From a8cbe50c9fa324ed887089e4333452ecc4355c92 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 14 Oct 2023 12:14:56 +0300
Subject: remove duplicated code

---
 extensions-builtin/Lora/networks.py | 31 ++-----------------------------
 1 file changed, 2 insertions(+), 29 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 12f70576..d5f0f9f1 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -15,7 +15,7 @@ import torch
 from typing import Union
 
 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
-from modules.textual_inversion.textual_inversion import Embedding
+import modules.textual_inversion.textual_inversion as textual_inversion
 
 from lora_logger import logger
 
@@ -210,34 +210,7 @@ def load_network(name, network_on_disk):
 
     embeddings = {}
     for emb_name, data in bundle_embeddings.items():
-        # textual inversion embeddings
-        if 'string_to_param' in data:
-            param_dict = data['string_to_param']
-            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
-            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
-            emb = next(iter(param_dict.items()))[1]
-            vec = emb.detach().to(devices.device, dtype=torch.float32)
-            shape = vec.shape[-1]
-            vectors = vec.shape[0]
-        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
-            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
-            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
-            vectors = data['clip_g'].shape[0]
-        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
-            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
-
-            emb = next(iter(data.values()))
-            if len(emb.shape) == 1:
-                emb = emb.unsqueeze(0)
-            vec = emb.detach().to(devices.device, dtype=torch.float32)
-            shape = vec.shape[-1]
-            vectors = vec.shape[0]
-        else:
-            raise Exception(f"Couldn't identify {emb_name} in lora: {name} as neither textual inversion embedding nor diffuser concept.")
-
-        embedding = Embedding(vec, emb_name)
-        embedding.vectors = vectors
-        embedding.shape = shape
+        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
         embedding.loaded = None
         embeddings[emb_name] = embedding
 
-- 
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