Update nodes.py

nodes/nodes.py

@@ -1651,100 +1651,3 @@ If no image is provided, mode is set to text-to-image
             except json.JSONDecodeError:
                 # If the response is not valid JSON, raise a different exception
                 raise Exception(f"Server error: {response.text}")
-            
-class LoadICLightUnet:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "model_path": (folder_paths.get_filename_list("unet"), )
-            } 
-        }
-
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "load"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-LoadICLightUnet: Loads an ICLightUnet model. (Experimental)
-WORK IN PROGRESS  
-Very hacky (but currently working) way to load the converted IC-Light model available here:  
-https://huggingface.co/Kijai/iclight-comfy/blob/main/iclight_fc_converted.safetensors  
-
-Used with InstructPixToPixConditioning -node
-
-"""
-
-    def load(self, model, model_path):
-        print("LoadICLightUnet: Checking LoadICLightUnet path")
-        model_full_path = folder_paths.get_full_path("unet", model_path)
-        if not os.path.exists(model_full_path):
-            raise Exception("Invalid model path")
-        else:
-            print("LoadICLightUnet: Loading LoadICLightUnet weights")
-            from comfy.utils import load_torch_file
-            model_clone = model.clone()
-
-            conv_layer = model_clone.model.diffusion_model.input_blocks[0][0]
-            print(f"Current number of input channels: {conv_layer.in_channels}")
-            
-            # Create a new Conv2d layer with 8 input channels
-            new_conv_layer = torch.nn.Conv2d(8, conv_layer.out_channels, kernel_size=conv_layer.kernel_size, stride=conv_layer.stride, padding=conv_layer.padding)
-            new_conv_layer.weight.zero_()
-            new_conv_layer.weight[:, :4, :, :].copy_(conv_layer.weight)
-            new_conv_layer.bias = conv_layer.bias
-            new_conv_layer = new_conv_layer.to(torch.float16)
-            conv_layer.conv_in = new_conv_layer
-            # Replace the old layer with the new one
-            model_clone.model.diffusion_model.input_blocks[0][0] = new_conv_layer
-            # Verify the change
-            print(f"New number of input channels: {model_clone.model.diffusion_model.input_blocks[0][0].in_channels}")
-            
-            # Monkey patch because I don't know what I'm doing
-            from comfy.model_base import IP2P
-            import types
-            base_model_instance = model_clone.model
-
-            # Dynamically add the extra_conds method from IP2P to the instance of BaseModel
-            def bound_extra_conds(self, **kwargs):
-                return IP2P.extra_conds(self, **kwargs)
-            base_model_instance.process_ip2p_image_in = lambda image: image
-            base_model_instance.extra_conds = types.MethodType(bound_extra_conds, base_model_instance)
-           
-            # Some Proper patching
-            new_state_dict = load_torch_file(model_full_path)
-            prefix_to_remove = 'model.'
-            new_keys_dict = {key[len(prefix_to_remove):]: new_state_dict[key] for key in new_state_dict if key.startswith(prefix_to_remove)}
-
-            print("LoadICLightUnet: Attempting to add patches with LoadICLightUnet weights")
-            try:
-                for key in new_keys_dict:
-                    model_clone.add_patches({key: (new_keys_dict[key],)}, 1.0, 1.0)
-            except:
-                raise Exception("Could not patch model")
-            print("LoadICLightUnet: Added LoadICLightUnet patches")
-
-            return (model_clone, )
-        
-
-# Change UNet
-
-# with torch.no_grad():
-#     new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
-#     new_conv_in.weight.zero_()
-#     new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
-#     new_conv_in.bias = unet.conv_in.bias
-#     unet.conv_in = new_conv_in
-
-# unet_original_forward = unet.forward
-
-
-# def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
-#     c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
-#     c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
-#     new_sample = torch.cat([sample, c_concat], dim=1)
-#     kwargs['cross_attention_kwargs'] = {}
-#     return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
-
-
-# unet.forward = hooked_unet_forward