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init anima UI

pull/3485/head
GameSpy 2026-02-11 19:21:13 +01:00
parent 4f45d7cf17
commit 8009f31d78
4 changed files with 407 additions and 53 deletions
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218
kohya_gui/class_anima.py Normal file
View File

@ -0,0 +1,218 @@
import gradio as gr
from .common_gui import (
get_any_file_path,
document_symbol,
)
class animaTraining:
def __init__(
self,
headless: bool = False,
finetuning: bool = False,
config: dict = {},
anima_checkbox: gr.Checkbox = False,
) -> None:
self.headless = headless
self.finetuning = finetuning
self.config = config
self.anima_checkbox = anima_checkbox
with gr.Accordion(
"Anima", open=True, visible=False, elem_classes=["anima_background"]
) as anima_accordion:
with gr.Group():
gr.Markdown("### Anima Model Paths")
with gr.Row():
self.qwen3 = gr.Textbox(
label="Qwen3-0.6B Text Encoder Path",
placeholder="Path to Qwen3-0.6B model directory or .safetensors",
value=self.config.get("anima.qwen3", ""),
interactive=True,
)
self.qwen3_button = gr.Button(
document_symbol,
elem_id="open_folder_small",
visible=(not headless),
interactive=True,
)
self.qwen3_button.click(
get_any_file_path,
outputs=self.qwen3,
show_progress=False,
)
self.anima_vae = gr.Textbox(
label="Qwen-Image VAE Path",
placeholder="Path to Qwen-Image VAE .safetensors or .pth",
value=self.config.get("anima.anima_vae", ""),
interactive=True,
)
self.anima_vae_button = gr.Button(
document_symbol,
elem_id="open_folder_small",
visible=(not headless),
interactive=True,
)
self.anima_vae_button.click(
get_any_file_path,
outputs=self.anima_vae,
show_progress=False,
)
with gr.Row():
self.llm_adapter_path = gr.Textbox(
label="LLM Adapter Path (Optional)",
placeholder="Path to LLM adapter .safetensors. If empty, loaded from DiT if present.",
value=self.config.get("anima.llm_adapter_path", ""),
interactive=True,
)
self.llm_adapter_path_button = gr.Button(
document_symbol,
elem_id="open_folder_small",
visible=(not headless),
interactive=True,
)
self.llm_adapter_path_button.click(
get_any_file_path,
outputs=self.llm_adapter_path,
show_progress=False,
)
self.t5_tokenizer_path = gr.Textbox(
label="T5 Tokenizer Path (Optional)",
placeholder="Path to T5 tokenizer directory. If empty, uses bundled configs/t5_old/.",
value=self.config.get("anima.t5_tokenizer_path", ""),
interactive=True,
)
self.t5_tokenizer_path_button = gr.Button(
document_symbol,
elem_id="open_folder_small",
visible=(not headless),
interactive=True,
)
self.t5_tokenizer_path_button.click(
get_any_file_path,
outputs=self.t5_tokenizer_path,
show_progress=False,
)
gr.Markdown("### Anima Training Parameters")
with gr.Row():
self.anima_timestep_sampling = gr.Dropdown(
label="Timestep Sampling",
choices=["sigmoid", "sigma", "uniform", "shift", "flux_shift"],
value=self.config.get("anima.anima_timestep_sampling", "sigmoid"),
info="Timestep sampling method. Same options as FLUX training. Default: sigmoid.",
interactive=True,
)
self.anima_discrete_flow_shift = gr.Number(
label="Discrete Flow Shift",
value=self.config.get("anima.anima_discrete_flow_shift", 1.0),
info="Shift for timestep distribution in Rectified Flow. Default 1.0. Used when timestep_sampling=shift.",
minimum=0.0,
maximum=100.0,
step=0.1,
interactive=True,
)
self.anima_sigmoid_scale = gr.Number(
label="Sigmoid Scale",
value=self.config.get("anima.anima_sigmoid_scale", 1.0),
info="Scale factor for sigmoid/shift/flux_shift timestep sampling. Default 1.0.",
minimum=0.001,
maximum=100.0,
step=0.1,
interactive=True,
)
with gr.Row():
self.qwen3_max_token_length = gr.Number(
label="Qwen3 Max Token Length",
value=self.config.get("anima.qwen3_max_token_length", 512),
info="Maximum token length for Qwen3 tokenizer. Default 512.",
minimum=1,
maximum=4096,
step=1,
interactive=True,
)
self.t5_max_token_length = gr.Number(
label="T5 Max Token Length",
value=self.config.get("anima.t5_max_token_length", 512),
info="Maximum token length for T5 tokenizer. Default 512.",
minimum=1,
maximum=4096,
step=1,
interactive=True,
)
self.anima_attn_mode = gr.Dropdown(
label="Attention Mode",
choices=["torch", "xformers", "flash", "sageattn"],
value=self.config.get("anima.anima_attn_mode", "torch"),
info="Attention implementation. xformers requires split_attn. sageattn is inference-only.",
interactive=True,
)
self.anima_split_attn = gr.Checkbox(
label="Split Attention",
value=self.config.get("anima.anima_split_attn", False),
info="Split attention computation to reduce memory. Required when using xformers attn_mode.",
interactive=True,
)
gr.Markdown("### Memory & Speed")
with gr.Row():
self.anima_cache_text_encoder_outputs = gr.Checkbox(
label="Cache Text Encoder Outputs",
value=self.config.get("anima.anima_cache_text_encoder_outputs", False),
info="Cache Qwen3 outputs to reduce VRAM. Recommended when not training text encoder LoRA.",
interactive=True,
)
self.anima_cache_text_encoder_outputs_to_disk = gr.Checkbox(
label="Cache Text Encoder Outputs to Disk",
value=self.config.get("anima.anima_cache_text_encoder_outputs_to_disk", False),
info="Cache text encoder outputs to disk.",
interactive=True,
)
self.anima_blocks_to_swap = gr.Slider(
label="Blocks to Swap",
value=self.config.get("anima.anima_blocks_to_swap", 0),
info="Number of Transformer blocks to swap CPU<->GPU. 28-block model: max 26. Reduces VRAM at cost of speed.",
minimum=0,
maximum=34,
step=1,
interactive=True,
)
self.anima_unsloth_offload_checkpointing = gr.Checkbox(
label="Unsloth Offload Checkpointing",
value=self.config.get("anima.anima_unsloth_offload_checkpointing", False),
info="Offload activations to CPU RAM using async non-blocking transfers. Faster than cpu_offload_checkpointing. Cannot combine with blocks_to_swap.",
interactive=True,
)
with gr.Row():
self.vae_chunk_size = gr.Number(
label="VAE Chunk Size",
value=self.config.get("anima.vae_chunk_size", 0),
info="Chunk size for Qwen-Image VAE processing to reduce VRAM. 0 = no chunking.",
minimum=0,
maximum=1024,
step=8,
interactive=True,
)
self.vae_disable_cache = gr.Checkbox(
label="VAE Disable Cache",
value=self.config.get("anima.vae_disable_cache", False),
info="Disable internal caching in Qwen-Image VAE to reduce VRAM.",
interactive=True,
)
self.anima_train_llm_adapter = gr.Checkbox(
label="Train LLM Adapter LoRA",
value=self.config.get("anima.anima_train_llm_adapter", False),
info="Apply LoRA to LLM Adapter blocks (6-layer transformer bridge from Qwen3 to T5-compatible space). Only supported with LoRA type 'Anima', ignored for LyCORIS variants.",
interactive=True,
)
self.anima_checkbox.change(
lambda anima_checkbox: gr.Accordion(visible=anima_checkbox),
inputs=[self.anima_checkbox],
outputs=[anima_accordion],
)

72
kohya_gui/class_source_model.py
View File

@ -275,58 +275,45 @@ class SourceModel:
min_width=60,
interactive=True,
)
self.anima_checkbox = gr.Checkbox(
label="Anima",
value=False,
visible=False,
min_width=70,
interactive=True,
)
def toggle_checkboxes(v2, v_parameterization, sdxl_checkbox, sd3_checkbox, flux1_checkbox):
def toggle_checkboxes(v2, v_parameterization, sdxl_checkbox, sd3_checkbox, flux1_checkbox, anima_checkbox):
# Check if all checkboxes are unchecked
if not v2 and not sdxl_checkbox and not sd3_checkbox and not flux1_checkbox:
if not v2 and not sdxl_checkbox and not sd3_checkbox and not flux1_checkbox and not anima_checkbox:
# If all unchecked, return new interactive checkboxes
return (
gr.Checkbox(interactive=True), # v2 checkbox
gr.Checkbox(interactive=False, value=False), # v_parameterization checkbox
gr.Checkbox(interactive=True), # sdxl_checkbox
gr.Checkbox(interactive=True), # sd3_checkbox
gr.Checkbox(interactive=True), # sd3_checkbox
gr.Checkbox(interactive=True), # v2
gr.Checkbox(interactive=False, value=False), # v_parameterization
gr.Checkbox(interactive=True), # sdxl_checkbox
gr.Checkbox(interactive=True), # sd3_checkbox
gr.Checkbox(interactive=True), # flux1_checkbox
gr.Checkbox(interactive=True), # anima_checkbox
)
else:
# If any checkbox is checked, return checkboxes with current interactive state
# If any checkbox is checked, only allow that one to be toggled
return (
gr.Checkbox(interactive=v2), # v2 checkbox
gr.Checkbox(interactive=sdxl_checkbox), # v_parameterization checkbox
gr.Checkbox(interactive=v2), # v2
gr.Checkbox(interactive=sdxl_checkbox), # v_parameterization
gr.Checkbox(interactive=sdxl_checkbox), # sdxl_checkbox
gr.Checkbox(interactive=sd3_checkbox), # sd3_checkbox
gr.Checkbox(interactive=flux1_checkbox), # flux1_checkbox
gr.Checkbox(interactive=sd3_checkbox), # sd3_checkbox
gr.Checkbox(interactive=flux1_checkbox), # flux1_checkbox
gr.Checkbox(interactive=anima_checkbox), # anima_checkbox
)
self.v2.change(
fn=toggle_checkboxes,
inputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
outputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
show_progress=False,
)
self.v_parameterization.change(
fn=toggle_checkboxes,
inputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
outputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
show_progress=False,
)
self.sdxl_checkbox.change(
fn=toggle_checkboxes,
inputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
outputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
show_progress=False,
)
self.sd3_checkbox.change(
fn=toggle_checkboxes,
inputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
outputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
show_progress=False,
)
self.flux1_checkbox.change(
fn=toggle_checkboxes,
inputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
outputs=[self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox],
show_progress=False,
)
_all_checkboxes = [self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox, self.anima_checkbox]
for _cb in [self.v2, self.v_parameterization, self.sdxl_checkbox, self.sd3_checkbox, self.flux1_checkbox, self.anima_checkbox]:
_cb.change(
fn=toggle_checkboxes,
inputs=_all_checkboxes,
outputs=_all_checkboxes,
show_progress=False,
)
with gr.Column():
gr.Group(visible=False)
@ -364,6 +351,7 @@ class SourceModel:
self.sdxl_checkbox,
self.sd3_checkbox,
self.flux1_checkbox,
self.anima_checkbox,
],
show_progress=False,
)

10
kohya_gui/common_gui.py
View File

@ -966,6 +966,7 @@ def set_pretrained_model_name_or_path_input(
sdxl = gr.Checkbox(value=True, visible=False)
sd3 = gr.Checkbox(value=False, visible=False)
flux1 = gr.Checkbox(value=False, visible=False)
anima = gr.Checkbox(value=False, visible=False)
return (
gr.Dropdown(),
v2,
@ -973,6 +974,7 @@ def set_pretrained_model_name_or_path_input(
sdxl,
sd3,
flux1,
anima,
)
# Check if the given pretrained_model_name_or_path is in the list of V2 base models
@ -983,6 +985,7 @@ def set_pretrained_model_name_or_path_input(
sdxl = gr.Checkbox(value=False, visible=False)
sd3 = gr.Checkbox(value=False, visible=False)
flux1 = gr.Checkbox(value=False, visible=False)
anima = gr.Checkbox(value=False, visible=False)
return (
gr.Dropdown(),
v2,
@ -990,6 +993,7 @@ def set_pretrained_model_name_or_path_input(
sdxl,
sd3,
flux1,
anima,
)
# Check if the given pretrained_model_name_or_path is in the list of V parameterization models
@ -1002,6 +1006,7 @@ def set_pretrained_model_name_or_path_input(
sdxl = gr.Checkbox(value=False, visible=False)
sd3 = gr.Checkbox(value=False, visible=False)
flux1 = gr.Checkbox(value=False, visible=False)
anima = gr.Checkbox(value=False, visible=False)
return (
gr.Dropdown(),
v2,
@ -1009,6 +1014,7 @@ def set_pretrained_model_name_or_path_input(
sdxl,
sd3,
flux1,
anima,
)
# Check if the given pretrained_model_name_or_path is in the list of V1 models
@ -1019,6 +1025,7 @@ def set_pretrained_model_name_or_path_input(
sdxl = gr.Checkbox(value=False, visible=False)
sd3 = gr.Checkbox(value=False, visible=False)
flux1 = gr.Checkbox(value=False, visible=False)
anima = gr.Checkbox(value=False, visible=False)
return (
gr.Dropdown(),
v2,
@ -1026,6 +1033,7 @@ def set_pretrained_model_name_or_path_input(
sdxl,
sd3,
flux1,
anima,
)
# Check if the model_list is set to 'custom'
@ -1034,6 +1042,7 @@ def set_pretrained_model_name_or_path_input(
sdxl = gr.Checkbox(visible=True)
sd3 = gr.Checkbox(visible=True)
flux1 = gr.Checkbox(visible=True)
anima = gr.Checkbox(visible=True)
# Auto-detect model type if safetensors file path is given
if pretrained_model_name_or_path.lower().endswith(".safetensors"):
@ -1058,6 +1067,7 @@ def set_pretrained_model_name_or_path_input(
sdxl,
sd3,
flux1,
anima,
)

160
kohya_gui/lora_gui.py
View File

@ -42,6 +42,7 @@ from .class_huggingface import HuggingFace
from .class_metadata import MetaData
from .class_gui_config import KohyaSSGUIConfig
from .class_flux1 import flux1Training
from .class_anima import animaTraining
from .dreambooth_folder_creation_gui import (
gradio_dreambooth_folder_creation_tab,
@ -319,6 +320,26 @@ def save_configuration(
sd3_text_encoder_batch_size,
weighting_scheme,
sd3_checkbox,
# Anima parameters
anima_checkbox,
anima_qwen3,
anima_vae,
anima_llm_adapter_path,
anima_t5_tokenizer_path,
anima_timestep_sampling,
anima_discrete_flow_shift,
anima_sigmoid_scale,
anima_qwen3_max_token_length,
anima_t5_max_token_length,
anima_attn_mode,
anima_split_attn,
anima_cache_text_encoder_outputs,
anima_cache_text_encoder_outputs_to_disk,
anima_blocks_to_swap,
anima_unsloth_offload_checkpointing,
anima_vae_chunk_size,
anima_vae_disable_cache,
anima_train_llm_adapter,
):
# Get list of function parameters and values
parameters = list(locals().items())
@ -605,6 +626,26 @@ def open_configuration(
sd3_text_encoder_batch_size,
weighting_scheme,
sd3_checkbox,
# Anima parameters
anima_checkbox,
anima_qwen3,
anima_vae,
anima_llm_adapter_path,
anima_t5_tokenizer_path,
anima_timestep_sampling,
anima_discrete_flow_shift,
anima_sigmoid_scale,
anima_qwen3_max_token_length,
anima_t5_max_token_length,
anima_attn_mode,
anima_split_attn,
anima_cache_text_encoder_outputs,
anima_cache_text_encoder_outputs_to_disk,
anima_blocks_to_swap,
anima_unsloth_offload_checkpointing,
anima_vae_chunk_size,
anima_vae_disable_cache,
anima_train_llm_adapter,
##
training_preset,
):
@ -982,6 +1023,26 @@ def train_model(
sd3_text_encoder_batch_size,
weighting_scheme,
sd3_checkbox,
# Anima parameters
anima_checkbox,
anima_qwen3,
anima_vae,
anima_llm_adapter_path,
anima_t5_tokenizer_path,
anima_timestep_sampling,
anima_discrete_flow_shift,
anima_sigmoid_scale,
anima_qwen3_max_token_length,
anima_t5_max_token_length,
anima_attn_mode,
anima_split_attn,
anima_cache_text_encoder_outputs,
anima_cache_text_encoder_outputs_to_disk,
anima_blocks_to_swap,
anima_unsloth_offload_checkpointing,
anima_vae_chunk_size,
anima_vae_disable_cache,
anima_train_llm_adapter,
):
# Get list of function parameters and values
parameters = list(locals().items())
@ -1019,6 +1080,24 @@ def train_model(
)
return TRAIN_BUTTON_VISIBLE
if anima_checkbox:
log.info(f"Validating lora type is Anima if Anima checkbox is checked...")
if LoRA_type != "Anima" and "LyCORIS" not in LoRA_type:
log.error(
"LoRA type must be set to 'Anima' or 'LyCORIS' if Anima checkbox is checked."
)
return TRAIN_BUTTON_VISIBLE
if not anima_qwen3:
log.error(
"Qwen3 Text Encoder path is required for Anima training. Please set it in the Anima section."
)
return TRAIN_BUTTON_VISIBLE
if not anima_vae:
log.error(
"Anima VAE path is required for Anima training. Please set it in the Anima section."
)
return TRAIN_BUTTON_VISIBLE
#
# Validate paths
#
@ -1265,6 +1344,8 @@ def train_model(
run_cmd.append(rf"{scriptdir}/sd-scripts/flux_train_network.py")
elif sd3_checkbox:
run_cmd.append(rf"{scriptdir}/sd-scripts/sd3_train_network.py")
elif anima_checkbox:
run_cmd.append(rf"{scriptdir}/sd-scripts/anima_train_network.py")
else:
run_cmd.append(rf"{scriptdir}/sd-scripts/train_network.py")
@ -1375,6 +1456,11 @@ def train_model(
if value:
network_args += f" {key}={value}"
if LoRA_type == "Anima":
network_module = "networks.lora_anima"
if anima_train_llm_adapter:
network_args += " train_llm_adapter=True"
if LoRA_type in ["Kohya LoCon", "Standard"]:
kohya_lora_var_list = [
"down_lr_weight",
@ -1540,14 +1626,14 @@ def train_model(
if (sdxl and sdxl_cache_text_encoder_outputs)
or (flux1_checkbox and flux1_cache_text_encoder_outputs)
or (sd3_checkbox and sd3_cache_text_encoder_outputs)
or (anima_checkbox and anima_cache_text_encoder_outputs)
else None
),
"cache_text_encoder_outputs_to_disk": (
True
if flux1_checkbox
and flux1_cache_text_encoder_outputs_to_disk
or sd3_checkbox
and sd3_cache_text_encoder_outputs_to_disk
if (flux1_checkbox and flux1_cache_text_encoder_outputs_to_disk)
or (sd3_checkbox and sd3_cache_text_encoder_outputs_to_disk)
or (anima_checkbox and anima_cache_text_encoder_outputs_to_disk)
else None
),
"caption_dropout_every_n_epochs": int(caption_dropout_every_n_epochs),
@ -1609,7 +1695,7 @@ def train_model(
"max_bucket_reso": max_bucket_reso,
"max_grad_norm": max_grad_norm,
"max_timestep": max_timestep if max_timestep != 0 else None,
"max_token_length": int(max_token_length) if not flux1_checkbox else None,
"max_token_length": int(max_token_length) if not flux1_checkbox and not anima_checkbox else None,
"max_train_epochs": (
int(max_train_epochs) if int(max_train_epochs) != 0 else None
),
@ -1693,7 +1779,7 @@ def train_model(
"save_state_to_huggingface": save_state_to_huggingface,
"scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred,
"scale_weight_norms": scale_weight_norms,
"sdpa": True if xformers == "sdpa" else None,
"sdpa": True if xformers == "sdpa" and not anima_checkbox else None,
"seed": int(seed) if int(seed) != 0 else None,
"shuffle_caption": shuffle_caption,
"skip_cache_check": skip_cache_check,
@ -1709,12 +1795,12 @@ def train_model(
"v2": v2,
"v_parameterization": v_parameterization,
"v_pred_like_loss": v_pred_like_loss if v_pred_like_loss != 0 else None,
"vae": vae,
"vae": anima_vae if anima_checkbox else vae,
"vae_batch_size": vae_batch_size if vae_batch_size != 0 else None,
"wandb_api_key": wandb_api_key,
"wandb_run_name": wandb_run_name if wandb_run_name != "" else output_name,
"weighted_captions": weighted_captions,
"xformers": True if xformers == "xformers" else None,
"xformers": True if xformers == "xformers" and not anima_checkbox else None,
# SD3 only Parameters
# "cache_text_encoder_outputs": see previous assignment above for code
# "cache_text_encoder_outputs_to_disk": see previous assignment above for code
@ -1745,9 +1831,7 @@ def train_model(
"ae": ae if flux1_checkbox else None,
# "clip_l": see previous assignment above for code
"t5xxl": t5xxl_value,
"discrete_flow_shift": float(discrete_flow_shift) if flux1_checkbox else None,
"model_prediction_type": model_prediction_type if flux1_checkbox else None,
"timestep_sampling": timestep_sampling if flux1_checkbox else None,
"split_mode": split_mode if flux1_checkbox else None,
"t5xxl_max_token_length": (
int(t5xxl_max_token_length) if flux1_checkbox else None
@ -1758,9 +1842,33 @@ def train_model(
"cpu_offload_checkpointing": (
cpu_offload_checkpointing if flux1_checkbox else None
),
"blocks_to_swap": blocks_to_swap if flux1_checkbox or sd3_checkbox else None,
"blocks_to_swap": blocks_to_swap if flux1_checkbox or sd3_checkbox else (
int(anima_blocks_to_swap) if anima_checkbox and anima_blocks_to_swap else None
),
"single_blocks_to_swap": single_blocks_to_swap if flux1_checkbox else None,
"double_blocks_to_swap": double_blocks_to_swap if flux1_checkbox else None,
# Anima specific parameters
"qwen3": anima_qwen3 if anima_checkbox else None,
"llm_adapter_path": anima_llm_adapter_path if anima_checkbox and anima_llm_adapter_path else None,
"t5_tokenizer_path": anima_t5_tokenizer_path if anima_checkbox and anima_t5_tokenizer_path else None,
"timestep_sampling": (
timestep_sampling if flux1_checkbox
else anima_timestep_sampling if anima_checkbox
else None
),
"discrete_flow_shift": (
float(discrete_flow_shift) if flux1_checkbox
else float(anima_discrete_flow_shift) if anima_checkbox
else None
),
"sigmoid_scale": float(anima_sigmoid_scale) if anima_checkbox else None,
"qwen3_max_token_length": int(anima_qwen3_max_token_length) if anima_checkbox else None,
"t5_max_token_length": int(anima_t5_max_token_length) if anima_checkbox else None,
"attn_mode": anima_attn_mode if anima_checkbox else None,
"split_attn": anima_split_attn if anima_checkbox else None,
"unsloth_offload_checkpointing": anima_unsloth_offload_checkpointing if anima_checkbox else None,
"vae_chunk_size": int(anima_vae_chunk_size) if anima_checkbox and anima_vae_chunk_size else None,
"vae_disable_cache": anima_vae_disable_cache if anima_checkbox else None,
}
# Given dictionary `config_toml_data`
@ -1923,6 +2031,7 @@ def lora_tab(
LoRA_type = gr.Dropdown(
label="LoRA type",
choices=[
"Anima",
"Flux1",
"Flux1 OFT",
"Kohya DyLoRA",
@ -2238,6 +2347,7 @@ def lora_tab(
"update_params": {
"visible": LoRA_type
in {
"Anima",
"Flux1",
"Flux1 OFT",
"Kohya DyLoRA",
@ -2292,6 +2402,7 @@ def lora_tab(
"update_params": {
"visible": LoRA_type
in {
"Anima",
"Flux1",
"Flux1 OFT",
"Standard",
@ -2314,6 +2425,7 @@ def lora_tab(
"update_params": {
"visible": LoRA_type
in {
"Anima",
"Flux1",
"Flux1 OFT",
"Standard",
@ -2336,6 +2448,7 @@ def lora_tab(
"update_params": {
"visible": LoRA_type
in {
"Anima",
"Flux1",
"Flux1 OFT",
"Standard",
@ -2679,6 +2792,11 @@ def lora_tab(
headless=headless, config=config, sd3_checkbox=source_model.sd3_checkbox
)
# Add Anima Parameters
anima_training = animaTraining(
headless=headless, config=config, anima_checkbox=source_model.anima_checkbox
)
with gr.Accordion(
"Advanced", open=False, elem_classes="advanced_background"
):
@ -3029,6 +3147,26 @@ def lora_tab(
sd3_training.sd3_text_encoder_batch_size,
sd3_training.weighting_scheme,
source_model.sd3_checkbox,
# Anima Parameters
source_model.anima_checkbox,
anima_training.qwen3,
anima_training.anima_vae,
anima_training.llm_adapter_path,
anima_training.t5_tokenizer_path,
anima_training.anima_timestep_sampling,
anima_training.anima_discrete_flow_shift,
anima_training.anima_sigmoid_scale,
anima_training.qwen3_max_token_length,
anima_training.t5_max_token_length,
anima_training.anima_attn_mode,
anima_training.anima_split_attn,
anima_training.anima_cache_text_encoder_outputs,
anima_training.anima_cache_text_encoder_outputs_to_disk,
anima_training.anima_blocks_to_swap,
anima_training.anima_unsloth_offload_checkpointing,
anima_training.vae_chunk_size,
anima_training.vae_disable_cache,
anima_training.anima_train_llm_adapter,
]
configuration.button_open_config.click(