[LoRA SD3] add support for lora fusion in sd3 (#8616)

* add support for lora fusion in sd3

* add test to ensure fused lora and effective lora produce same outpouts

src/diffusers/loaders/lora.py

@@ -1728,3 +1728,78 @@ class SD3LoraLoaderMixin:
                     remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
 
         return (is_model_cpu_offload, is_sequential_cpu_offload)
+
+    def fuse_lora(
+        self,
+        fuse_transformer: bool = True,
+        lora_scale: float = 1.0,
+        safe_fusing: bool = False,
+        adapter_names: Optional[List[str]] = None,
+    ):
+        r"""
+        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            fuse_transformer (`bool`, defaults to `True`): Whether to fuse the transformer LoRA parameters.
+            lora_scale (`float`, defaults to 1.0):
+                Controls how much to influence the outputs with the LoRA parameters.
+            safe_fusing (`bool`, defaults to `False`):
+                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+            adapter_names (`List[str]`, *optional*):
+                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "nerijs/pixel-art-medium-128-v0.1",
+            weight_name="pixel-art-medium-128-v0.1.safetensors",
+            adapter_name="pixel",
+        )
+        pipeline.fuse_lora(lora_scale=0.7)
+        ```
+        """
+        if fuse_transformer:
+            self.num_fused_loras += 1
+
+        if fuse_transformer:
+            transformer = (
+                getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
+            )
+            transformer.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)
+
+    def unfuse_lora(self, unfuse_transformer: bool = True):
+        r"""
+        Reverses the effect of
+        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the transformer LoRA parameters.
+        """
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
+        if unfuse_transformer:
+            for module in transformer.modules():
+                if isinstance(module, BaseTunerLayer):
+                    module.unmerge()
+
+        self.num_fused_loras -= 1

src/diffusers/models/transformers/transformer_sd3.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 
+import inspect
+from functools import partial
 from typing import Any, Dict, List, Optional, Union
 
 import torch
@@ -239,6 +241,47 @@ class SD3Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
         if hasattr(module, "gradient_checkpointing"):
             module.gradient_checkpointing = value
 
+    def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for `fuse_lora()`.")
+
+        self.lora_scale = lora_scale
+        self._safe_fusing = safe_fusing
+        self.apply(partial(self._fuse_lora_apply, adapter_names=adapter_names))
+
+    def _fuse_lora_apply(self, module, adapter_names=None):
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        merge_kwargs = {"safe_merge": self._safe_fusing}
+
+        if isinstance(module, BaseTunerLayer):
+            if self.lora_scale != 1.0:
+                module.scale_layer(self.lora_scale)
+
+            # For BC with prevous PEFT versions, we need to check the signature
+            # of the `merge` method to see if it supports the `adapter_names` argument.
+            supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
+            if "adapter_names" in supported_merge_kwargs:
+                merge_kwargs["adapter_names"] = adapter_names
+            elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
+                raise ValueError(
+                    "The `adapter_names` argument is not supported with your PEFT version. Please upgrade"
+                    " to the latest version of PEFT. `pip install -U peft`"
+                )
+
+            module.merge(**merge_kwargs)
+
+    def unfuse_lora(self):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for `unfuse_lora()`.")
+        self.apply(self._unfuse_lora_apply)
+
+    def _unfuse_lora_apply(self, module):
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        if isinstance(module, BaseTunerLayer):
+            module.unmerge()
+
     def forward(
         self,
         hidden_states: torch.FloatTensor,

tests/lora/test_lora_layers_sd3.py

@@ -205,3 +205,85 @@ class SD3LoRATests(unittest.TestCase):
             np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
             "Lora + 0 scale should lead to same result as no LoRA",
         )
+
+    def test_simple_inference_with_transformer_fused(self):
+        components = self.get_dummy_components()
+        transformer_lora_config = self.get_lora_config_for_transformer()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_no_lora = pipe(**inputs).images
+
+        pipe.transformer.add_adapter(transformer_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+
+        pipe.fuse_lora()
+        # Fusing should still keep the LoRA layers
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+
+        inputs = self.get_dummy_inputs(torch_device)
+        ouput_fused = pipe(**inputs).images
+        self.assertFalse(
+            np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
+        )
+
+    def test_simple_inference_with_transformer_fused_with_no_fusion(self):
+        components = self.get_dummy_components()
+        transformer_lora_config = self.get_lora_config_for_transformer()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_no_lora = pipe(**inputs).images
+
+        pipe.transformer.add_adapter(transformer_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        inputs = self.get_dummy_inputs(torch_device)
+        ouput_lora = pipe(**inputs).images
+
+        pipe.fuse_lora()
+        # Fusing should still keep the LoRA layers
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+
+        inputs = self.get_dummy_inputs(torch_device)
+        ouput_fused = pipe(**inputs).images
+        self.assertFalse(
+            np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
+        )
+        self.assertTrue(
+            np.allclose(ouput_fused, ouput_lora, atol=1e-3, rtol=1e-3),
+            "Fused lora output should be changed when LoRA isn't fused but still effective.",
+        )
+
+    def test_simple_inference_with_transformer_fuse_unfuse(self):
+        components = self.get_dummy_components()
+        transformer_lora_config = self.get_lora_config_for_transformer()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_no_lora = pipe(**inputs).images
+
+        pipe.transformer.add_adapter(transformer_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+
+        pipe.fuse_lora()
+        # Fusing should still keep the LoRA layers
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        inputs = self.get_dummy_inputs(torch_device)
+        ouput_fused = pipe(**inputs).images
+        self.assertFalse(
+            np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
+        )
+
+        pipe.unfuse_lora()
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        inputs = self.get_dummy_inputs(torch_device)
+        output_unfused_lora = pipe(**inputs).images
+        self.assertTrue(
+            np.allclose(ouput_fused, output_unfused_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
+        )