mirror of
https://github.com/anapnoe/stable-diffusion-webui-ux.git
synced 2026-09-19 09:15:20 +02:00
Checkpoint Merger + Train (untested)
This commit is contained in:
anapnoe
+188
-153
@@ -1105,131 +1105,153 @@ def create_ui():
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return interp_descriptions[value]
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with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
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with gr.Row().style(equal_height=False):
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with gr.Column(variant='compact'):
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interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
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with FormRow(elem_id="modelmerger_models"):
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primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
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create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
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secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
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create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
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tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
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create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
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custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
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interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
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interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
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interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
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with FormRow():
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checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
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save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
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with FormRow():
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with gr.Column():
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config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
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with gr.Column():
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with FormRow():
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bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
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create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
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with FormRow():
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discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
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gr.Row(elem_id="modelmerger_2img_prompt_image", visible=False)
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with gr.Row():
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with gr.Column(elem_id="modelmerger_2img_results"):
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with gr.Row():
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modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
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gr.Row(elem_id="modelmerger_splitter")
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with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
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with gr.Group(elem_id="modelmerger_results_panel"):
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modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
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gr.Row(elem_id="modelmerger_2img_splitter")
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with gr.Column(variant='panel', elem_id="modelmerger_2img_settings"):
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modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
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with gr.Column(elem_id="modelmerger_2img_settings_scroll"):
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interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
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with FormRow(elem_id="modelmerger_models"):
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with gr.Box():
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with gr.Row(elem_id="modelmerger_primary_row-collapse-all"):
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primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
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create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
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with gr.Box():
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with gr.Row(elem_id="modelmerger_secondary_row-collapse-all"):
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secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
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create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
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with gr.Box():
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with gr.Row(elem_id="modelmerger_tertiary_row-collapse-all"):
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tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
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create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
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custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
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interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
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interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
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interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
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with FormRow():
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checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
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save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
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with FormRow():
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with gr.Column():
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config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
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with gr.Column():
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with gr.Box():
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with gr.Row(elem_id="modelmerger_bake_in_vae_row-collapse-all"):
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bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
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create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
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with FormRow():
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discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
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with gr.Blocks(analytics_enabled=False) as train_interface:
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with gr.Row().style(equal_height=False):
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gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
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with gr.Row(variant="compact").style(equal_height=False):
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with gr.Tabs(elem_id="train_tabs"):
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#with gr.Row(elem_id="textual_inversion_wiki"):
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# gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
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gr.Row(elem_id="ti_2img_prompt_image", visible=False)
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with gr.Row():
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with gr.Column(elem_id="ti_2img_results"):
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with gr.Column(elem_id='ti_gallery_container'):
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ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
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ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
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ti_progress = gr.HTML(elem_id="ti_progress", value="")
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ti_outcome = gr.HTML(elem_id="ti_error", value="")
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gr.Row(elem_id="ti_2img_splitter")
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with gr.Tabs(elem_id="train_tabs_2img_settings"):
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with gr.Tab(label="Create embedding"):
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new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
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initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
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nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
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overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
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#create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
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with gr.Column(elem_id="embedding_2img_settings_scroll"):
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new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
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initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
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nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
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overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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with gr.Column():
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create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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with gr.Column():
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create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
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with gr.Tab(label="Create hypernetwork"):
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new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
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new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
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new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
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new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
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new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
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new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
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new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
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new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
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overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
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#create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
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with gr.Column(elem_id="hypernetwork_2img_settings_scroll"):
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new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
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new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
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new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
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new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
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new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
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new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
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new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
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new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
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overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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with gr.Column():
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create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
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with gr.Column():
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create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
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with gr.Tab(label="Preprocess images"):
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process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
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process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
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process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
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process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
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preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
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# with gr.Column():
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# with gr.Row():
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# interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
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# run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
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with gr.Column(elem_id="preprocess_2img_settings_scroll"):
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process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
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process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
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process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
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process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
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preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
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with gr.Row():
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process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
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process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
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process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
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process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
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process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
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process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
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with gr.Row(visible=False) as process_split_extra_row:
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process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
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process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
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with gr.Row(visible=False) as process_focal_crop_row:
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process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
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process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
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process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
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process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
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with gr.Column(visible=False) as process_multicrop_col:
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gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
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with gr.Row():
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process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
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process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
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with gr.Row():
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process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
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process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
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with gr.Row():
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process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
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process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
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process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
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process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
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process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
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process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
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process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
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with gr.Column():
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with gr.Row(visible=False) as process_split_extra_row:
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process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
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process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
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with gr.Row(visible=False) as process_focal_crop_row:
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process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
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process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
|
||||
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
|
||||
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
|
||||
|
||||
with gr.Column(visible=False) as process_multicrop_col:
|
||||
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
|
||||
with gr.Row():
|
||||
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
|
||||
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
|
||||
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
|
||||
with gr.Row():
|
||||
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
|
||||
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
|
||||
with gr.Row():
|
||||
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
|
||||
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
|
||||
|
||||
process_split.change(
|
||||
fn=lambda show: gr_show(show),
|
||||
@@ -1252,67 +1274,80 @@ def create_ui():
|
||||
def get_textual_inversion_template_names():
|
||||
return sorted([x for x in textual_inversion.textual_inversion_templates])
|
||||
|
||||
with gr.Tab(label="Train"):
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
|
||||
with FormRow():
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
create_refresh_button(train_embedding_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.Tab(label="Train"):
|
||||
# with gr.Row():
|
||||
# train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
|
||||
# interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
|
||||
# train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
|
||||
with gr.Column(elem_id="train_2img_settings_scroll"):
|
||||
gr.HTML(value="<p style='margin-bottom: 0'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
|
||||
with FormRow():
|
||||
with gr.Box():
|
||||
with gr.Row(elem_id="train_embedding_row-collapse-all"):
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
create_refresh_button(train_embedding_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.Box():
|
||||
with gr.Row(elem_id="train_hypernetwork_row-collapse-all"):
|
||||
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
|
||||
create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
|
||||
|
||||
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
|
||||
create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
|
||||
with FormRow():
|
||||
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
|
||||
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
|
||||
|
||||
with FormRow():
|
||||
with gr.Box():
|
||||
with gr.Row(elem_id="train_gradient_clipping_row-collapse-all"):
|
||||
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
clip_grad_value = gr.Textbox(label="Value", value="0.1")
|
||||
|
||||
with FormRow():
|
||||
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
|
||||
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
|
||||
|
||||
with FormRow():
|
||||
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
||||
clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
|
||||
with FormRow():
|
||||
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
|
||||
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
|
||||
|
||||
with FormRow():
|
||||
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
|
||||
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
|
||||
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
|
||||
with FormRow():
|
||||
with gr.Box():
|
||||
with gr.Row(elem_id="train_template_file_row-collapse-all"):
|
||||
template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refresh_train_template_file")
|
||||
|
||||
with FormRow():
|
||||
template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
|
||||
create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refresh_train_template_file")
|
||||
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
|
||||
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
|
||||
varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
|
||||
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
|
||||
|
||||
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
|
||||
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
|
||||
varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
|
||||
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
|
||||
with FormRow():
|
||||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
|
||||
|
||||
with FormRow():
|
||||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
|
||||
use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
|
||||
|
||||
use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
|
||||
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
|
||||
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
|
||||
|
||||
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
|
||||
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
|
||||
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
|
||||
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
|
||||
|
||||
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
|
||||
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
|
||||
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
|
||||
|
||||
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
|
||||
|
||||
with gr.Row():
|
||||
train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
|
||||
interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
|
||||
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
|
||||
with gr.Row():
|
||||
train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
|
||||
interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
|
||||
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
|
||||
|
||||
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
|
||||
|
||||
script_callbacks.ui_train_tabs_callback(params)
|
||||
|
||||
with gr.Column(elem_id='ti_gallery_container'):
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
# with gr.Column(elem_id='ti_gallery_container'):
|
||||
# ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
# ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
# ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
# ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
|
||||
create_embedding.click(
|
||||
fn=modules.textual_inversion.ui.create_embedding,
|
||||
|
||||
@@ -11,7 +11,7 @@ def create_ui():
|
||||
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras_2img", shared.opts.outdir_extras_samples)
|
||||
gr.Row(elem_id="extras_2img_splitter")
|
||||
with gr.Column(variant='panel', elem_id="extras_2img_settings"):
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
submit = gr.Button('Upscale', elem_id="extras_generate", variant='primary')
|
||||
with gr.Column(elem_id="extras_2img_settings_scroll"):
|
||||
with gr.Accordion("Image Source", elem_id="extras_accordion", open=True):
|
||||
with gr.Tabs(elem_id="mode_extras"):
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
:host{--main-bg-color:hsl(195deg 22% 8%);--primary-color:hsl(159deg 96% 55%);--input-bg-color:hsl(202deg 25% 12%);--input-border-color:hsl(200deg 24% 32%);--panel-bg-color:hsl(200deg 24% 20%);--panel-border-color:hsl(200deg 24% 32%);--panel-border-radius:4px;--subgroup-bg-color:hsl(195deg 22% 8%);--subgroup-input-bg-color:hsl(200deg 24% 8%);--subgroup-input-border-color:hsl(200deg 24% 32%);--subpanel-bg-color:hsl(202deg 25% 12%);--subpanel-border-color:hsl(200deg 24% 32%);--subpanel-border-radius:8px;--textarea-focus-color:hsl(152deg 3% 36%);--input-focus-color:hsl(159deg 96% 55%);--outside-gap-size:8px;--inside-padding-size:8px;--tool-button-size:34px;--tool-button-radius:16px;--generate-button-height:70px;--cancel-color:hsl(200deg 24% 32%);--max-padding:max(var(--outside-gap-size),var(--inside-padding-size));--icon-color:hsl(201deg 24% 77%);--icon-hover-color:hsl(195deg 22% 8%);--icon-size:22px;--nav-bg-color:hsl(194deg 24% 4%);--nav-color:hsl(201deg 24% 77%);--nav-hover-color:hsl(194deg 24% 4%);--input-color:hsl(159deg 96% 55%);--label-color:hsl(201deg 24% 77%);--subgroup-input-color:hsl(302deg 100% 100%);--placeholder-color:hsl(200deg 24% 32%);--text-color:hsl(201deg 24% 77%);}/*BREAKPOINT_CSS_CONTENT*/
|
||||
:host{--main-bg-color:hsl(195deg 22% 8%);--primary-color:hsl(159deg 96% 55%);--input-bg-color:hsl(202deg 25% 12%);--input-border-color:hsl(200deg 24% 32%);--panel-bg-color:hsl(200deg 24% 20%);--panel-border-color:hsl(200deg 24% 32%);--panel-border-radius:4px;--subgroup-bg-color:hsl(195deg 22% 8%);--subgroup-input-bg-color:hsl(200deg 24% 8%);--subgroup-input-border-color:hsl(200deg 24% 32%);--subpanel-bg-color:hsl(202deg 25% 12%);--subpanel-border-color:hsl(200deg 24% 32%);--subpanel-border-radius:8px;--textarea-focus-color:hsl(152deg 3% 36%);--input-focus-color:hsl(159deg 96% 55%);--outside-gap-size:8px;--inside-padding-size:8px;--tool-button-size:34px;--tool-button-radius:16px;--generate-button-height:70px;--cancel-color:hsl(200deg 24% 32%);--max-padding:max(var(--outside-gap-size),var(--inside-padding-size));--icon-color:hsl(159deg 96% 55%);--icon-hover-color:hsl(195deg 22% 8%);--icon-size:22px;--nav-bg-color:hsl(194deg 24% 4%);--nav-color:hsl(201deg 24% 77%);--nav-hover-color:hsl(194deg 24% 4%);--input-color:hsl(159deg 96% 55%);--label-color:hsl(201deg 24% 77%);--subgroup-input-color:hsl(302deg 100% 100%);--placeholder-color:hsl(200deg 24% 32%);--text-color:hsl(201deg 24% 77%);}/*BREAKPOINT_CSS_CONTENT*/
|
||||
|
||||
/*
|
||||
Theme Name: DarkUX
|
||||
@@ -96,7 +96,7 @@ body {
|
||||
|
||||
/* icons */
|
||||
|
||||
[id^="refresh_"],
|
||||
[id*="refresh_"],
|
||||
[id$="_clear_prompt"],
|
||||
[id$="2img_style_create"],
|
||||
[id$="2img_style_apply"],
|
||||
@@ -114,7 +114,7 @@ body {
|
||||
[id="inpaint_tab"],
|
||||
[id="txt2img_tab"]
|
||||
{
|
||||
background-color: var(--panel-bg-color);
|
||||
/*background-color: var(--panel-bg-color);*/
|
||||
position: relative;
|
||||
font-size: 0 !important;
|
||||
}
|
||||
@@ -122,7 +122,7 @@ body {
|
||||
|
||||
|
||||
|
||||
[id^="refresh_"]::before,
|
||||
[id*="refresh_"]::before,
|
||||
[id$="_clear_prompt"]::before,
|
||||
[id$="2img_style_create"]::before,
|
||||
[id$="2img_style_apply"]::before,
|
||||
@@ -156,7 +156,7 @@ body {
|
||||
background-color: var(--primary-color);
|
||||
}
|
||||
|
||||
[id^="refresh_"]:hover::before,
|
||||
[id*="refresh_"]:hover::before,
|
||||
[id$="_clear_prompt"]:hover::before,
|
||||
[id$="2img_style_create"]:hover::before,
|
||||
[id$="2img_style_apply"]:hover::before,
|
||||
@@ -177,7 +177,7 @@ body {
|
||||
background-color: var(--icon-hover-color);
|
||||
}
|
||||
|
||||
[id^="refresh_"]::before
|
||||
[id*="refresh_"]::before
|
||||
{
|
||||
-webkit-mask: url(./file=html/svg/refresh-line.svg) no-repeat 50% 50%;
|
||||
mask: url(./file=html/svg/refresh-line.svg) no-repeat 50% 50%;
|
||||
@@ -920,18 +920,11 @@ label.block span {
|
||||
}
|
||||
|
||||
|
||||
|
||||
#tab_ti .gr-button-tool,
|
||||
#settings .gr-button-tool {
|
||||
align-self: flex-end !important;
|
||||
margin-bottom: 10px;
|
||||
margin-right: 10px;
|
||||
min-width: 34px !important;
|
||||
max-width: 34px;
|
||||
height: 34px;
|
||||
#textual_inversion_wiki{
|
||||
position:absolute;
|
||||
transform: translateX(-50%) rotate(-90deg);
|
||||
}
|
||||
|
||||
|
||||
[id$="2img_res_switch_btn"]
|
||||
{
|
||||
height: auto;
|
||||
@@ -1156,7 +1149,8 @@ dark .gr-button-secondary:hover {
|
||||
|
||||
|
||||
|
||||
[id$="-collapse"]
|
||||
[id$="-collapse"],
|
||||
[id$="-collapse-one"] > div
|
||||
{
|
||||
border:0!important;
|
||||
margin:0!important;
|
||||
@@ -1178,6 +1172,10 @@ dark .gr-button-secondary:hover {
|
||||
|
||||
}
|
||||
|
||||
[id$="-collapse-all"] button
|
||||
{
|
||||
align-self: flex-end;
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -1220,7 +1218,8 @@ textarea:focus
|
||||
/* Generate Interrupt Skip */
|
||||
/***************************/
|
||||
|
||||
[id$="_generate"]
|
||||
[id$="_generate"],
|
||||
[id$="2img_settings"] > button:first-child
|
||||
{
|
||||
min-height: var(--generate-button-height);
|
||||
}
|
||||
@@ -1505,6 +1504,10 @@ canvas[key=mask] {
|
||||
background: linear-gradient(0deg, var(--main-bg-color), transparent 2%, transparent 98%, var(--main-bg-color) 100%);
|
||||
}
|
||||
|
||||
/***********/
|
||||
/* PngInfo */
|
||||
/***********/
|
||||
|
||||
[id$="png_2img_settings"]::before
|
||||
{
|
||||
display:none;
|
||||
@@ -1534,6 +1537,29 @@ canvas[key=mask] {
|
||||
[id$="png_2img_results"] > div:nth-child(3) {
|
||||
display:none;
|
||||
}
|
||||
|
||||
[id$="train_tabs_2img_settings"]::before {
|
||||
|
||||
display:none;
|
||||
}
|
||||
|
||||
[id$="train_tabs_2img_settings"] .tabitem {
|
||||
background-color:var(--input-bg-color) !important;
|
||||
border-radius: var(--panel-border-radius) !important;
|
||||
}
|
||||
|
||||
|
||||
[id$="train_tabs_2img_settings"] > div:first-child {
|
||||
margin-bottom: var(--outside-gap-size);
|
||||
}
|
||||
|
||||
[id$="train_tabs_2img_settings"] [id$="_2img_settings_scroll"] {
|
||||
padding-top: 0 !important;
|
||||
}
|
||||
|
||||
.p-2 {
|
||||
padding: var(--outside-gap-size);
|
||||
}
|
||||
/****************/
|
||||
/* Results View */
|
||||
/****************/
|
||||
@@ -2254,19 +2280,22 @@ ul.list-none {
|
||||
margin-top: 1em;
|
||||
overflow: visible;
|
||||
}
|
||||
|
||||
/*
|
||||
#modelmerger_models {
|
||||
gap: 0;
|
||||
}
|
||||
|
||||
#modelmerger_interp_description>p {
|
||||
margin: 0 !important;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
|
||||
#modelmerger_interp_description {
|
||||
margin: .35rem .75rem 1.23rem;
|
||||
}
|
||||
*/
|
||||
|
||||
#modelmerger_interp_description>p {
|
||||
margin: 0 !important;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
/*************/
|
||||
/* settings */
|
||||
|
||||
Reference in New Issue
Block a user