use original ControlNet instead of Diffusers

library/sdxl_original_unet.py

@@ -30,7 +30,7 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn import functional as F
 from einops import rearrange
-from .utils import setup_logging
+from library.utils import setup_logging
 
 setup_logging()
 import logging
@@ -1156,9 +1156,9 @@ class InferSdxlUNet2DConditionModel:
             self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000
             self.ds_ratio = ds_ratio
 
-    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
+    def forward(self, x, timesteps=None, context=None, y=None, input_resi_add=None, mid_add=None, **kwargs):
         r"""
-        current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink.
+        current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink and ControlNet.
         """
         _self = self.delegate
 
@@ -1209,6 +1209,8 @@ class InferSdxlUNet2DConditionModel:
             hs.append(h)
 
         h = call_module(_self.middle_block, h, emb, context)
+        if mid_add is not None:
+            h = h + mid_add
 
         for module in _self.output_blocks:
             # Deep Shrink
@@ -1217,7 +1219,11 @@ class InferSdxlUNet2DConditionModel:
                     # print("upsample", h.shape, hs[-1].shape)
                     h = resize_like(h, hs[-1])
 
-            h = torch.cat([h, hs.pop()], dim=1)
+            resi = hs.pop()
+            if input_resi_add is not None:
+                resi = resi + input_resi_add.pop()
+
+            h = torch.cat([h, resi], dim=1)
             h = call_module(module, h, emb, context)
 
         # Deep Shrink: in case of depth 0