Bump version to 1.5.8

Fix dtype mismatch for SparseCtrl and SVD-ControlNet models

adv_control/control.py

@@ -811,6 +811,7 @@ def load_sparsectrl(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, tim
         controlnet_config["operations"] = manual_cast_clean_groupnorm
     else:
         controlnet_config["operations"] = disable_weight_init_clean_groupnorm
+    controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     # get proper hint channels
     if use_simplified_conditioning_embedding:
@@ -845,6 +846,10 @@ def load_sparsectrl(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, tim
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
     if len(missing) > 0 or len(unexpected) > 0:
         logger.info(f"SparseCtrl ControlNet: {missing}, {unexpected}")
+    # cast control_model to the intended dtype; load_state_dict can leave weights
+    # in their on-disk dtype (e.g. comfy's lazy/zero-copy state dict loading), which
+    # would otherwise mismatch the activations at runtime
+    control_model = control_model.to(unet_dtype)
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -944,6 +949,7 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
         controlnet_config["operations"] = comfy.ops.manual_cast
+    controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = SVDControlNet(**controlnet_config)
@@ -972,6 +978,10 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
     if len(missing) > 0 or len(unexpected) > 0:
         logger.info(f"SVD-ControlNet: {missing}, {unexpected}")
+    # cast control_model to the intended dtype; load_state_dict can leave weights
+    # in their on-disk dtype (e.g. comfy's lazy/zero-copy state dict loading), which
+    # would otherwise mismatch the activations at runtime
+    control_model = control_model.to(unet_dtype)
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]

adv_control/utils.py

@@ -338,8 +338,10 @@ class AbstractPreprocWrapper:
 class disable_weight_init_clean_groupnorm(comfy.ops.disable_weight_init):
     class GroupNorm(comfy.ops.disable_weight_init.GroupNorm):
         def forward_comfy_cast_weights(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+            comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
         def forward(self, input):
             if self.comfy_cast_weights:

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-advanced-controlnet"
 description = "Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks."
-version = "1.5.6"
+version = "1.5.8"
 license = { file = "LICENSE" }
 dependencies = []
 
@@ -13,3 +13,4 @@ Repository = "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet"
 PublisherId = "kosinkadink"
 DisplayName = "ComfyUI-Advanced-ControlNet"
 Icon = ""
+requires-comfyui = ">=0.3.68"