 1.0, the output becomes blurry, and I have to adjust it to 0.7 for it to be usable. Below are my training parameters:
# Processing
resolution**: 1024
caption_extension**: ".txt"
shuffle_tags**: True
shuffle_caption**: True
activation_tags**: "1"
keep_tokens**: 1
# Steps
num_repeats**: 100
preferred_unit**: "Epochs"
how_many**: 10
max_train_epochs**: 10
max_train_steps**: None
save_every_n_epochs**: 1
keep_only_last_n_epochs**: 10
# Learning
unet_lr**: 5e-5
text_encoder_lr**: 6e-5
lr_scheduler**: "cosine_with_restarts"
lr_scheduler_number**: 3
lr_warmup_ratio**: 0.05
lr_warmup_steps**: 0
min_snr_gamma**: 8.0
multinoise**: True
# Structure
lora_type**: "LoRA"
network_dim**: 8
network_alpha**: 4
conv_dim**: 4
conv_alpha**: 1
network_module**: "networks.lora"
network_args**: ["conv_dim=4", "conv_alpha=1"]
# Training
train_batch_size**: 4
cross_attention**: "sdpa"
mixed_precision**: "bf16"
cache_latents**: True
cache_latents_to_drive**: True
cache_text_encoder_outputs**: False
# Advanced
optimizer**: "Prodigy"
optimizer_args**: ["decouple=True", "weight_decay=0.01", "betas=[0.9,0.999]", "d_coef=2", "use_bias_correction=True", "safeguard_warmup=True"]
recommended_values**: True
Hi, I have 3 suggestions for this.
1. Don't use too high a repeat. Unlike epo, it is easy to overfit if it is too high, so you can lower the rep and increase the corresponding epo.
2. In my opinion, there are some problems with your parameters. It is recommended that you restore the default, which usually can get a good lora.
3. Please do not judge the quality of Lora by loss, please focus on the output image.
@ASN0515 Thank you very much!
Details
Available On (1 platform)
Same model published on other platforms. May have additional downloads or version variants.


