changed embedding accepted shape detection to use existing code and support...

        return embedding

    def get_expected_shape(self):

        expected_shape = -1 # initialize with unknown

        idx = torch.tensor(0).to(shared.device)

        if expected_shape == -1:

            try: # matches sd15 signature

                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)

                expected_shape = first_embedding.shape[0]

            except:

                pass

        if expected_shape == -1:

            try: # matches sd20 signature

                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)

                expected_shape = first_embedding.shape[0]

            except:

                pass

        if expected_shape == -1:

            print('Could not determine expected embeddings shape from model')

        return expected_shape

        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)

        return vec.shape[1]

    def load_textual_inversion_embeddings(self, force_reload = False):

        mt = os.path.getmtime(self.embeddings_dir)

        def process_file(path, filename):

            name = os.path.splitext(filename)[0]

            data = []

            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:

                embed_image = Image.open(path)

                if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:

            embedding.vectors = vec.shape[0]

            embedding.shape = vec.shape[-1]

            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):

            if self.expected_shape == -1 or self.expected_shape == embedding.shape:

                self.register_embedding(embedding, shared.sd_model)

            else:

                self.skipped_embeddings.append(name)

                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))

        for fn in os.listdir(self.embeddings_dir):

            try:

                print(traceback.format_exc(), file=sys.stderr)

                continue

        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))

        if (len(self.skipped_embeddings) > 0):

            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))

        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")

        if len(self.skipped_embeddings) > 0:

            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")

    def find_embedding_at_position(self, tokens, offset):

        token = tokens[offset]