Add ability to choose using weighted loss or not

    shared.reload_hypernetworks()

def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

    from modules import images

    pin_memory = shared.opts.pin_memory

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:

        saved_params = dict(

                with devices.autocast():

                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

                    if use_weight:

                        w = batch.weight.to(devices.device, non_blocking=pin_memory)

                    if tag_drop_out != 0 or shuffle_tags:

                        shared.sd_model.cond_stage_model.to(devices.device)

                        shared.sd_model.cond_stage_model.to(devices.cpu)

                    else:

                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)

                    if use_weight:

                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step

                        del w

                    else:

                        loss = shared.sd_model.forward(x, c)[0] / gradient_step

                    del x

                    del c

class PersonalizedBase(Dataset):

    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False):

    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):

        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

        self.placeholder_token = placeholder_token

                image = Image.open(path)

                #Currently does not work for single color transparency

                #We would need to read image.info['transparency'] for that

                if 'A' in image.getbands():

                if use_weight and 'A' in image.getbands():

                    alpha_channel = image.getchannel('A')

                image = image.convert('RGB')

                if not varsize:

                    latent_sampling_method = "once"

            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)

            if alpha_channel is not None:

            if use_weight and alpha_channel is not None:

                channels, *latent_size = latent_sample.shape

                weight_img = alpha_channel.resize(latent_size)

                npweight = np.array(weight_img).astype(np.float32)

                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.

                weight -= weight.min()

                weight /= weight.mean()

            else:

            elif use_weight:

                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later

                weight = torch.ones([channels] + latent_size)

            else:

                weight = None

            if latent_sampling_method == "random":

                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)

        self.cond_text = [entry.cond_text for entry in data]

        self.cond = [entry.cond for entry in data]

        self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)

        if all(entry.weight is not None for entry in data):

            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)

        else:

            self.weight = None

        #self.emb_index = [entry.emb_index for entry in data]

        #print(self.latent_sample.device)

        assert log_directory, "Log directory is empty"

def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, 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(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, 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):

    save_embedding_every = save_embedding_every or 0

    create_image_every = create_image_every or 0

    template_file = textual_inversion_templates.get(template_filename, None)

    pin_memory = shared.opts.pin_memory

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:

        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})

                with devices.autocast():

                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

                    if use_weight:

                        w = batch.weight.to(devices.device, non_blocking=pin_memory)

                    c = shared.sd_model.cond_stage_model(batch.cond_text)

                    else:

                        cond = c

                    if use_weight:

                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step

                        del w

                    else:

                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step

                    del x

                    _loss_step += loss.item()

                        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")

                    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,

                tag_drop_out,

                latent_sampling_method,

                use_weight,

                create_image_every,

                save_embedding_every,

                template_file,

                shuffle_tags,

                tag_drop_out,

                latent_sampling_method,

                use_weight,

                create_image_every,

                save_embedding_every,

                template_file,