fix ControlNet not working

tools/original_control_net.py

@@ -4,8 +4,7 @@ import cv2
 import torch
 from safetensors.torch import load_file
 
-from diffusers import UNet2DConditionModel
-from diffusers.models.unet_2d_condition import UNet2DConditionOutput
+from library.original_unet import UNet2DConditionModel, SampleOutput
 
 import library.model_util as model_util
 
@@ -235,10 +234,6 @@ def unet_forward(
         print("Forward upsample size to force interpolation output size.")
         forward_upsample_size = True
 
-    # 0. center input if necessary
-    if unet.config.center_input_sample:
-        sample = 2 * sample - 1.0
-
     # 1. time
     timesteps = timestep
     if not torch.is_tensor(timesteps):
@@ -277,7 +272,7 @@ def unet_forward(
     # 3. down
     down_block_res_samples = (sample,)
     for downsample_block in unet.down_blocks:
-        if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+        if downsample_block.has_cross_attention:
             sample, res_samples = downsample_block(
                 hidden_states=sample,
                 temb=emb,
@@ -321,7 +316,7 @@ def unet_forward(
         if not is_final_block and forward_upsample_size:
             upsample_size = down_block_res_samples[-1].shape[2:]
 
-        if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+        if upsample_block.has_cross_attention:
             sample = upsample_block(
                 hidden_states=sample,
                 temb=emb,
@@ -338,4 +333,4 @@ def unet_forward(
     sample = unet.conv_act(sample)
     sample = unet.conv_out(sample)
 
-    return UNet2DConditionOutput(sample=sample)
+    return SampleOutput(sample=sample)