ControlNetXS fixes. (#6228)

update

src/diffusers/models/controlnetxs.py

@@ -23,9 +23,7 @@ from torch.nn.modules.normalization import GroupNorm
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput, logging
-from .attention_processor import (
-    AttentionProcessor,
-)
+from .attention_processor import USE_PEFT_BACKEND, AttentionProcessor
 from .autoencoders import AutoencoderKL
 from .lora import LoRACompatibleConv
 from .modeling_utils import ModelMixin
@@ -817,11 +815,23 @@ def increase_block_input_in_encoder_resnet(unet: UNet2DConditionModel, block_no,
     norm_kwargs = {a: getattr(old_norm1, a) for a in norm_args}
     norm_kwargs["num_channels"] += by  # surgery done here
     # conv1
-    conv1_args = (
-        "in_channels out_channels kernel_size stride padding dilation groups bias padding_mode lora_layer".split(" ")
-    )
+    conv1_args = [
+        "in_channels",
+        "out_channels",
+        "kernel_size",
+        "stride",
+        "padding",
+        "dilation",
+        "groups",
+        "bias",
+        "padding_mode",
+    ]
+    if not USE_PEFT_BACKEND:
+        conv1_args.append("lora_layer")
+
     for a in conv1_args:
         assert hasattr(old_conv1, a)
+
     conv1_kwargs = {a: getattr(old_conv1, a) for a in conv1_args}
     conv1_kwargs["bias"] = "bias" in conv1_kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
     conv1_kwargs["in_channels"] += by  # surgery done here
@@ -839,25 +849,42 @@ def increase_block_input_in_encoder_resnet(unet: UNet2DConditionModel, block_no,
     }
     # swap old with new modules
     unet.down_blocks[block_no].resnets[resnet_idx].norm1 = GroupNorm(**norm_kwargs)
-    unet.down_blocks[block_no].resnets[resnet_idx].conv1 = LoRACompatibleConv(**conv1_kwargs)
-    unet.down_blocks[block_no].resnets[resnet_idx].conv_shortcut = LoRACompatibleConv(**conv_shortcut_args_kwargs)
+    unet.down_blocks[block_no].resnets[resnet_idx].conv1 = (
+        nn.Conv2d(**conv1_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv1_kwargs)
+    )
+    unet.down_blocks[block_no].resnets[resnet_idx].conv_shortcut = (
+        nn.Conv2d(**conv_shortcut_args_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv_shortcut_args_kwargs)
+    )
     unet.down_blocks[block_no].resnets[resnet_idx].in_channels += by  # surgery done here
 
 
 def increase_block_input_in_encoder_downsampler(unet: UNet2DConditionModel, block_no, by):
     """Increase channels sizes to allow for additional concatted information from base model"""
     old_down = unet.down_blocks[block_no].downsamplers[0].conv
-    # conv1
-    args = "in_channels out_channels kernel_size stride padding dilation groups bias padding_mode lora_layer".split(
-        " "
-    )
+
+    args = [
+        "in_channels",
+        "out_channels",
+        "kernel_size",
+        "stride",
+        "padding",
+        "dilation",
+        "groups",
+        "bias",
+        "padding_mode",
+    ]
+    if not USE_PEFT_BACKEND:
+        args.append("lora_layer")
+
     for a in args:
         assert hasattr(old_down, a)
     kwargs = {a: getattr(old_down, a) for a in args}
     kwargs["bias"] = "bias" in kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
     kwargs["in_channels"] += by  # surgery done here
     # swap old with new modules
-    unet.down_blocks[block_no].downsamplers[0].conv = LoRACompatibleConv(**kwargs)
+    unet.down_blocks[block_no].downsamplers[0].conv = (
+        nn.Conv2d(**kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**kwargs)
+    )
     unet.down_blocks[block_no].downsamplers[0].channels += by  # surgery done here
 
 
@@ -871,12 +898,20 @@ def increase_block_input_in_mid_resnet(unet: UNet2DConditionModel, by):
         assert hasattr(old_norm1, a)
     norm_kwargs = {a: getattr(old_norm1, a) for a in norm_args}
     norm_kwargs["num_channels"] += by  # surgery done here
-    # conv1
-    conv1_args = (
-        "in_channels out_channels kernel_size stride padding dilation groups bias padding_mode lora_layer".split(" ")
-    )
-    for a in conv1_args:
-        assert hasattr(old_conv1, a)
+    conv1_args = [
+        "in_channels",
+        "out_channels",
+        "kernel_size",
+        "stride",
+        "padding",
+        "dilation",
+        "groups",
+        "bias",
+        "padding_mode",
+    ]
+    if not USE_PEFT_BACKEND:
+        conv1_args.append("lora_layer")
+
     conv1_kwargs = {a: getattr(old_conv1, a) for a in conv1_args}
     conv1_kwargs["bias"] = "bias" in conv1_kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
     conv1_kwargs["in_channels"] += by  # surgery done here
@@ -894,8 +929,12 @@ def increase_block_input_in_mid_resnet(unet: UNet2DConditionModel, by):
     }
     # swap old with new modules
     unet.mid_block.resnets[0].norm1 = GroupNorm(**norm_kwargs)
-    unet.mid_block.resnets[0].conv1 = LoRACompatibleConv(**conv1_kwargs)
-    unet.mid_block.resnets[0].conv_shortcut = LoRACompatibleConv(**conv_shortcut_args_kwargs)
+    unet.mid_block.resnets[0].conv1 = (
+        nn.Conv2d(**conv1_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv1_kwargs)
+    )
+    unet.mid_block.resnets[0].conv_shortcut = (
+        nn.Conv2d(**conv_shortcut_args_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv_shortcut_args_kwargs)
+    )
     unet.mid_block.resnets[0].in_channels += by  # surgery done here
 
 

tests/pipelines/controlnetxs/test_controlnetxs.py

@@ -34,6 +34,7 @@ from diffusers.utils.testing_utils import (
     enable_full_determinism,
     load_image,
     load_numpy,
+    numpy_cosine_similarity_distance,
     require_python39_or_higher,
     require_torch_2,
     require_torch_gpu,
@@ -273,7 +274,9 @@ class ControlNetXSPipelineSlowTests(unittest.TestCase):
 
         original_image = image[-3:, -3:, -1].flatten()
         expected_image = np.array([0.1274, 0.1401, 0.147, 0.1185, 0.1555, 0.1492, 0.1565, 0.1474, 0.1701])
-        assert np.allclose(original_image, expected_image, atol=1e-04)
+
+        max_diff = numpy_cosine_similarity_distance(original_image, expected_image)
+        assert max_diff < 1e-4
 
     def test_depth(self):
         controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SD2.1-depth")
@@ -298,7 +301,9 @@ class ControlNetXSPipelineSlowTests(unittest.TestCase):
 
         original_image = image[-3:, -3:, -1].flatten()
         expected_image = np.array([0.1098, 0.1025, 0.1211, 0.1129, 0.1165, 0.1262, 0.1185, 0.1261, 0.1703])
-        assert np.allclose(original_image, expected_image, atol=1e-04)
+
+        max_diff = numpy_cosine_similarity_distance(original_image, expected_image)
+        assert max_diff < 1e-4
 
     @require_python39_or_higher
     @require_torch_2