Arknights: Endfield | Operators Collection
Intended to generate non-commercial fan works of Operators from the Arknights: Endfield video game.
ℹ️ LoRA work best when applied to the base models on which they are trained. Please read the About This Version on the appropriate base models and workflow/training information.
Trained on a large mixed NL and tags dataset with 30% tag dropout rate, at mixed [1024, 1280] resolutions. Previews are mostly generated at 1024x1536.
Operators (game version 1.0):
female endministrator \(arknights\)
male endministrator \(arknights\)
perlica \(arknights\)
chen qianyu \(arknights\)
akekuri \(arknights\)
alesh \(arknights\)
antal \(arknights\)
arclight \(arknights\)
ardelia \(arknights\)
avywenna \(arknights\)
catcher \(arknights\)
da pan \(arknights\)
estella \(arknights\)
fluorite \(arknights\)
gilberta \(arknights\)
laevatain \(arknights\)
last rite \(arknights\)
lifeng \(arknights\)
snowshine \(arknights\)
wulfgard \(arknights\)
xaihi \(arknights\)
yvonne \(arknights\)
Operators (game version 1.1):
mi fu \(arknights\)
rossi \(arknights\)
tangtang \(arknights\)
zhuang fangyi \(arknights\)
Antagonists:
ardashir \(arknights\)
nefarith \(arknights\)
Works best in combination with NL if you name a character, then describe their basic appearance.
A vibrant and dynamic illustration of Yvonne from Arknights: Endfield, featuring her with long pink hair styled in twintails, pointy ears, and small horns, along with a playful tail...
To be fixed:
Pogranichnik had a mistake for his labelling
Ember did not seem to learn her outfit/features
Description
Trained on Anima Preview 3 Base
Dataset uses a mix of tags and natural language captions
Less prominent characters were not learned as well in this version
Training config:
# trained using diffusion-pipe commit 6e95020cad0b3cd7dcb93ce42b358669051bf6d2
output_dir = '/mnt/d/anima/training_output/arknights3'
dataset = 'dataset-anima.toml'
# training settings
epochs = 1000
# Per-resolution batch sizes
micro_batch_size_per_gpu = [[512, 64], [1024, 32], [1536, 16]]
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 100
# misc settings
save_every_n_epochs = 1
#save_every_n_steps = 1000
#save_every_n_examples = 4096000
#checkpoint_every_n_epochs = 1
#checkpoint_every_n_minutes = 120
activation_checkpointing = true
#reentrant_activation_checkpointing = true
partition_method = 'parameters'
# partition_method = 'manual'
# partition_split = [10]
save_dtype = 'bfloat16'
caching_batch_size = 1
map_num_proc = 8
steps_per_print = 1
compile = true
[model]
type = 'anima'
transformer_path = '/mnt/c/workspace/ComfyUI_windows_portable_nvidia/ComfyUI_windows_portable/ComfyUI/models/diffusion_models/anima-preview3-base.safetensors'
vae_path = '/mnt/c/workspace/ComfyUI_windows_portable_nvidia/ComfyUI_windows_portable/ComfyUI/models/vae/qwen_image_vae.safetensors'
llm_path = '/mnt/c/workspace/ComfyUI_windows_portable_nvidia/ComfyUI_windows_portable/ComfyUI/models/text_encoders/qwen_3_06b_base.safetensors'
dtype = 'bfloat16'
#cache_text_embeddings = false
llm_adapter_lr = 2e-6
#timestep_sample_method = 'uniform'
#flux_shift = true
#multiscale_loss_weight = 0.5
sigmoid_scale = 1.3
[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'
[optimizer]
type = 'adamw_optimi'
lr = 4e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8Dataset config:
resolutions = [512, 1024, 1536]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 9
[[directory]]
path = '/mnt/d/training_data/images_arknights_captions'
repeats = 4