Tidy _prepare_attention_processors(...) logic.

Author: RyanJDick

invokeai/backend/ip_adapter/unet_patcher.py

@@ -13,22 +13,10 @@ def _prepare_attention_processors(unet: UNet2DConditionModel, ip_adapters: list[
 
     Note that the `unet` param is only used to determine attention block dimensions and naming.
     """
-    # TODO(ryand): This logic can be simplified.
-
     # Construct a dict of attention processors based on the UNet's architecture.
     attn_procs = {}
     for idx, name in enumerate(unet.attn_processors.keys()):
-        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-        if name.startswith("mid_block"):
-            hidden_size = unet.config.block_out_channels[-1]
-        elif name.startswith("up_blocks"):
-            block_id = int(name[len("up_blocks.")])
-            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-        elif name.startswith("down_blocks"):
-            block_id = int(name[len("down_blocks.")])
-            hidden_size = unet.config.block_out_channels[block_id]
-
-        if cross_attention_dim is None:
+        if name.endswith("attn1.processor"):
             attn_procs[name] = AttnProcessor2_0()
         else:
             # Collect the weights from each IP Adapter for the idx'th attention processor.
@@ -43,8 +31,7 @@ def apply_ip_adapter_attention(unet: UNet2DConditionModel, ip_adapters: list[IPA
     """A context manager that patches `unet` with IP-Adapter attention processors.
 
     Yields:
-        Scales: The Scales object, which can be used to dynamically alter the scales of the
-            IP-Adapters.
+        Scales: The Scales object, which can be used to dynamically alter the scales of the IP-Adapters.
     """
     scales = Scales([1.0] * len(ip_adapters))