Release: v.0.21.4-patch

* fix lora fuse unfuse

* add same changes to loaders.py

* add test

---------

Co-authored-by: multimodalart <joaopaulo.passos+multimodal@gmail.com>

setup.py

@@ -244,7 +244,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.21.3",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.21.4",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.21.3"
+__version__ = "0.21.4"
 
 from typing import TYPE_CHECKING
 

src/diffusers/loaders.py

@@ -119,7 +119,7 @@ class PatchedLoraProjection(nn.Module):
         self.lora_scale = lora_scale
 
     def _unfuse_lora(self):
-        if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
+        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
             return
 
         fused_weight = self.regular_linear_layer.weight.data

src/diffusers/models/lora.py

@@ -139,7 +139,7 @@ class LoRACompatibleConv(nn.Conv2d):
         self._lora_scale = lora_scale
 
     def _unfuse_lora(self):
-        if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
+        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
             return
 
         fused_weight = self.weight.data
@@ -204,7 +204,7 @@ class LoRACompatibleLinear(nn.Linear):
         self._lora_scale = lora_scale
 
     def _unfuse_lora(self):
-        if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
+        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
             return
 
         fused_weight = self.weight.data

tests/models/test_lora_layers.py

@@ -43,7 +43,7 @@ from diffusers.models.attention_processor import (
     LoRAAttnProcessor2_0,
     XFormersAttnProcessor,
 )
-from diffusers.utils.testing_utils import floats_tensor, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import floats_tensor, nightly, require_torch_gpu, slow, torch_device
 
 
 def create_unet_lora_layers(unet: nn.Module):
@@ -1464,3 +1464,41 @@ class LoraIntegrationTests(unittest.TestCase):
         expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
 
         self.assertTrue(np.allclose(images, expected, atol=1e-3))
+
+    @nightly
+    def test_sequential_fuse_unfuse(self):
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+
+        # 1. round
+        pipe.load_lora_weights("Pclanglais/TintinIA")
+        pipe.fuse_lora()
+
+        generator = torch.Generator().manual_seed(0)
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        image_slice = images[0, -3:, -3:, -1].flatten()
+
+        pipe.unfuse_lora()
+
+        # 2. round
+        pipe.load_lora_weights("ProomptEngineer/pe-balloon-diffusion-style")
+        pipe.fuse_lora()
+        pipe.unfuse_lora()
+
+        # 3. round
+        pipe.load_lora_weights("ostris/crayon_style_lora_sdxl")
+        pipe.fuse_lora()
+        pipe.unfuse_lora()
+
+        # 4. back to 1st round
+        pipe.load_lora_weights("Pclanglais/TintinIA")
+        pipe.fuse_lora()
+
+        generator = torch.Generator().manual_seed(0)
+        images_2 = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        image_slice_2 = images_2[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(image_slice, image_slice_2, atol=1e-3))