diff --git a/multitalk/nodes.py b/multitalk/nodes.py
index b4fcb04..e4ee204 100644
--- a/multitalk/nodes.py
+++ b/multitalk/nodes.py
@@ -12,9 +12,7 @@ class MultiTalkModelLoader:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
-
-            "base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}),
+                "model": (folder_paths.get_filename_list("unet_gguf") + folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
             },
         }
 
@@ -23,18 +21,16 @@ class MultiTalkModelLoader:
     FUNCTION = "loadmodel"
     CATEGORY = "WanVideoWrapper"
 
-    def loadmodel(self, model, base_precision):
+    def loadmodel(self, model, base_precision=None):
         from .multitalk import AudioProjModel
-
-        device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
-        base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
         
         model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
-        sd = load_torch_file(model_path, device=offload_device, safe_load=True)
-
-        audio_proj_keys = [k for k in sd.keys() if "audio_proj" in k]
-        audio_proj_sd = {k.replace("audio_proj.", ""): sd.pop(k) for k in audio_proj_keys}
+        if model_path.endswith(".gguf"):
+            from diffusers.models.model_loading_utils import load_gguf_checkpoint
+            sd = load_gguf_checkpoint(model_path)
+        else:
+            sd = load_torch_file(model_path, device=offload_device, safe_load=True)
 
         audio_window=5
         intermediate_dim=512
@@ -52,18 +48,14 @@ class MultiTalkModelLoader:
                     context_tokens=context_tokens,
                     norm_output_audio=norm_output_audio,
             )
-        #fantasytalking_proj_model.load_state_dict(sd, strict=False)
-
-        for name, param in multitalk_proj_model.named_parameters():
-            set_module_tensor_to_device(multitalk_proj_model, name, device=offload_device, dtype=base_dtype, value=audio_proj_sd[name])
 
         multitalk = {
             "proj_model": multitalk_proj_model,
             "sd": sd,
+            "is_gguf": model_path.endswith(".gguf")
         }
 
         return (multitalk,)
-    
 
 def loudness_norm(audio_array, sr=16000, lufs=-23):
     try:
diff --git a/nodes_model_loading.py b/nodes_model_loading.py
index 9cc68f3..1c7ce4c 100644
--- a/nodes_model_loading.py
+++ b/nodes_model_loading.py
@@ -1012,6 +1012,8 @@ class WanVideoModelLoader:
             sd.update(ip_adapter_sd)
 
         if multitalk_model is not None:
+            if multitalk_model["is_gguf"] and not gguf:
+                raise ValueError("Multitalk/InfiniteTalk model is a GGUF model, main model also has to be a GGUF model.")
             # init audio module
             from .multitalk.multitalk import SingleStreamMultiAttention
             from .wanvideo.modules.model import WanRMSNorm, WanLayerNorm
@@ -1032,9 +1034,8 @@ class WanVideoModelLoader:
                     )
                 block.norm_x = WanLayerNorm(dim, transformer.eps, elementwise_affine=True) if norm_input_visual else nn.Identity()
             log.info("MultiTalk model detected, patching model...")
-            
+            transformer.audio_proj = multitalk_model["proj_model"]
             sd.update(multitalk_model["sd"])
-
         
         # Additional cond latents
         if "add_conv_in.weight" in sd:
@@ -1236,9 +1237,6 @@ class WanVideoModelLoader:
 
         del sd
 
-        if multitalk_model is not None:
-            transformer.audio_proj = multitalk_model["proj_model"]
-
         if vram_management_args is not None:
             if gguf:
                 raise ValueError("GGUF models don't support vram management")