diff --git a/modules/ui.py b/modules/ui.py index b4852db2..e9a10680 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1105,131 +1105,153 @@ def create_ui(): return interp_descriptions[value] with gr.Blocks(analytics_enabled=False) as modelmerger_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='compact'): - interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") - - with FormRow(elem_id="modelmerger_models"): - primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") - create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A") - - secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") - create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B") - - tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") - create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C") - - custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name") - 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") - interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") - interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) - - with FormRow(): - checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format") - save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half") - - with FormRow(): - with gr.Column(): - 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") - - with gr.Column(): - with FormRow(): - bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae") - create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae") - - with FormRow(): - discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights") - + gr.Row(elem_id="modelmerger_2img_prompt_image", visible=False) + with gr.Row(): + with gr.Column(elem_id="modelmerger_2img_results"): with gr.Row(): - modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary') - gr.Row(elem_id="modelmerger_splitter") - with gr.Column(variant='compact', elem_id="modelmerger_results_container"): - with gr.Group(elem_id="modelmerger_results_panel"): modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False) + gr.Row(elem_id="modelmerger_2img_splitter") + with gr.Column(variant='panel', elem_id="modelmerger_2img_settings"): + modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary') + with gr.Column(elem_id="modelmerger_2img_settings_scroll"): + interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") + + with FormRow(elem_id="modelmerger_models"): + with gr.Box(): + with gr.Row(elem_id="modelmerger_primary_row-collapse-all"): + primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") + create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A") + with gr.Box(): + with gr.Row(elem_id="modelmerger_secondary_row-collapse-all"): + secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") + create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B") + with gr.Box(): + with gr.Row(elem_id="modelmerger_tertiary_row-collapse-all"): + tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") + create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C") + + custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name") + 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") + interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") + interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) + + with FormRow(): + checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format") + save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half") + + with FormRow(): + with gr.Column(): + 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") + with gr.Column(): + with gr.Box(): + with gr.Row(elem_id="modelmerger_bake_in_vae_row-collapse-all"): + bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae") + create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae") + + with FormRow(): + discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights") + with gr.Blocks(analytics_enabled=False) as train_interface: - with gr.Row().style(equal_height=False): - gr.HTML(value="

See wiki for detailed explanation.

") - - with gr.Row(variant="compact").style(equal_height=False): - with gr.Tabs(elem_id="train_tabs"): + #with gr.Row(elem_id="textual_inversion_wiki"): + # gr.HTML(value="

See wiki for detailed explanation.

") + gr.Row(elem_id="ti_2img_prompt_image", visible=False) + with gr.Row(): + with gr.Column(elem_id="ti_2img_results"): + 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="") + gr.Row(elem_id="ti_2img_splitter") + with gr.Tabs(elem_id="train_tabs_2img_settings"): with gr.Tab(label="Create embedding"): - new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") - initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") - nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") - overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") + #create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") + with gr.Column(elem_id="embedding_2img_settings_scroll"): + new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") + initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") + nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") + overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + with gr.Column(): + create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") + + with gr.Tab(label="Create hypernetwork"): - new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") - new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") - 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") - 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") - 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") - new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") - new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") - 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'") - overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") + #create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") + with gr.Column(elem_id="hypernetwork_2img_settings_scroll"): + new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") + new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") + 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") + 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") + 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") + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") + 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'") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") - with gr.Column(): - create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") + with gr.Column(): + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") with gr.Tab(label="Preprocess images"): - process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") - process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") - process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") - process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") + # 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") + with gr.Column(elem_id="preprocess_2img_settings_scroll"): + process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") + process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") + process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") + process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") - with gr.Row(): - process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") - process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") - process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") - process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop") - process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") - - with gr.Row(visible=False) as process_split_extra_row: - 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") - 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") - - with gr.Row(visible=False) as process_focal_crop_row: - 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") - 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(): - 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="") + process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") + process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") + process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") + process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop") + process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") - with gr.Column(): + with gr.Row(visible=False) as process_split_extra_row: + 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") + 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") + + with gr.Row(visible=False) as process_focal_crop_row: + 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") + 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="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") - 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="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") + 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, diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index 83d81672..592e1862 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -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"): diff --git a/style.css b/style.css index 30969407..12b37d42 100644 --- a/style.css +++ b/style.css @@ -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 */