Merge pull request #237 from Kosinkadink/develop

Fixed device mismatch issue for ControlNet++, SparseCtrl, and SVDControlNet

Author: Kosinkadink

adv_control/control.py

@@ -326,7 +326,7 @@ def get_control_advanced(self, x_noisy, t, cond, batched_number, transformer_opt
         # uses 'y' in new ComfyUI update
         y = cond.get('y', None)
         if y is not None:
-            y = y.to(dtype)
+            y = comfy.model_base.convert_tensor(y, dtype, x_noisy.device)
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
         # concat c_concat if exists (should exist for SVD), doubling channels to 8
@@ -459,7 +459,7 @@ def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int, tr
         context = cond['c_crossattn']
         y = cond.get('y', None)
         if y is not None:
-            y = y.to(dtype)
+            y = comfy.model_base.convert_tensor(y, dtype, x_noisy.device)
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 

adv_control/control_plusplus.py

@@ -18,6 +18,7 @@
 #from comfy.t2i_adapter.adapter import ResidualAttentionBlock
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ops
+import comfy.model_base
 import comfy.model_management
 import comfy.model_detection
 import comfy.utils
@@ -339,7 +340,7 @@ def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number, transfo
         context = cond.get('crossattn_controlnet', cond['c_crossattn'])
         y = cond.get('y', None)
         if y is not None:
-            y = y.to(dtype)
+            y = comfy.model_base.convert_tensor(y, dtype, x_noisy.device)
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 

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 = "1.5.5"
+version = "1.5.6"
 license = { file = "LICENSE" }
 dependencies = []