diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index e5ce403cb7..a3d5ae3c07 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -307,9 +307,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if is_xformers_available() and not Globals.disable_xformers:
             self.enable_xformers_memory_efficient_attention()
         else:
-            slice_size = 2
+            slice_size = 4 # or 2, or 8. i chose this arbitrarily.
             self.enable_attention_slicing(slice_size=slice_size)
 
+
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               conditioning_data: ConditioningData,
                               *,
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 8b5467e85f..c248343040 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -560,77 +560,6 @@ class SwapCrossAttnContext:
         return mask, indices
 
 
-class SwapCrossAttnProcessor(CrossAttnProcessor):
-
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None,
-                 # kwargs
-                 swap_cross_attn_context: SwapCrossAttnContext=None):
-
-        attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
-
-        # if cross-attention control is not in play, just call through to the base implementation.
-        if swap_cross_attn_context is None or not swap_cross_attn_context.wants_cross_attention_control(attention_type):
-            #print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
-            return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
-        #else:
-        #    print(f"SwapCrossAttnContext for {attention_type} active")
-
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
-        query = attn.to_q(hidden_states)
-        query = attn.head_to_batch_dim(query)
-
-        # helper function
-        def get_attention_probs(embeddings):
-            this_key = attn.to_k(embeddings)
-            this_key = attn.head_to_batch_dim(this_key)
-            return attn.get_attention_scores(query, this_key, attention_mask)
-
-        if attention_type == CrossAttentionType.SELF:
-            # self attention has no remapping, it just bluntly copies the whole tensor
-            attention_probs = get_attention_probs(hidden_states)
-            value = attn.to_v(hidden_states)
-        else:
-            # tokens (cross) attention
-            # first, find attention probabilities for the "original" prompt
-            original_text_embeddings = encoder_hidden_states
-            original_attention_probs = get_attention_probs(original_text_embeddings)
-
-            # then, find attention probabilities for the "modified" prompt
-            modified_text_embeddings = swap_cross_attn_context.modified_text_embeddings
-            modified_attention_probs = get_attention_probs(modified_text_embeddings)
-
-            # because the prompt modifications may result in token sequences shifted forwards or backwards,
-            # the original attention probabilities must be remapped to account for token index changes in the
-            # modified prompt
-            remapped_original_attention_probs = torch.index_select(original_attention_probs, -1,
-                                                                   swap_cross_attn_context.index_map)
-
-            # only some tokens taken from the original attention probabilities. this is controlled by the mask.
-            mask = swap_cross_attn_context.mask
-            inverse_mask = 1 - mask
-            attention_probs = \
-                remapped_original_attention_probs * mask + \
-                modified_attention_probs * inverse_mask
-
-            # for the "value" just use the modified text embeddings.
-            value = attn.to_v(modified_text_embeddings)
-
-        value = attn.head_to_batch_dim(value)
-
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
-
-
-
 class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
     # TODO: dynamically pick slice size based on memory conditions
@@ -714,3 +643,9 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         hidden_states = attn.to_out[1](hidden_states)
 
         return hidden_states
+
+
+class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
+
+    def __init__(self):
+        super(SwapCrossAttnProcessor, self).__init__(slice_size=1e6) # big number so we never slice