use shared function from processing for creating dummy mask when training inpainting model

    return image

def txt2img_image_conditioning(sd_model, x, width, height):

    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:

        # Dummy zero conditioning if we're not using inpainting model.

        # Still takes up a bit of memory, but no encoder call.

        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

    # The "masked-image" in this case will just be all zeros since the entire image is masked.

    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))

    # Add the fake full 1s mask to the first dimension.

    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)

    image_conditioning = image_conditioning.to(x.dtype)

    return image_conditioning

class StableDiffusionProcessing():

    """

    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

        self.iteration = 0

    def txt2img_image_conditioning(self, x, width=None, height=None):

        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:

            # Dummy zero conditioning if we're not using inpainting model.

            # Still takes up a bit of memory, but no encoder call.

            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

            return x.new_zeros(x.shape[0], 5, 1, 1)

        self.is_using_inpainting_conditioning = True

        height = height or self.height

        width = width or self.width

        # The "masked-image" in this case will just be all zeros since the entire image is masked.

        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))

        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}

        # Add the fake full 1s mask to the first dimension.

        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)

        image_conditioning = image_conditioning.to(x.dtype)

        return image_conditioning

        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)

    def depth2img_image_conditioning(self, source_image):

        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model

        assert log_directory, "Log directory is empty"

def create_dummy_mask(x, width=None, height=None):

    if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:

        # The "masked-image" in this case will just be all zeros since the entire image is masked.

        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

        image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning))

        # Add the fake full 1s mask to the first dimension.

        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)

        image_conditioning = image_conditioning.to(x.dtype)

    else:

        # Dummy zero conditioning if we're not using inpainting model.

        # Still takes up a bit of memory, but no encoder call.

        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

        image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

    return image_conditioning

def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, 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):

    save_embedding_every = save_embedding_every or 0

    create_image_every = create_image_every or 0

        else:

            print("No saved optimizer exists in checkpoint")

    scaler = torch.cuda.amp.GradScaler()

    batch_size = ds.batch_size

    forced_filename = "<none>"

    embedding_yet_to_be_embedded = False

    is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}

    img_c = None

    pbar = tqdm.tqdm(total=steps - initial_step)

    try:

        for i in range((steps-initial_step) * gradient_step):

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

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

                    if is_training_inpainting_model:

                        if img_c is None:

                        img_c = create_dummy_mask(c, training_width, training_height)

                            img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)

                        cond = {"c_concat": [img_c], "c_crossattn": [c]}

                    else:

                        cond = c

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

                    del x