Support higher dimension LoRAs (#4625)

* Support higher dimension LoRAs

* add: tests

* fix: assertion values.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/models/lora.py

@@ -22,9 +22,6 @@ class LoRALinearLayer(nn.Module):
     def __init__(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None):
         super().__init__()
 
-        if rank > min(in_features, out_features):
-            raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
-
         self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
         self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
         # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
@@ -54,9 +51,6 @@ class LoRAConv2dLayer(nn.Module):
     ):
         super().__init__()
 
-        if rank > min(in_features, out_features):
-            raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
-
         self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
         # according to the official kohya_ss trainer kernel_size are always fixed for the up layer
         # # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129

tests/models/test_lora_layers.py

@@ -779,6 +779,25 @@ class LoraIntegrationTests(unittest.TestCase):
 
         self.assertTrue(np.allclose(images, expected, atol=1e-4))
 
+    def test_kohya_sd_v15_with_higher_dimensions(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+            torch_device
+        )
+        lora_model_id = "hf-internal-testing/urushisato-lora"
+        lora_filename = "urushisato_v15.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.7165, 0.6616, 0.5833, 0.7504, 0.6718, 0.587, 0.6871, 0.6361, 0.5694])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+
     def test_vanilla_funetuning(self):
         generator = torch.Generator().manual_seed(0)