diff --git a/modules/model_t5.py b/modules/model_t5.py
index 1a1e40382..277ec92f4 100644
--- a/modules/model_t5.py
+++ b/modules/model_t5.py
@@ -1,3 +1,4 @@
+import torch
 import transformers
 
 
@@ -37,12 +38,24 @@ def load_t5(t5=None, cache_dir=None):
             cache_dir=cache_dir,
             torch_dtype=devices.dtype,
         )
+    elif 'qint8' in t5.lower():
+        modelloader.hf_login()
+        from installer import install
+        install('optimum-quanto', quiet=True)
+        from modules.sd_models_compile import optimum_quanto_model
+        t5 = transformers.T5EncoderModel.from_pretrained(
+            repo_id,
+            subfolder='text_encoder_3',
+            cache_dir=cache_dir,
+            torch_dtype=devices.dtype,
+        )
+        t5 = optimum_quanto_model(t5, weights="qint8")
     elif 'int8' in t5.lower():
         modelloader.hf_login()
         from installer import install
         install('nncf==2.7.0', quiet=True)
         from modules.sd_models_compile import nncf_compress_model
-        from modules.sd_hijack import NNCF_T5DenseGatedActDense # T5DenseGatedActDense uses fp32
+        from modules.sd_hijack import NNCF_T5DenseGatedActDense
         t5 = transformers.T5EncoderModel.from_pretrained(
             repo_id,
             subfolder='text_encoder_3',
@@ -51,7 +64,8 @@ def load_t5(t5=None, cache_dir=None):
         )
         for i in range(len(t5.encoder.block)):
             t5.encoder.block[i].layer[1].DenseReluDense = NNCF_T5DenseGatedActDense(
-                t5.encoder.block[i].layer[1].DenseReluDense
+                t5.encoder.block[i].layer[1].DenseReluDense,
+                dtype=torch.float32 if devices.dtype != torch.bfloat16 else torch.bfloat16
             )
         t5 = nncf_compress_model(t5)
     else:
diff --git a/modules/sd_models_compile.py b/modules/sd_models_compile.py
index bf6e61804..5bf1158cb 100644
--- a/modules/sd_models_compile.py
+++ b/modules/sd_models_compile.py
@@ -150,13 +150,14 @@ def nncf_compress_weights(sd_model):
         shared.log.warning(f"NNCF Compress Weights: error: {e}")
     return sd_model
 
-def optimum_quanto_model(model):
+def optimum_quanto_model(model, weights=None):
     from optimum import quanto
+    weights = getattr(quanto, weights) if weights is not None else getattr(quanto, shared.opts.optimum_quanto_weights_type)
     model.eval()
     backup_embeddings = None
     if hasattr(model, "get_input_embeddings"):
         backup_embeddings = copy.deepcopy(model.get_input_embeddings())
-    quanto.quantize(model, weights=getattr(quanto, shared.opts.optimum_quanto_weights_type))
+    quanto.quantize(model, weights=weights)
     quanto.freeze(model)
     if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
         model.set_input_embeddings(backup_embeddings)
diff --git a/modules/shared.py b/modules/shared.py
index 3b4a680ff..784e90524 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -391,7 +391,7 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
     "sd_unet": OptionInfo("None", "UNET model", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list),
-    "sd_text_encoder": OptionInfo('None', "Text encoder model", gr.Dropdown, lambda: {"choices": ['None', 'T5 FP4', 'T5 FP8', 'T5 INT8', 'T5 FP16']}),
+    "sd_text_encoder": OptionInfo('None', "Text encoder model", gr.Dropdown, lambda: {"choices": ['None', 'T5 FP4', 'T5 FP8', 'T5 INT8', 'T5 QINT8', 'T5 FP16']}),
     "sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_autoload": OptionInfo(True, "Model autoload on start"),
     "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results"),