Add a way to load the diffusion model in fp8 with UNETLoader node.

comfy/sd.py

@@ -567,7 +567,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers or regular format
+def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular format
 
     #Allow loading unets from checkpoint files
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
@@ -576,7 +576,6 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         sd = temp_sd
 
     parameters = comfy.utils.calculate_parameters(sd)
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -603,7 +602,11 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    else:
+        unet_dtype = dtype
+
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model = model_config.get_model(new_sd, "")
@@ -614,9 +617,9 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
-def load_unet(unet_path):
+def load_unet(unet_path, dtype=None):
     sd = comfy.utils.load_torch_file(unet_path)
-    model = load_unet_state_dict(sd)
+    model = load_unet_state_dict(sd, dtype=dtype)
     if model is None:
         logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))

nodes.py

@@ -818,15 +818,17 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
 
     CATEGORY = "advanced/loaders"
 
-    def load_unet(self, unet_name):
+    def load_unet(self, unet_name, weight_dtype):
+        weight_dtype = {"default":None, "fp8_e4m3fn":torch.float8_e4m3fn, "fp8_e5m2":torch.float8_e4m3fn}[weight_dtype]
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = comfy.sd.load_unet(unet_path)
+        model = comfy.sd.load_unet(unet_path, dtype=weight_dtype)
         return (model,)
 
 class CLIPLoader: