Photo Background - 2d Compositing｜写真背景・二次元合成
Trained on 2d illustrations composited on a photo background.
This is a small LoRA I thought would be interesting to see how models trained on illustrations or real world images/video can produce the composite, mixed reality effect.
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.
Metadata is included in all uploaded files, you can drag the generated videos into ComfyUI to use the embedded workflows.
Description
Trained on Anima Preview 3 Base
Training config:
# trained using diffusion-pipe commit 6e95020cad0b3cd7dcb93ce42b358669051bf6d2
output_dir = '/mnt/d/anima/training_output/nijigen'
dataset = 'dataset-anima.toml'
# training settings
epochs = 1000
# Per-resolution batch sizes
# micro_batch_size_per_gpu = [[512, 32], [1024, 32], [1536, 16]]
micro_batch_size_per_gpu = 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 = '/ComfyUI/models/diffusion_models/anima-preview3-base.safetensors'
vae_path = '/ComfyUI/models/vae/qwen_image_vae.safetensors'
llm_path = '/ComfyUI_windows_portable/ComfyUI/models/text_encoders/qwen_3_06b_base.safetensors'
dtype = 'bfloat16'
#cache_text_embeddings = false
llm_adapter_lr = 0
#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 = 2e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8resolutions = [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_niji_captions'
repeats = 8