diff --git a/modules/models/sd3/other_impls.py b/modules/models/sd3/other_impls.py
index 695d356cb..5492d5e11 100644
--- a/modules/models/sd3/other_impls.py
+++ b/modules/models/sd3/other_impls.py
@@ -449,7 +449,7 @@ class T5Attention(torch.nn.Module):
         else:
             mask = None
 
-        out = attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask.to(x.dtype) if mask is not None else None)
+        out = attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask.to(q.dtype) if mask is not None else None)
 
         return self.o(out), past_bias
 
@@ -486,15 +486,17 @@ class T5Stack(torch.nn.Module):
         self.block = torch.nn.ModuleList([T5Block(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device) for i in range(num_layers)])
         self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
 
-    def forward(self, input_ids, intermediate_output=None, final_layer_norm_intermediate=True):
+    def forward(self, x, intermediate_output=None, final_layer_norm_intermediate=True):
         intermediate = None
-        x = self.embed_tokens(input_ids).to(torch.float32)  # needs float32 or else T5 returns all zeroes
+        #x = self.embed_tokens(input_ids).to(torch.float32)  # needs float32 or else T5 returns all zeroes
+        # some T5XXL do not embed_token. use shared token instead like comfy
         past_bias = None
         for i, layer in enumerate(self.block):
             x, past_bias = layer(x, past_bias)
             if i == intermediate_output:
                 intermediate = x.clone()
         x = self.final_layer_norm(x)
+        x = torch.nan_to_num(x)
         if intermediate is not None and final_layer_norm_intermediate:
             intermediate = self.final_layer_norm(intermediate)
         return x, intermediate
@@ -505,13 +507,18 @@ class T5(torch.nn.Module):
         super().__init__()
         self.num_layers = config_dict["num_layers"]
         self.encoder = T5Stack(self.num_layers, config_dict["d_model"], config_dict["d_model"], config_dict["d_ff"], config_dict["num_heads"], config_dict["vocab_size"], dtype, device)
+        self.shared = torch.nn.Embedding(config_dict["vocab_size"], config_dict["d_model"])
         self.dtype = dtype
 
     def get_input_embeddings(self):
-        return self.encoder.embed_tokens
+        #return self.encoder.embed_tokens
+        return self.shared
 
     def set_input_embeddings(self, embeddings):
-        self.encoder.embed_tokens = embeddings
+        #self.encoder.embed_tokens = embeddings
+        self.shared = embeddings
 
-    def forward(self, *args, **kwargs):
-        return self.encoder(*args, **kwargs)
+    def forward(self, input_ids, *args, **kwargs):
+        x = self.shared(input_ids).float()
+        x = torch.nan_to_num(x)
+        return self.encoder(x, *args, **kwargs)
diff --git a/modules/models/sd3/sd3_cond.py b/modules/models/sd3/sd3_cond.py
index 6a43f569b..7058950a5 100644
--- a/modules/models/sd3/sd3_cond.py
+++ b/modules/models/sd3/sd3_cond.py
@@ -43,7 +43,7 @@ CLIPG_CONFIG = {
     "textual_inversion_key": "clip_g",
 }
 
-T5_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
+T5_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16_e4m3fn.safetensors"
 T5_CONFIG = {
     "d_ff": 10240,
     "d_model": 4096,
@@ -164,11 +164,11 @@ class SD3Cond(torch.nn.Module):
         self.tokenizer = SD3Tokenizer()
 
         with torch.no_grad():
-            self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
-            self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
+            self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype_inference)
+            self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype_inference, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
 
             if shared.opts.sd3_enable_t5:
-                self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
+                self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype_inference)
             else:
                 self.t5xxl = None
 
@@ -199,8 +199,8 @@ class SD3Cond(torch.nn.Module):
             with safetensors.safe_open(clip_l_file, framework="pt") as file:
                 self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
 
-        if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.embed_tokens.weight' not in state_dict:
-            t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
+        if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.k.weight' not in state_dict:
+            t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp8_e4m3fn.safetensors")
             with safetensors.safe_open(t5_file, framework="pt") as file:
                 self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)