Merge branch 'main' into dreambooth-lora-flux-exploration

src/diffusers/loaders/lora_pipeline.py

@@ -1358,14 +1358,30 @@ def load_lora_into_transformer(
             inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name, **peft_kwargs)
             incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name, **peft_kwargs)
 
+            warn_msg = ""
             if incompatible_keys is not None:
-                # check only for unexpected keys
+                # Check only for unexpected keys.
                 unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                 if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
 
             # Offload back.
             if is_model_cpu_offload:
@@ -1932,14 +1948,30 @@ def load_lora_into_transformer(
             inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name, **peft_kwargs)
             incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name, **peft_kwargs)
 
+            warn_msg = ""
             if incompatible_keys is not None:
-                # check only for unexpected keys
+                # Check only for unexpected keys.
                 unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                 if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
 
             # Offload back.
             if is_model_cpu_offload:
@@ -2279,14 +2311,30 @@ def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, ada
             inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
             incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)
 
+            warn_msg = ""
             if incompatible_keys is not None:
-                # check only for unexpected keys
+                # Check only for unexpected keys.
                 unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                 if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
 
             # Offload back.
             if is_model_cpu_offload:
@@ -2717,14 +2765,30 @@ def load_lora_into_transformer(
             inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name, **peft_kwargs)
             incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name, **peft_kwargs)
 
+            warn_msg = ""
             if incompatible_keys is not None:
-                # check only for unexpected keys
+                # Check only for unexpected keys.
                 unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                 if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
 
             # Offload back.
             if is_model_cpu_offload:

src/diffusers/loaders/unet.py

@@ -354,14 +354,30 @@ def _process_lora(
             inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
             incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
 
+            warn_msg = ""
             if incompatible_keys is not None:
-                # check only for unexpected keys
+                # Check only for unexpected keys.
                 unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                 if unexpected_keys:
-                    logger.warning(
-                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
-                        f" {unexpected_keys}. "
-                    )
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
 
         return is_model_cpu_offload, is_sequential_cpu_offload
 

src/diffusers/training_utils.py

@@ -24,6 +24,9 @@
 if is_transformers_available():
     import transformers
 
+    if transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
+        import deepspeed
+
 if is_peft_available():
     from peft import set_peft_model_state_dict
 
@@ -442,15 +445,13 @@ def step(self, parameters: Iterable[torch.nn.Parameter]):
         self.cur_decay_value = decay
         one_minus_decay = 1 - decay
 
-        context_manager = contextlib.nullcontext
-        if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
-            import deepspeed
+        context_manager = contextlib.nullcontext()
 
         if self.foreach:
             if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
                 context_manager = deepspeed.zero.GatheredParameters(parameters, modifier_rank=None)
 
-            with context_manager():
+            with context_manager:
                 params_grad = [param for param in parameters if param.requires_grad]
                 s_params_grad = [
                     s_param for s_param, param in zip(self.shadow_params, parameters) if param.requires_grad
@@ -472,7 +473,7 @@ def step(self, parameters: Iterable[torch.nn.Parameter]):
                 if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
                     context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None)
 
-                with context_manager():
+                with context_manager:
                     if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param))
                     else:

tests/lora/test_lora_layers_flux.py

@@ -27,6 +27,7 @@
 from diffusers.utils.testing_utils import (
     floats_tensor,
     is_peft_available,
+    numpy_cosine_similarity_distance,
     require_peft_backend,
     require_torch_gpu,
     slow,
@@ -166,7 +167,7 @@ def test_modify_padding_mode(self):
 @slow
 @require_torch_gpu
 @require_peft_backend
-@unittest.skip("We cannot run inference on this model with the current CI hardware")
+# @unittest.skip("We cannot run inference on this model with the current CI hardware")
 # TODO (DN6, sayakpaul): move these tests to a beefier GPU
 class FluxLoRAIntegrationTests(unittest.TestCase):
     """internal note: The integration slices were obtained on audace.
@@ -208,9 +209,11 @@ def test_flux_the_last_ben(self):
             generator=torch.manual_seed(self.seed),
         ).images
         out_slice = out[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.1719, 0.1719, 0.1699, 0.1719, 0.1719, 0.1738, 0.1641, 0.1621, 0.2090])
+        expected_slice = np.array([0.1855, 0.1855, 0.1836, 0.1855, 0.1836, 0.1875, 0.1777, 0.1758, 0.2246])
 
-        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
+
+        assert max_diff < 1e-3
 
     def test_flux_kohya(self):
         self.pipeline.load_lora_weights("Norod78/brain-slug-flux")
@@ -230,7 +233,9 @@ def test_flux_kohya(self):
         out_slice = out[0, -3:, -3:, -1].flatten()
         expected_slice = np.array([0.6367, 0.6367, 0.6328, 0.6367, 0.6328, 0.6289, 0.6367, 0.6328, 0.6484])
 
-        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
+
+        assert max_diff < 1e-3
 
     def test_flux_kohya_with_text_encoder(self):
         self.pipeline.load_lora_weights("cocktailpeanut/optimus", weight_name="optimus.safetensors")
@@ -248,9 +253,11 @@ def test_flux_kohya_with_text_encoder(self):
         ).images
 
         out_slice = out[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.4023, 0.4043, 0.4023, 0.3965, 0.3984, 0.3984, 0.3906, 0.3906, 0.4219])
+        expected_slice = np.array([0.4023, 0.4023, 0.4023, 0.3965, 0.3984, 0.3965, 0.3926, 0.3906, 0.4219])
 
-        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
+
+        assert max_diff < 1e-3
 
     def test_flux_xlabs(self):
         self.pipeline.load_lora_weights("XLabs-AI/flux-lora-collection", weight_name="disney_lora.safetensors")
@@ -268,6 +275,8 @@ def test_flux_xlabs(self):
             generator=torch.manual_seed(self.seed),
         ).images
         out_slice = out[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.3984, 0.4199, 0.4453, 0.4102, 0.4375, 0.4590, 0.4141, 0.4355, 0.4980])
+        expected_slice = np.array([0.3965, 0.4180, 0.4434, 0.4082, 0.4375, 0.4590, 0.4141, 0.4375, 0.4980])
+
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
 
-        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+        assert max_diff < 1e-3

tests/lora/utils.py

@@ -27,8 +27,10 @@
     LCMScheduler,
     UNet2DConditionModel,
 )
+from diffusers.utils import logging
 from diffusers.utils.import_utils import is_peft_available
 from diffusers.utils.testing_utils import (
+    CaptureLogger,
     floats_tensor,
     require_peft_backend,
     require_peft_version_greater,
@@ -219,10 +221,18 @@ def _get_modules_to_save(self, pipe, has_denoiser=False):
         modules_to_save = {}
         lora_loadable_modules = self.pipeline_class._lora_loadable_modules
 
-        if "text_encoder" in lora_loadable_modules and hasattr(pipe, "text_encoder"):
+        if (
+            "text_encoder" in lora_loadable_modules
+            and hasattr(pipe, "text_encoder")
+            and getattr(pipe.text_encoder, "peft_config", None) is not None
+        ):
             modules_to_save["text_encoder"] = pipe.text_encoder
 
-        if "text_encoder_2" in lora_loadable_modules and hasattr(pipe, "text_encoder_2"):
+        if (
+            "text_encoder_2" in lora_loadable_modules
+            and hasattr(pipe, "text_encoder_2")
+            and getattr(pipe.text_encoder_2, "peft_config", None) is not None
+        ):
             modules_to_save["text_encoder_2"] = pipe.text_encoder_2
 
         if has_denoiser:
@@ -1747,6 +1757,83 @@ def test_simple_inference_with_dora(self):
                 "DoRA lora should change the output",
             )
 
+    def test_missing_keys_warning(self):
+        scheduler_cls = self.scheduler_classes[0]
+        # Skip text encoder check for now as that is handled with `transformers`.
+        components, _, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+        denoiser.add_adapter(denoiser_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+            lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+            self.pipeline_class.save_lora_weights(
+                save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
+            )
+            pipe.unload_lora_weights()
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
+            state_dict = torch.load(os.path.join(tmpdirname, "pytorch_lora_weights.bin"), weights_only=True)
+
+        # To make things dynamic since we cannot settle with a single key for all the models where we
+        # offer PEFT support.
+        missing_key = [k for k in state_dict if "lora_A" in k][0]
+        del state_dict[missing_key]
+
+        logger = (
+            logging.get_logger("diffusers.loaders.unet")
+            if self.unet_kwargs is not None
+            else logging.get_logger("diffusers.loaders.lora_pipeline")
+        )
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            pipe.load_lora_weights(state_dict)
+
+        # Since the missing key won't contain the adapter name ("default_0").
+        # Also strip out the component prefix (such as "unet." from `missing_key`).
+        component = list({k.split(".")[0] for k in state_dict})[0]
+        self.assertTrue(missing_key.replace(f"{component}.", "") in cap_logger.out.replace("default_0.", ""))
+
+    def test_unexpected_keys_warning(self):
+        scheduler_cls = self.scheduler_classes[0]
+        # Skip text encoder check for now as that is handled with `transformers`.
+        components, _, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+        denoiser.add_adapter(denoiser_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+            lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+            self.pipeline_class.save_lora_weights(
+                save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
+            )
+            pipe.unload_lora_weights()
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
+            state_dict = torch.load(os.path.join(tmpdirname, "pytorch_lora_weights.bin"), weights_only=True)
+
+        unexpected_key = [k for k in state_dict if "lora_A" in k][0] + ".diffusers_cat"
+        state_dict[unexpected_key] = torch.tensor(1.0, device=torch_device)
+
+        logger = (
+            logging.get_logger("diffusers.loaders.unet")
+            if self.unet_kwargs is not None
+            else logging.get_logger("diffusers.loaders.lora_pipeline")
+        )
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            pipe.load_lora_weights(state_dict)
+
+        self.assertTrue(".diffusers_cat" in cap_logger.out)
+
     @unittest.skip("This is failing for now - need to investigate")
     def test_simple_inference_with_text_denoiser_lora_unfused_torch_compile(self):
         """