diff --git a/nodes.py b/nodes.py
index 1f3bcf9..8de8185 100644
--- a/nodes.py
+++ b/nodes.py
@@ -324,12 +324,18 @@ class WanVideoTextEncode:
             device_to = mm.get_torch_device()
         else:
             device_to = torch.device("cpu")
+
+        if encoder.quantization == "fp8_e4m3fn":
+            cast_dtype = torch.float8_e4m3fn
+        else:
+            cast_dtype = encoder.dtype
+
         params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
         for name, param in encoder.model.named_parameters():
-            dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else encoder.dtype
+            dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
             set_module_tensor_to_device(encoder.model, name, device=device_to, dtype=dtype_to_use, value=encoder.state_dict[name])
 
-        with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=dtype, enabled=encoder.quantization != 'disabled'):
+        with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=encoder.dtype, enabled=encoder.quantization != 'disabled'):
             # Encode positive if not loaded from cache
             if use_disk_cache and context is not None:
                 pass
@@ -453,12 +459,17 @@ class WanVideoTextEncodeSingle:
                 device_to = mm.get_torch_device()
             else:
                 device_to = torch.device("cpu")
+
+            if encoder.quantization == "fp8_e4m3fn":
+                cast_dtype = torch.float8_e4m3fn
+            else:
+                cast_dtype = encoder.dtype
             params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
             for name, param in encoder.model.named_parameters():
-                dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else encoder.dtype
+                dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
                 set_module_tensor_to_device(encoder.model, name, device=device_to, dtype=dtype_to_use, value=encoder.state_dict[name])
 
-            with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=dtype, enabled=encoder.quantization != 'disabled'):
+            with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=encoder.dtype, enabled=encoder.quantization != 'disabled'):
                 encoded = encoder([prompt], device_to)
 
             if force_offload:
@@ -1848,7 +1859,7 @@ class WanVideoSampler:
             shot_num = len(text_embeds["prompt_embeds"])
             shot_len = [latent_video_length//shot_num] * (shot_num-1)
             shot_len.append(latent_video_length-sum(shot_len))
-            log.info(f"Number of shots in prompt: {shot_num}, Shot token lengths: {shot_len}")
+            log.info(f"EchoShot - Number of shots in prompt: {shot_num}, Shot token lengths: {shot_len}")
 
         #region transformer settings
         #rope
diff --git a/nodes_model_loading.py b/nodes_model_loading.py
index e64da13..d0aed5e 100644
--- a/nodes_model_loading.py
+++ b/nodes_model_loading.py
@@ -1387,6 +1387,13 @@ class LoadWanVideoT5TextEncoder:
 
         model_path = folder_paths.get_full_path("text_encoders", model_name)
         sd = load_torch_file(model_path, safe_load=True)
+
+        if quantization == "disabled":
+            for k, v in sd.items():
+                if isinstance(v, torch.Tensor):
+                    if v.dtype == torch.float8_e4m3fn:
+                        quantization = "fp8_e4m3fn"
+                        break
         
         if "token_embedding.weight" not in sd and "shared.weight" not in sd:
             raise ValueError("Invalid T5 text encoder model, this node expects the 'umt5-xxl' model")