Merge branch 'main' into lora/fix-wan

Author: a-r-r-o-w

src/diffusers/loaders/lora_base.py

@@ -934,6 +934,27 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
         Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
         you want to load multiple adapters and free some GPU memory.
 
+        After offloading the LoRA adapters to CPU, as long as the rest of the model is still on GPU, the LoRA adapters
+        can no longer be used for inference, as that would cause a device mismatch. Remember to set the device back to
+        GPU before using those LoRA adapters for inference.
+
+        ```python
+        >>> pipe.load_lora_weights(path_1, adapter_name="adapter-1")
+        >>> pipe.load_lora_weights(path_2, adapter_name="adapter-2")
+        >>> pipe.set_adapters("adapter-1")
+        >>> image_1 = pipe(**kwargs)
+        >>> # switch to adapter-2, offload adapter-1
+        >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cpu")
+        >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cuda:0")
+        >>> pipe.set_adapters("adapter-2")
+        >>> image_2 = pipe(**kwargs)
+        >>> # switch back to adapter-1, offload adapter-2
+        >>> pipeline.set_lora_device(adapter_names=["adapter-2"], device="cpu")
+        >>> pipeline.set_lora_device(adapter_names=["adapter-1"], device="cuda:0")
+        >>> pipe.set_adapters("adapter-1")
+        >>> ...
+        ```
+
         Args:
             adapter_names (`List[str]`):
                 List of adapters to send device to.
@@ -949,6 +970,10 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
                 for module in model.modules():
                     if isinstance(module, BaseTunerLayer):
                         for adapter_name in adapter_names:
+                            if adapter_name not in module.lora_A:
+                                # it is sufficient to check lora_A
+                                continue
+
                             module.lora_A[adapter_name].to(device)
                             module.lora_B[adapter_name].to(device)
                             # this is a param, not a module, so device placement is not in-place -> re-assign

tests/lora/test_lora_layers_sd.py

@@ -120,7 +120,7 @@ def test_integration_move_lora_cpu(self):
 
         self.assertTrue(
             check_if_lora_correctly_set(pipe.unet),
-            "Lora not correctly set in text encoder",
+            "Lora not correctly set in unet",
         )
 
         # We will offload the first adapter in CPU and check if the offloading
@@ -187,7 +187,7 @@ def test_integration_move_lora_dora_cpu(self):
 
         self.assertTrue(
             check_if_lora_correctly_set(pipe.unet),
-            "Lora not correctly set in text encoder",
+            "Lora not correctly set in unet",
         )
 
         for name, param in pipe.unet.named_parameters():
@@ -208,6 +208,53 @@ def test_integration_move_lora_dora_cpu(self):
             if "lora_" in name:
                 self.assertNotEqual(param.device, torch.device("cpu"))
 
+    @slow
+    @require_torch_accelerator
+    def test_integration_set_lora_device_different_target_layers(self):
+        # fixes a bug that occurred when calling set_lora_device with multiple adapters loaded that target different
+        # layers, see #11833
+        from peft import LoraConfig
+
+        path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        # configs partly target the same, partly different layers
+        config0 = LoraConfig(target_modules=["to_k", "to_v"])
+        config1 = LoraConfig(target_modules=["to_k", "to_q"])
+        pipe.unet.add_adapter(config0, adapter_name="adapter-0")
+        pipe.unet.add_adapter(config1, adapter_name="adapter-1")
+        pipe = pipe.to(torch_device)
+
+        self.assertTrue(
+            check_if_lora_correctly_set(pipe.unet),
+            "Lora not correctly set in unet",
+        )
+
+        # sanity check that the adapters don't target the same layers, otherwise the test passes even without the fix
+        modules_adapter_0 = {n for n, _ in pipe.unet.named_modules() if n.endswith(".adapter-0")}
+        modules_adapter_1 = {n for n, _ in pipe.unet.named_modules() if n.endswith(".adapter-1")}
+        self.assertNotEqual(modules_adapter_0, modules_adapter_1)
+        self.assertTrue(modules_adapter_0 - modules_adapter_1)
+        self.assertTrue(modules_adapter_1 - modules_adapter_0)
+
+        # setting both separately works
+        pipe.set_lora_device(["adapter-0"], "cpu")
+        pipe.set_lora_device(["adapter-1"], "cpu")
+
+        for name, module in pipe.unet.named_modules():
+            if "adapter-0" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device == torch.device("cpu"))
+            elif "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device == torch.device("cpu"))
+
+        # setting both at once also works
+        pipe.set_lora_device(["adapter-0", "adapter-1"], torch_device)
+
+        for name, module in pipe.unet.named_modules():
+            if "adapter-0" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device != torch.device("cpu"))
+            elif "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device != torch.device("cpu"))
+
 
 @slow
 @nightly