(SD 2.X) Embedding Example Settings for a Dataset of 30 High Quality Captioned Images, use a photo of [name], [filewords] (on line) as filewords for training a person and a photo of [filwords], [name] for style!

[Flonixcorn]

Modified modules/ui.py to have best defaults used for great making of 2.X embeddings, used by many great ambedding makes, SoCalGuitarist, Conflictx

Raw part of the defaults:

            with gr.Tab(label="Train"):
                gr.HTML(value="<p style='margin-bottom: 0.7em'>Example Settings for a Dataset of 30 High Quality Captioned Images, use a photo of [name], [filewords] (on line) as filewords for training a person and a photo of [filwords], [name] for style! 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 gr.Row():
                    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.Row():
                    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 gr.Row():
                    embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.004", 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")

                batch_size = gr.Number(label='Batch size', value=15, precision=0, elem_id="train_batch_size")
                gradient_step = gr.Number(label='Gradient accumulation steps', value=2, 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")
                template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
                training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=768, elem_id="train_training_width")
                training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=768, elem_id="train_training_height")
                steps = gr.Number(label='Max steps', value=200, precision=0, elem_id="train_steps")
                create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1, 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=2, precision=0, elem_id="train_save_embedding_every")
                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")
                with gr.Row():
                    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")
                with gr.Row():
                    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():
                    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")
                    train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")

Example Settings for a Dataset of 30 High Quality Captioned Images, use a photo of [name], [filewords] (on line) as filewords for training a person and a photo of [filwords], [name] for style!

Youd also need to change the default style_filewords and subject_filewords to be the oneliners that are there at top!

making an embed is 75% having a good dataset!!!

[ProGamerGov]

A batch size of 15 is pretty big, what kind of Graphics cards are people running these settings on?

[DamonianoStudios]

That would have to be a 3090, as I go over 16gb of Vram at 15 batch size, 24 batch size puts me at 23.7

[Flonixcorn]

yea, 15 is way too big, 2 batch size and 2 ga is best