Gradient clipping for textual embedding

        })

def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    assert embedding_name, 'embedding not selected'

    shared.state.textinfo = "Initializing textual inversion training..."

    if ititial_step > steps:

        return embedding, filename

    clip_grad_mode_value = clip_grad_mode == "value"

    clip_grad_mode_norm = clip_grad_mode == "norm"

    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)

            optimizer.zero_grad()

            loss.backward()

            if clip_grad_mode_value:

                torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value)

            elif clip_grad_mode_norm:

                torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value)

            optimizer.step()

                training_width,

                training_height,

                steps,

                clip_grad_mode,

                clip_grad_value,

                create_image_every,

                save_embedding_every,

                template_file,