diff --git a/adv_control/control.py b/adv_control/control.py
index f84e011..ee19d9c 100644
--- a/adv_control/control.py
+++ b/adv_control/control.py
@@ -301,7 +301,7 @@ class ControlLLLiteAdvanced(ControlBase, AdvancedControlBase):
 
     def pre_run_advanced(self, *args, **kwargs):
         AdvancedControlBase.pre_run_advanced(self, *args, **kwargs)
-        self.patch.control = self
+        self.patch.set_control(self)
     
     def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
         # normal ControlNet stuff
@@ -341,6 +341,10 @@ class ControlLLLiteAdvanced(ControlBase, AdvancedControlBase):
         self.copy_to(c)
         self.copy_to_advanced(c)
         return c
+    
+    # deepcopy needs to properly keep track of objects to work between model.clone calls!
+    def __deepcopy__(self, *args, **kwargs):
+        return self
 
     # def get_models(self):
     #     # get_models is called once at the start of every KSampler run - use to reset already_patched status
@@ -358,7 +362,6 @@ def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, mo
     for key in controlnet_data:
         # LLLLite check
         if "lllite" in key:
-            logger.info("ControlLLLite controlnet!")
             controlnet_type = ControlWeightType.CONTROLLLLITE
             break
         # SparseCtrl check
@@ -596,7 +599,7 @@ def load_controllllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
         if len(modules) == 1:
             module.is_first = True
 
-    logger.info(f"loaded {ckpt_path} successfully, {len(modules)} modules")
+    #logger.info(f"loaded {ckpt_path} successfully, {len(modules)} modules")
 
     patch = LLLitePatch(modules=modules)
     control = ControlLLLiteAdvanced(patch=patch, timestep_keyframes=timestep_keyframe)
diff --git a/adv_control/control_lllite.py b/adv_control/control_lllite.py
index e92a416..c5b7928 100644
--- a/adv_control/control_lllite.py
+++ b/adv_control/control_lllite.py
@@ -48,7 +48,6 @@ class LLLitePatch:
             # it turns out comparing single-value tensors to floats is extremely slow
             # a: Tensor = extra_options["sigmas"][0]
             if self.control.t > self.control.timestep_range[0] or self.control.t < self.control.timestep_range[1]:
-                logger.info("Stopping short!!!")
                 return q, k, v
 
         module_pfx = extra_options_to_module_prefix(extra_options)
@@ -63,11 +62,6 @@ class LLLitePatch:
         module_pfx_to_k = module_pfx + "_to_k"
         module_pfx_to_v = module_pfx + "_to_v"
 
-        # if masks present, get masks with same dims as attention
-        # if q.shape != k.shape or q.shape != v.shape:
-        #     logger.warn(f"mismatch!!! q:{q.shape}, k:{k.shape}, v:{v.shape}")
-        #logger.warn(f"{q.shape}")
-
         if module_pfx_to_q in self.modules:
             q = q + self.modules[module_pfx_to_q](q, self.control)
         if module_pfx_to_k in self.modules:
diff --git a/adv_control/control_sparsectrl.py b/adv_control/control_sparsectrl.py
index 9d51e6e..5885ed5 100644
--- a/adv_control/control_sparsectrl.py
+++ b/adv_control/control_sparsectrl.py
@@ -17,19 +17,35 @@ from comfy.ldm.modules.diffusionmodules.util import (
     timestep_embedding,
 )
 
+from comfy.cli_args import args
 from comfy.cldm.cldm import ControlNet as ControlNetCLDM
 from comfy.ldm.modules.attention import SpatialTransformer
-from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample
-from comfy.ldm.util import exists
-from comfy.ldm.modules.attention import default, optimized_attention
+from comfy.ldm.modules.attention import attention_basic, attention_pytorch, attention_split, attention_sub_quad, default
 from comfy.ldm.modules.attention import FeedForward, SpatialTransformer
+from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample
 from comfy.controlnet import broadcast_image_to
 from comfy.utils import repeat_to_batch_size
 import comfy.ops
+import comfy.model_management
 
 from .utils import TimestepKeyframeGroup, disable_weight_init_clean_groupnorm, prepare_mask_batch
 
 
+# until xformers bug is fixed, do not use xformers for VersatileAttention! TODO: change this when fix is out
+# logic for choosing optimized_attention method taken from comfy/ldm/modules/attention.py
+optimized_attention_mm = attention_basic
+if comfy.model_management.xformers_enabled():
+    pass
+    #optimized_attention_mm = attention_xformers
+if comfy.model_management.pytorch_attention_enabled():
+    optimized_attention_mm = attention_pytorch
+else:
+    if args.use_split_cross_attention:
+        optimized_attention_mm = attention_split
+    else:
+        optimized_attention_mm = attention_sub_quad
+
+
 class SparseControlNet(ControlNetCLDM):
     def __init__(self, *args,**kwargs):
         super().__init__(*args, **kwargs)
@@ -810,7 +826,7 @@ class CrossAttentionMM(nn.Module):
         if scale_mask is not None:
             k *= scale_mask
 
-        out = optimized_attention(q, k, v, self.heads, mask)
+        out = optimized_attention_mm(q, k, v, self.heads, mask)
         return self.to_out(out)