From 2c11e9009ea18bab4ce2963d44db0c6fd3227370 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 24 Jul 2023 11:57:59 +0300
Subject: repair --medvram for SD2.x too after SDXL update

---
 modules/lowvram.py             | 7 ++++---
 modules/sd_hijack_open_clip.py | 2 +-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/lowvram.py b/modules/lowvram.py
index 6bbc11eb..3f830664 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -90,8 +90,12 @@ def setup_for_low_vram(sd_model, use_medvram):
         sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
     elif is_sd2:
         sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+        sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+        parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
     else:
         sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
@@ -101,9 +105,6 @@ def setup_for_low_vram(sd_model, use_medvram):
     if sd_model.embedder:
         sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
 
-    if hasattr(sd_model, 'cond_stage_model'):
-        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)
     else:
diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py
index bb0b96c7..25c5e983 100644
--- a/modules/sd_hijack_open_clip.py
+++ b/modules/sd_hijack_open_clip.py
@@ -32,7 +32,7 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
     def encode_embedding_init_text(self, init_text, nvpt):
         ids = tokenizer.encode(init_text)
         ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
-        embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
 
         return embedded
 
-- 
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