mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
segment prototype
This commit is contained in:
Vladimir Mandic
+208
-204
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import gradio as gr
|
||||
from modules import sd_hijack, script_callbacks, shared
|
||||
from modules import script_callbacks, shared
|
||||
from modules.ui_components import FormRow
|
||||
from modules.ui_common import create_refresh_button
|
||||
from modules.ui_sections import create_sampler_inputs
|
||||
@@ -150,227 +150,231 @@ def create_ui():
|
||||
)
|
||||
|
||||
### train embedding tab
|
||||
with gr.Tab(label="Train embedding", id="train_embedding_tab") as tab_ti:
|
||||
tab_ti.select(fn=lambda: train_tab_change('ti'), inputs=[], outputs=[action_pp, action_ti, action_hn])
|
||||
def get_textual_inversion_template_names():
|
||||
return sorted(textual_inversion.textual_inversion_templates)
|
||||
if shared.backend == shared.Backend.ORIGINAL:
|
||||
from modules import sd_hijack
|
||||
with gr.Tab(label="Train embedding", id="train_embedding_tab") as tab_ti:
|
||||
tab_ti.select(fn=lambda: train_tab_change('ti'), inputs=[], outputs=[action_pp, action_ti, action_hn])
|
||||
def get_textual_inversion_template_names():
|
||||
return sorted(textual_inversion.textual_inversion_templates)
|
||||
|
||||
gr.HTML('<h2>Select existing embedding to continue training or create a new one</h2>')
|
||||
with FormRow():
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
ti_name = gr.Dropdown(label='Select embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
create_refresh_button(ti_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
|
||||
with gr.Column():
|
||||
ti_new_name = gr.Textbox(label="Create emebedding")
|
||||
ti_init_text = gr.Textbox(label="Initialization text", value="*")
|
||||
ti_vectors = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
|
||||
ti_overwrite = gr.Checkbox(value=False, label="Overwrite Old Embedding")
|
||||
with gr.Row():
|
||||
ti_create = gr.Button(value="Create embedding", variant='secondary')
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training parameters</h2>')
|
||||
ti_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
|
||||
gr.HTML('<h2>Select existing embedding to continue training or create a new one</h2>')
|
||||
with FormRow():
|
||||
ti_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
ti_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
|
||||
ti_batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
||||
ti_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
|
||||
ti_steps = gr.Number(label='Max steps', value=1000, precision=0)
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
ti_name = gr.Dropdown(label='Select embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
create_refresh_button(ti_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
|
||||
with gr.Column():
|
||||
ti_new_name = gr.Textbox(label="Create emebedding")
|
||||
ti_init_text = gr.Textbox(label="Initialization text", value="*")
|
||||
ti_vectors = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
|
||||
ti_overwrite = gr.Checkbox(value=False, label="Overwrite Old Embedding")
|
||||
with gr.Row():
|
||||
ti_create = gr.Button(value="Create embedding", variant='secondary')
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training images</h2>')
|
||||
ti_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
with FormRow():
|
||||
ti_varsize = gr.Checkbox(label="Do not resize images", value=False)
|
||||
ti_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
|
||||
ti_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
|
||||
ti_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training parameters</h2>')
|
||||
ti_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
|
||||
with FormRow():
|
||||
ti_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
ti_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
|
||||
ti_batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
||||
ti_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
|
||||
ti_steps = gr.Number(label='Max steps', value=1000, precision=0)
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Dataset processing</h2>')
|
||||
with FormRow():
|
||||
ti_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(ti_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
||||
ti_shuffle = gr.Checkbox(label="Shuffle tags", value=False)
|
||||
ti_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
|
||||
ti_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training images</h2>')
|
||||
ti_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
with FormRow():
|
||||
ti_varsize = gr.Checkbox(label="Do not resize images", value=False)
|
||||
ti_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
|
||||
ti_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
|
||||
ti_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training outputs</h2>')
|
||||
with FormRow():
|
||||
ti_create_every = gr.Number(label='Create interim images', value=500, precision=0)
|
||||
ti_save_every = gr.Number(label='Create interim embeddings', value=500, precision=0)
|
||||
ti_save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
|
||||
ti_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
|
||||
ti_log_directory = gr.Textbox(label='Log directory', placeholder="Defaults to train/log/embedding", value="")
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Dataset processing</h2>')
|
||||
with FormRow():
|
||||
ti_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(ti_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
||||
ti_shuffle = gr.Checkbox(label="Shuffle tags", value=False)
|
||||
ti_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
|
||||
ti_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
|
||||
|
||||
ti_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training outputs</h2>')
|
||||
with FormRow():
|
||||
ti_create_every = gr.Number(label='Create interim images', value=500, precision=0)
|
||||
ti_save_every = gr.Number(label='Create interim embeddings', value=500, precision=0)
|
||||
ti_save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
|
||||
ti_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
|
||||
ti_log_directory = gr.Textbox(label='Log directory', placeholder="Defaults to train/log/embedding", value="")
|
||||
|
||||
ti_create.click(
|
||||
fn=modules.textual_inversion.ui.create_embedding,
|
||||
inputs=[
|
||||
ti_new_name,
|
||||
ti_init_text,
|
||||
ti_vectors,
|
||||
ti_overwrite,
|
||||
],
|
||||
outputs=[
|
||||
ti_name,
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
ti_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
||||
|
||||
ti_train.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
|
||||
_js="startTrainMonitor",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
ti_name,
|
||||
ti_learn_rate,
|
||||
ti_batch_size,
|
||||
ti_gradient_step,
|
||||
ti_dataset_directory,
|
||||
ti_log_directory,
|
||||
ti_width,
|
||||
ti_height,
|
||||
ti_varsize,
|
||||
ti_steps,
|
||||
ti_clip_grad_mode,
|
||||
ti_clip_grad_value,
|
||||
ti_shuffle,
|
||||
ti_tag_drop_out,
|
||||
ti_latent_sampling_method,
|
||||
ti_use_weight,
|
||||
ti_create_every,
|
||||
ti_save_every,
|
||||
ti_template,
|
||||
ti_save_image_with_stored_embedding,
|
||||
ti_preview_from_txt2img,
|
||||
*txt2img_preview_params,
|
||||
],
|
||||
outputs=[
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
ti_create.click(
|
||||
fn=modules.textual_inversion.ui.create_embedding,
|
||||
inputs=[
|
||||
ti_new_name,
|
||||
ti_init_text,
|
||||
ti_vectors,
|
||||
ti_overwrite,
|
||||
],
|
||||
outputs=[
|
||||
ti_name,
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
ti_train.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
|
||||
_js="startTrainMonitor",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
ti_name,
|
||||
ti_learn_rate,
|
||||
ti_batch_size,
|
||||
ti_gradient_step,
|
||||
ti_dataset_directory,
|
||||
ti_log_directory,
|
||||
ti_width,
|
||||
ti_height,
|
||||
ti_varsize,
|
||||
ti_steps,
|
||||
ti_clip_grad_mode,
|
||||
ti_clip_grad_value,
|
||||
ti_shuffle,
|
||||
ti_tag_drop_out,
|
||||
ti_latent_sampling_method,
|
||||
ti_use_weight,
|
||||
ti_create_every,
|
||||
ti_save_every,
|
||||
ti_template,
|
||||
ti_save_image_with_stored_embedding,
|
||||
ti_preview_from_txt2img,
|
||||
*txt2img_preview_params,
|
||||
],
|
||||
outputs=[
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
### train hypernetwork tab
|
||||
|
||||
with gr.Tab(label="Train hypernetwork", id="train_hypernetwork_tab") as tab_hn:
|
||||
tab_hn.select(fn=lambda: train_tab_change('hn'), inputs=[], outputs=[action_pp, action_ti, action_hn])
|
||||
gr.HTML('<h2>Select existing hypernetwork to continue training or create a new one</h2>')
|
||||
with FormRow():
|
||||
with gr.Column():
|
||||
if shared.backend == shared.Backend.ORIGINAL:
|
||||
from modules import sd_hijack
|
||||
with gr.Tab(label="Train hypernetwork", id="train_hypernetwork_tab") as tab_hn:
|
||||
tab_hn.select(fn=lambda: train_tab_change('hn'), inputs=[], outputs=[action_pp, action_ti, action_hn])
|
||||
gr.HTML('<h2>Select existing hypernetwork to continue training or create a new one</h2>')
|
||||
with FormRow():
|
||||
with gr.Column():
|
||||
with FormRow():
|
||||
hn_name = gr.Dropdown(label='Hypernetwork', choices=sorted(shared.hypernetworks))
|
||||
create_refresh_button(hn_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
|
||||
with gr.Column():
|
||||
hn_new_name = gr.Textbox(label="Name")
|
||||
hn_new_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
|
||||
hn_new_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
|
||||
with gr.Row():
|
||||
hn_new_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
|
||||
hn_new_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
|
||||
hn_new_add_layer_norm = gr.Checkbox(label="Add layer normalization")
|
||||
hn_new_use_dropout = gr.Checkbox(label="Use dropout")
|
||||
hn_new_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
|
||||
hn_overwrite = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
|
||||
with gr.Row():
|
||||
hn_create = gr.Button(value="Create hypernetwork", variant='secondary')
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training parameters</h2>')
|
||||
hn_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
|
||||
with FormRow():
|
||||
hn_name = gr.Dropdown(label='Hypernetwork', choices=sorted(shared.hypernetworks))
|
||||
create_refresh_button(hn_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
|
||||
with gr.Column():
|
||||
hn_new_name = gr.Textbox(label="Name")
|
||||
hn_new_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
|
||||
hn_new_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
|
||||
with gr.Row():
|
||||
hn_new_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
|
||||
hn_new_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
|
||||
hn_new_add_layer_norm = gr.Checkbox(label="Add layer normalization")
|
||||
hn_new_use_dropout = gr.Checkbox(label="Use dropout")
|
||||
hn_new_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
|
||||
hn_overwrite = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
|
||||
with gr.Row():
|
||||
hn_create = gr.Button(value="Create hypernetwork", variant='secondary')
|
||||
hn_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
hn_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
|
||||
hn_batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
||||
hn_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
|
||||
hn_steps = gr.Number(label='Max steps', value=1000, precision=0)
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training parameters</h2>')
|
||||
hn_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
|
||||
with FormRow():
|
||||
hn_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
hn_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
|
||||
hn_batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
||||
hn_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
|
||||
hn_steps = gr.Number(label='Max steps', value=1000, precision=0)
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training images</h2>')
|
||||
hn_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
with FormRow():
|
||||
hn_varsize = gr.Checkbox(label="Do not resize images", value=False)
|
||||
hn_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
|
||||
hn_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
|
||||
hn_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training images</h2>')
|
||||
hn_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
with FormRow():
|
||||
hn_varsize = gr.Checkbox(label="Do not resize images", value=False)
|
||||
hn_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
|
||||
hn_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
|
||||
hn_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Dataset processing</h2>')
|
||||
with FormRow():
|
||||
hn_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(hn_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
||||
hn_shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
|
||||
hn_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
|
||||
hn_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Dataset processing</h2>')
|
||||
with FormRow():
|
||||
hn_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(hn_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
||||
hn_shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
|
||||
hn_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
|
||||
hn_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training outputs</h2>')
|
||||
with FormRow():
|
||||
hn_create_every = gr.Number(label='Create interim images', value=500, precision=0)
|
||||
hn_save_every = gr.Number(label='Create interim hypernetworks', value=500, precision=0)
|
||||
hn_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
|
||||
hn_log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value=f"{os.path.join('cmd_opts.data_dir', 'train/log/embeddings')}")
|
||||
|
||||
with gr.Box():
|
||||
gr.HTML('<h2>Training outputs</h2>')
|
||||
with FormRow():
|
||||
hn_create_every = gr.Number(label='Create interim images', value=500, precision=0)
|
||||
hn_save_every = gr.Number(label='Create interim hypernetworks', value=500, precision=0)
|
||||
hn_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
|
||||
hn_log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value=f"{os.path.join('cmd_opts.data_dir', 'train/log/embeddings')}")
|
||||
hn_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
||||
|
||||
hn_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
||||
hn_create.click(
|
||||
fn=modules.hypernetworks.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
hn_new_name,
|
||||
hn_new_sizes,
|
||||
hn_overwrite,
|
||||
hn_new_layer_structure,
|
||||
hn_new_activation_func,
|
||||
hn_new_initialization_option,
|
||||
hn_new_add_layer_norm,
|
||||
hn_new_use_dropout,
|
||||
hn_new_dropout_structure
|
||||
],
|
||||
outputs=[
|
||||
hn_name,
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
hn_create.click(
|
||||
fn=modules.hypernetworks.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
hn_new_name,
|
||||
hn_new_sizes,
|
||||
hn_overwrite,
|
||||
hn_new_layer_structure,
|
||||
hn_new_activation_func,
|
||||
hn_new_initialization_option,
|
||||
hn_new_add_layer_norm,
|
||||
hn_new_use_dropout,
|
||||
hn_new_dropout_structure
|
||||
],
|
||||
outputs=[
|
||||
hn_name,
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
hn_train.click(
|
||||
fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
||||
_js="startTrainMonitor",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
hn_name,
|
||||
hn_learn_rate,
|
||||
hn_batch_size,
|
||||
hn_gradient_step,
|
||||
hn_dataset_directory,
|
||||
hn_log_directory,
|
||||
hn_width,
|
||||
hn_height,
|
||||
hn_varsize,
|
||||
hn_steps,
|
||||
hn_clip_grad_mode,
|
||||
hn_clip_grad_value,
|
||||
hn_shuffle_tags,
|
||||
hn_tag_drop_out,
|
||||
hn_latent_sampling_method,
|
||||
hn_use_weight,
|
||||
hn_create_every,
|
||||
hn_save_every,
|
||||
hn_template,
|
||||
hn_preview_from_txt2img,
|
||||
*txt2img_preview_params,
|
||||
],
|
||||
outputs=[
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
hn_train.click(
|
||||
fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
||||
_js="startTrainMonitor",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
hn_name,
|
||||
hn_learn_rate,
|
||||
hn_batch_size,
|
||||
hn_gradient_step,
|
||||
hn_dataset_directory,
|
||||
hn_log_directory,
|
||||
hn_width,
|
||||
hn_height,
|
||||
hn_varsize,
|
||||
hn_steps,
|
||||
hn_clip_grad_mode,
|
||||
hn_clip_grad_value,
|
||||
hn_shuffle_tags,
|
||||
hn_tag_drop_out,
|
||||
hn_latent_sampling_method,
|
||||
hn_use_weight,
|
||||
hn_create_every,
|
||||
hn_save_every,
|
||||
hn_template,
|
||||
hn_preview_from_txt2img,
|
||||
*txt2img_preview_params,
|
||||
],
|
||||
outputs=[
|
||||
train_output,
|
||||
train_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
|
||||
script_callbacks.ui_train_tabs_callback(params)
|
||||
|
||||
Reference in New Issue
Block a user