Revert "[LoRA] introduce LoraBaseMixin to promote reusability." (#8976)

Revert "[LoRA] introduce LoraBaseMixin to promote reusability. (#8774)"

This reverts commit 527430d0.

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py

@@ -11,7 +11,7 @@ from torch.nn.functional import grid_sample
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
@@ -281,9 +281,7 @@ def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_s
     return warped_latents
 
 
-class TextToVideoZeroPipeline(
-    DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin
-):
+class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin):
     r"""
     Pipeline for zero-shot text-to-video generation using Stable Diffusion.
 
@@ -833,7 +831,7 @@ class TextToVideoZeroPipeline(
         """
         # set lora scale so that monkey patched LoRA
         # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
@@ -966,7 +964,7 @@ class TextToVideoZeroPipeline(
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
         if self.text_encoder is not None:
-            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+            if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
                 # Retrieve the original scale by scaling back the LoRA layers
                 unscale_lora_layers(self.text_encoder, lora_scale)
 

src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py

@@ -14,7 +14,7 @@ from transformers import (
 )
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
@@ -422,7 +422,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
         """
         # set lora scale so that monkey patched LoRA
         # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
@@ -555,7 +555,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
         if self.text_encoder is not None:
-            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+            if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
                 # Retrieve the original scale by scaling back the LoRA layers
                 unscale_lora_layers(self.text_encoder, lora_scale)
 

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py

@@ -20,7 +20,7 @@ import numpy as np
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer
 
-from ...loaders import StableDiffusionLoraLoaderMixin
+from ...loaders import LoraLoaderMixin
 from ...schedulers import DDPMWuerstchenScheduler
 from ...utils import BaseOutput, deprecate, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
@@ -62,7 +62,7 @@ class WuerstchenPriorPipelineOutput(BaseOutput):
     image_embeddings: Union[torch.Tensor, np.ndarray]
 
 
-class WuerstchenPriorPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
+class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
     """
     Pipeline for generating image prior for Wuerstchen.
 
@@ -70,8 +70,8 @@ class WuerstchenPriorPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin)
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
 
     The pipeline also inherits the following loading methods:
-        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
 
     Args:
         prior ([`Prior`]):
@@ -95,7 +95,6 @@ class WuerstchenPriorPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin)
     text_encoder_name = "text_encoder"
     model_cpu_offload_seq = "text_encoder->prior"
     _callback_tensor_inputs = ["latents", "text_encoder_hidden_states", "negative_prompt_embeds"]
-    _lora_loadable_modules = ["prior", "text_encoder"]
 
     def __init__(
         self,

tests/lora/test_lora_layers_sd3.py

@@ -12,55 +12,376 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import os
 import sys
+import tempfile
 import unittest
 
+import numpy as np
+import torch
+from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
+
 from diffusers import (
+    AutoencoderKL,
     FlowMatchEulerDiscreteScheduler,
+    SD3Transformer2DModel,
     StableDiffusion3Pipeline,
 )
 from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, require_torch_gpu, torch_device
 
 
 if is_peft_available():
-    pass
+    from peft import LoraConfig
+    from peft.utils import get_peft_model_state_dict
 
 sys.path.append(".")
 
-from utils import PeftLoraLoaderMixinTests  # noqa: E402
+from utils import check_if_lora_correctly_set  # noqa: E402
 
 
 @require_peft_backend
-class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
+class SD3LoRATests(unittest.TestCase):
     pipeline_class = StableDiffusion3Pipeline
-    scheduler_cls = FlowMatchEulerDiscreteScheduler()
-    scheduler_kwargs = {}
-    transformer_kwargs = {
-        "sample_size": 32,
-        "patch_size": 1,
-        "in_channels": 4,
-        "num_layers": 1,
-        "attention_head_dim": 8,
-        "num_attention_heads": 4,
-        "caption_projection_dim": 32,
-        "joint_attention_dim": 32,
-        "pooled_projection_dim": 64,
-        "out_channels": 4,
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = SD3Transformer2DModel(
+            sample_size=32,
+            patch_size=1,
+            in_channels=4,
+            num_layers=1,
+            attention_head_dim=8,
+            num_attention_heads=4,
+            caption_projection_dim=32,
+            joint_attention_dim=32,
+            pooled_projection_dim=64,
+            out_channels=4,
+        )
+        clip_text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+
+        torch.manual_seed(0)
+        text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)
+
+        torch.manual_seed(0)
+        text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
+
+        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            block_out_channels=(4,),
+            layers_per_block=1,
+            latent_channels=4,
+            norm_num_groups=1,
+            use_quant_conv=False,
+            use_post_quant_conv=False,
+            shift_factor=0.0609,
+            scaling_factor=1.5035,
+        )
+
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        return {
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "text_encoder_2": text_encoder_2,
+            "text_encoder_3": text_encoder_3,
+            "tokenizer": tokenizer,
+            "tokenizer_2": tokenizer_2,
+            "tokenizer_3": tokenizer_3,
+            "transformer": transformer,
+            "vae": vae,
         }
-    vae_kwargs = {
-        "sample_size": 32,
-        "in_channels": 3,
-        "out_channels": 3,
-        "block_out_channels": (4,),
-        "layers_per_block": 1,
-        "latent_channels": 4,
-        "norm_num_groups": 1,
-        "use_quant_conv": False,
-        "use_post_quant_conv": False,
-        "shift_factor": 0.0609,
-        "scaling_factor": 1.5035,
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "output_type": "np",
         }
-    has_three_text_encoders = True
+        return inputs
+
+    def get_lora_config_for_transformer(self):
+        lora_config = LoraConfig(
+            r=4,
+            lora_alpha=4,
+            target_modules=["to_q", "to_k", "to_v", "to_out.0"],
+            init_lora_weights=False,
+            use_dora=False,
+        )
+        return lora_config
+
+    def get_lora_config_for_text_encoders(self):
+        text_lora_config = LoraConfig(
+            r=4,
+            lora_alpha=4,
+            init_lora_weights="gaussian",
+            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+        )
+        return text_lora_config
+
+    def test_simple_inference_with_transformer_lora_save_load(self):
+        components = self.get_dummy_components()
+        transformer_config = self.get_lora_config_for_transformer()
+
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        pipe.transformer.add_adapter(transformer_config)
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        inputs = self.get_dummy_inputs(torch_device)
+        images_lora = pipe(**inputs).images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            transformer_state_dict = get_peft_model_state_dict(pipe.transformer)
+
+            self.pipeline_class.save_lora_weights(
+                save_directory=tmpdirname,
+                transformer_lora_layers=transformer_state_dict,
+            )
+
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
+            pipe.unload_lora_weights()
+
+            pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
+
+        inputs = self.get_dummy_inputs(torch_device)
+        images_lora_from_pretrained = pipe(**inputs).images
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+
+        self.assertTrue(
+            np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+            "Loading from saved checkpoints should give same results.",
+        )
+
+    def test_simple_inference_with_clip_encoders_lora_save_load(self):
+        components = self.get_dummy_components()
+        transformer_config = self.get_lora_config_for_transformer()
+        text_encoder_config = self.get_lora_config_for_text_encoders()
+
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        pipe.transformer.add_adapter(transformer_config)
+        pipe.text_encoder.add_adapter(text_encoder_config)
+        pipe.text_encoder_2.add_adapter(text_encoder_config)
+
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder.")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2.")
+
+        inputs = self.get_dummy_inputs(torch_device)
+        images_lora = pipe(**inputs).images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            transformer_state_dict = get_peft_model_state_dict(pipe.transformer)
+            text_encoder_one_state_dict = get_peft_model_state_dict(pipe.text_encoder)
+            text_encoder_two_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
+
+            self.pipeline_class.save_lora_weights(
+                save_directory=tmpdirname,
+                transformer_lora_layers=transformer_state_dict,
+                text_encoder_lora_layers=text_encoder_one_state_dict,
+                text_encoder_2_lora_layers=text_encoder_two_state_dict,
+            )
+
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
+            pipe.unload_lora_weights()
+
+            pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
+
+        inputs = self.get_dummy_inputs(torch_device)
+        images_lora_from_pretrained = pipe(**inputs).images
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text_encoder_one")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text_encoder_two")
+
+        self.assertTrue(
+            np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+            "Loading from saved checkpoints should give same results.",
+        )
+
+    def test_simple_inference_with_transformer_lora_and_scale(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)
+        output_lora = pipe(**inputs).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
+        )
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_lora_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.5}).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
+            "Lora + scale should change the output",
+        )
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_lora_0_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.0}).images
+        self.assertTrue(
+            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_clip_encoders_lora_and_scale(self):
+        components = self.get_dummy_components()
+        transformer_lora_config = self.get_lora_config_for_transformer()
+        text_encoder_config = self.get_lora_config_for_text_encoders()
+        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)
+        pipe.text_encoder.add_adapter(text_encoder_config)
+        pipe.text_encoder_2.add_adapter(text_encoder_config)
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text_encoder_one")
+        self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text_encoder_two")
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_lora = pipe(**inputs).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
+        )
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_lora_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.5}).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
+            "Lora + scale should change the output",
+        )
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_lora_0_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.0}).images
+        self.assertTrue(
+            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"
+        )
 
     @require_torch_gpu
     def test_sd3_lora(self):

tests/lora/utils.py

@@ -19,14 +19,12 @@ from itertools import product
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
-    FlowMatchEulerDiscreteScheduler,
     LCMScheduler,
-    SD3Transformer2DModel,
     UNet2DConditionModel,
 )
 from diffusers.utils.import_utils import is_peft_available
@@ -73,32 +71,21 @@ class PeftLoraLoaderMixinTests:
     scheduler_cls = None
     scheduler_kwargs = None
     has_two_text_encoders = False
-    has_three_text_encoders = False
     unet_kwargs = None
-    transformer_kwargs = None
     vae_kwargs = None
 
     def get_dummy_components(self, scheduler_cls=None, use_dora=False):
-        if self.unet_kwargs and self.transformer_kwargs:
-            raise ValueError("Both `unet_kwargs` and `transformer_kwargs` cannot be specified.")
-        if self.has_two_text_encoders and self.has_three_text_encoders:
-            raise ValueError("Both `has_two_text_encoders` and `has_three_text_encoders` cannot be True.")
-
         scheduler_cls = self.scheduler_cls if scheduler_cls is None else scheduler_cls
         rank = 4
 
         torch.manual_seed(0)
-        if self.unet_kwargs is not None:
         unet = UNet2DConditionModel(**self.unet_kwargs)
-        else:
-            transformer = SD3Transformer2DModel(**self.transformer_kwargs)
 
         scheduler = scheduler_cls(**self.scheduler_kwargs)
 
         torch.manual_seed(0)
         vae = AutoencoderKL(**self.vae_kwargs)
 
-        if not self.has_three_text_encoders:
         text_encoder = CLIPTextModel.from_pretrained("peft-internal-testing/tiny-clip-text-2")
         tokenizer = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")
 
@@ -106,14 +93,6 @@ class PeftLoraLoaderMixinTests:
             text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("peft-internal-testing/tiny-clip-text-2")
             tokenizer_2 = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")
 
-        if self.has_three_text_encoders:
-            tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-            tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-            tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
-            text_encoder = CLIPTextModelWithProjection.from_pretrained("hf-internal-testing/tiny-sd3-text_encoder")
-            text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("hf-internal-testing/tiny-sd3-text_encoder-2")
-            text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
-
         text_lora_config = LoraConfig(
             r=rank,
             lora_alpha=rank,
@@ -122,7 +101,7 @@ class PeftLoraLoaderMixinTests:
             use_dora=use_dora,
         )
 
-        denoiser_lora_config = LoraConfig(
+        unet_lora_config = LoraConfig(
             r=rank,
             lora_alpha=rank,
             target_modules=["to_q", "to_k", "to_v", "to_out.0"],
@@ -130,8 +109,7 @@ class PeftLoraLoaderMixinTests:
             use_dora=use_dora,
         )
 
-        if self.has_two_text_encoders or self.has_three_text_encoders:
-            if self.unet_kwargs is not None:
+        if self.has_two_text_encoders:
             pipeline_components = {
                 "unet": unet,
                 "scheduler": scheduler,
@@ -143,18 +121,6 @@ class PeftLoraLoaderMixinTests:
                 "image_encoder": None,
                 "feature_extractor": None,
             }
-            elif self.has_three_text_encoders and self.transformer_kwargs is not None:
-                pipeline_components = {
-                    "transformer": transformer,
-                    "scheduler": scheduler,
-                    "vae": vae,
-                    "text_encoder": text_encoder,
-                    "tokenizer": tokenizer,
-                    "text_encoder_2": text_encoder_2,
-                    "tokenizer_2": tokenizer_2,
-                    "text_encoder_3": text_encoder_3,
-                    "tokenizer_3": tokenizer_3,
-                }
         else:
             pipeline_components = {
                 "unet": unet,
@@ -167,7 +133,7 @@ class PeftLoraLoaderMixinTests:
                 "image_encoder": None,
             }
 
-        return pipeline_components, text_lora_config, denoiser_lora_config
+        return pipeline_components, text_lora_config, unet_lora_config
 
     def get_dummy_inputs(self, with_generator=True):
         batch_size = 1
@@ -204,12 +170,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple inference and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -217,20 +178,14 @@ class PeftLoraLoaderMixinTests:
 
             _, _, inputs = self.get_dummy_inputs()
             output_no_lora = pipe(**inputs).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
     def test_simple_inference_with_text_lora(self):
         """
         Tests a simple inference with lora attached on the text encoder
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -238,13 +193,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -260,12 +214,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached on the text encoder + scale argument
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -273,13 +222,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -290,27 +238,17 @@ class PeftLoraLoaderMixinTests:
                 not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
             )
 
-            if self.unet_kwargs is not None:
             output_lora_scale = pipe(
                 **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
             ).images
-            else:
-                output_lora_scale = pipe(
-                    **inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.5}
-                ).images
             self.assertTrue(
                 not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
                 "Lora + scale should change the output",
             )
 
-            if self.unet_kwargs is not None:
             output_lora_0_scale = pipe(
                 **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
             ).images
-            else:
-                output_lora_0_scale = pipe(
-                    **inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.0}
-                ).images
             self.assertTrue(
                 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",
@@ -321,12 +259,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -334,13 +267,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -350,7 +282,7 @@ class PeftLoraLoaderMixinTests:
             # Fusing should still keep the LoRA layers
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                 )
@@ -365,12 +297,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached to text encoder, then unloads the lora weights
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -378,13 +305,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -396,7 +322,7 @@ class PeftLoraLoaderMixinTests:
                 check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
             )
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertFalse(
                     check_if_lora_correctly_set(pipe.text_encoder_2),
                     "Lora not correctly unloaded in text encoder 2",
@@ -412,12 +338,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple usecase where users could use saving utilities for LoRA.
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -425,13 +346,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -441,7 +361,7 @@ class PeftLoraLoaderMixinTests:
 
             with tempfile.TemporaryDirectory() as tmpdirname:
                 text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
-                if self.has_two_text_encoders or self.has_three_text_encoders:
+                if self.has_two_text_encoders:
                     text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
 
                     self.pipeline_class.save_lora_weights(
@@ -465,7 +385,7 @@ class PeftLoraLoaderMixinTests:
             images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                 )
@@ -481,14 +401,9 @@ class PeftLoraLoaderMixinTests:
         with different ranks and some adapters removed
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, _, _ = self.get_dummy_components(scheduler_cls)
-            # Verify `StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder` handles different ranks per module (PR#8324).
+            # Verify `LoraLoaderMixin.load_lora_into_text_encoder` handles different ranks per module (PR#8324).
             text_lora_config = LoraConfig(
                 r=4,
                 rank_pattern={"q_proj": 1, "k_proj": 2, "v_proj": 3},
@@ -503,8 +418,7 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
@@ -516,7 +430,7 @@ class PeftLoraLoaderMixinTests:
                 if "text_model.encoder.layers.4" not in module_name
             }
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -548,12 +462,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
@@ -561,13 +470,12 @@ class PeftLoraLoaderMixinTests:
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -586,7 +494,7 @@ class PeftLoraLoaderMixinTests:
                 "Lora not correctly set in text encoder",
             )
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe_from_pretrained.text_encoder_2),
                     "Lora not correctly set in text encoder 2",
@@ -599,42 +507,27 @@ class PeftLoraLoaderMixinTests:
                 "Loading from saved checkpoints should give same results.",
             )
 
-    def test_simple_inference_with_text_denoiser_lora_save_load(self):
+    def test_simple_inference_with_text_unet_lora_save_load(self):
         """
         Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in Unet")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -644,36 +537,22 @@ class PeftLoraLoaderMixinTests:
 
             with tempfile.TemporaryDirectory() as tmpdirname:
                 text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
-
-                if self.unet_kwargs is not None:
-                    denoiser_state_dict = get_peft_model_state_dict(pipe.unet)
-                else:
-                    denoiser_state_dict = get_peft_model_state_dict(pipe.transformer)
-
-                if self.has_two_text_encoders or self.has_three_text_encoders:
+                unet_state_dict = get_peft_model_state_dict(pipe.unet)
+                if self.has_two_text_encoders:
                     text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
 
-                    if self.unet_kwargs is not None:
-                        self.pipeline_class.save_lora_weights(
-                            save_directory=tmpdirname,
-                            text_encoder_lora_layers=text_encoder_state_dict,
-                            text_encoder_2_lora_layers=text_encoder_2_state_dict,
-                            unet_lora_layers=denoiser_state_dict,
-                            safe_serialization=False,
-                        )
-                    else:
                     self.pipeline_class.save_lora_weights(
                         save_directory=tmpdirname,
                         text_encoder_lora_layers=text_encoder_state_dict,
                         text_encoder_2_lora_layers=text_encoder_2_state_dict,
-                            transformer_lora_layers=denoiser_state_dict,
+                        unet_lora_layers=unet_state_dict,
                         safe_serialization=False,
                     )
                 else:
                     self.pipeline_class.save_lora_weights(
                         save_directory=tmpdirname,
                         text_encoder_lora_layers=text_encoder_state_dict,
-                        unet_lora_layers=denoiser_state_dict,
+                        unet_lora_layers=unet_state_dict,
                         safe_serialization=False,
                     )
 
@@ -684,10 +563,9 @@ class PeftLoraLoaderMixinTests:
 
             images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                 )
@@ -697,37 +575,27 @@ class PeftLoraLoaderMixinTests:
                 "Loading from saved checkpoints should give same results.",
             )
 
-    def test_simple_inference_with_text_denoiser_lora_and_scale(self):
+    def test_simple_inference_with_text_unet_lora_and_scale(self):
         """
         Tests a simple inference with lora attached on the text encoder + Unet + scale argument
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -738,27 +606,17 @@ class PeftLoraLoaderMixinTests:
                 not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
             )
 
-            if self.unet_kwargs is not None:
             output_lora_scale = pipe(
                 **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
             ).images
-            else:
-                output_lora_scale = pipe(
-                    **inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.5}
-                ).images
             self.assertTrue(
                 not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
                 "Lora + scale should change the output",
             )
 
-            if self.unet_kwargs is not None:
             output_lora_0_scale = pipe(
                 **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
             ).images
-            else:
-                output_lora_0_scale = pipe(
-                    **inputs, generator=torch.manual_seed(0), joint_attention_kwargs={"scale": 0.0}
-                ).images
             self.assertTrue(
                 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",
@@ -769,38 +627,28 @@ class PeftLoraLoaderMixinTests:
                 "The scaling parameter has not been correctly restored!",
             )
 
-    def test_simple_inference_with_text_lora_denoiser_fused(self):
+    def test_simple_inference_with_text_lora_unet_fused(self):
         """
         Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
         and makes sure it works as expected - with unet
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -809,10 +657,9 @@ class PeftLoraLoaderMixinTests:
             pipe.fuse_lora()
             # Fusing should still keep the LoRA layers
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                 )
@@ -822,37 +669,27 @@ class PeftLoraLoaderMixinTests:
                 np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
             )
 
-    def test_simple_inference_with_text_denoiser_lora_unloaded(self):
+    def test_simple_inference_with_text_unet_lora_unloaded(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -863,12 +700,9 @@ class PeftLoraLoaderMixinTests:
             self.assertFalse(
                 check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
             )
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertFalse(
-                check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly unloaded in denoiser"
-            )
+            self.assertFalse(check_if_lora_correctly_set(pipe.unet), "Lora not correctly unloaded in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertFalse(
                     check_if_lora_correctly_set(pipe.text_encoder_2),
                     "Lora not correctly unloaded in text encoder 2",
@@ -880,34 +714,25 @@ class PeftLoraLoaderMixinTests:
                 "Fused lora should change the output",
             )
 
-    def test_simple_inference_with_text_denoiser_lora_unfused(self):
+    def test_simple_inference_with_text_unet_lora_unfused(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -922,10 +747,9 @@ class PeftLoraLoaderMixinTests:
             output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
             # unloading should remove the LoRA layers
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Unfuse should still keep LoRA layers")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Unfuse should still keep LoRA layers")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Unfuse should still keep LoRA layers"
                 )
@@ -936,18 +760,13 @@ class PeftLoraLoaderMixinTests:
                 "Fused lora should change the output",
             )
 
-    def test_simple_inference_with_text_denoiser_multi_adapter(self):
+    def test_simple_inference_with_text_unet_multi_adapter(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches
         multiple adapters and set them
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -958,20 +777,13 @@ class PeftLoraLoaderMixinTests:
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                 self.assertTrue(
@@ -1014,21 +826,13 @@ class PeftLoraLoaderMixinTests:
                 "output with no lora and output with lora disabled should give same results",
             )
 
-    def test_simple_inference_with_text_denoiser_block_scale(self):
+    def test_simple_inference_with_text_unet_block_scale(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches
         one adapter and set differnt weights for different blocks (i.e. block lora)
         """
-        if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
-            return
-
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -1037,16 +841,12 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -1081,21 +881,13 @@ class PeftLoraLoaderMixinTests:
                 "output with no lora and output with lora disabled should give same results",
             )
 
-    def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
+    def test_simple_inference_with_text_unet_multi_adapter_block_lora(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches
         multiple adapters and set differnt weights for different blocks (i.e. block lora)
         """
-        if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
-            return
-
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -1106,20 +898,13 @@ class PeftLoraLoaderMixinTests:
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                 self.assertTrue(
@@ -1168,10 +953,8 @@ class PeftLoraLoaderMixinTests:
             with self.assertRaises(ValueError):
                 pipe.set_adapters(["adapter-1", "adapter-2"], [scales_1])
 
-    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
+    def test_simple_inference_with_text_unet_block_scale_for_all_dict_options(self):
         """Tests that any valid combination of lora block scales can be used in pipe.set_adapter"""
-        if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
-            return
 
         def updown_options(blocks_with_tf, layers_per_block, value):
             """
@@ -1236,19 +1019,16 @@ class PeftLoraLoaderMixinTests:
 
             return opts
 
-        components, text_lora_config, denoiser_lora_config = self.get_dummy_components(self.scheduler_cls)
+        components, text_lora_config, unet_lora_config = self.get_dummy_components(self.scheduler_cls)
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
         pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-        if self.unet_kwargs is not None:
-            pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-        else:
-            pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
-        if self.has_two_text_encoders or self.has_three_text_encoders:
+        if self.has_two_text_encoders:
             pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
 
         for scale_dict in all_possible_dict_opts(pipe.unet, value=1234):
@@ -1258,18 +1038,13 @@ class PeftLoraLoaderMixinTests:
 
             pipe.set_adapters("adapter-1", scale_dict)  # test will fail if this line throws an error
 
-    def test_simple_inference_with_text_denoiser_multi_adapter_delete_adapter(self):
+    def test_simple_inference_with_text_unet_multi_adapter_delete_adapter(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches
         multiple adapters and set/delete them
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -1280,20 +1055,13 @@ class PeftLoraLoaderMixinTests:
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                 self.assertTrue(
@@ -1345,14 +1113,8 @@ class PeftLoraLoaderMixinTests:
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             pipe.set_adapters(["adapter-1", "adapter-2"])
             pipe.delete_adapters(["adapter-1", "adapter-2"])
@@ -1364,18 +1126,13 @@ class PeftLoraLoaderMixinTests:
                 "output with no lora and output with lora disabled should give same results",
             )
 
-    def test_simple_inference_with_text_denoiser_multi_adapter_weighted(self):
+    def test_simple_inference_with_text_unet_multi_adapter_weighted(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches
         multiple adapters and set them
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -1386,20 +1143,13 @@ class PeftLoraLoaderMixinTests:
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                 self.assertTrue(
@@ -1452,13 +1202,8 @@ class PeftLoraLoaderMixinTests:
 
     @skip_mps
     def test_lora_fuse_nan(self):
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
@@ -1466,23 +1211,16 @@ class PeftLoraLoaderMixinTests:
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
             # corrupt one LoRA weight with `inf` values
             with torch.no_grad():
-                if self.unet_kwargs:
-                    pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A[
-                        "adapter-1"
-                    ].weight += float("inf")
-                else:
-                    pipe.transformer.transformer_blocks[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
+                pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float(
+                    "inf"
+                )
 
             # with `safe_fusing=True` we should see an Error
             with self.assertRaises(ValueError):
@@ -1500,32 +1238,21 @@ class PeftLoraLoaderMixinTests:
         Tests a simple usecase where we attach multiple adapters and check if the results
         are the expected results
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
             adapter_names = pipe.get_active_adapters()
             self.assertListEqual(adapter_names, ["adapter-1"])
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             adapter_names = pipe.get_active_adapters()
             self.assertListEqual(adapter_names, ["adapter-2"])
@@ -1538,108 +1265,65 @@ class PeftLoraLoaderMixinTests:
         Tests a simple usecase where we attach multiple adapters and check if the results
         are the expected results
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
             adapter_names = pipe.get_list_adapters()
-            dicts_to_be_checked = {"text_encoder": ["adapter-1"]}
-            if self.unet_kwargs is not None:
-                dicts_to_be_checked.update({"unet": ["adapter-1"]})
-            else:
-                dicts_to_be_checked.update({"transformer": ["adapter-1"]})
-            self.assertDictEqual(adapter_names, dicts_to_be_checked)
+            self.assertDictEqual(adapter_names, {"text_encoder": ["adapter-1"], "unet": ["adapter-1"]})
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             adapter_names = pipe.get_list_adapters()
-            dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
-            if self.unet_kwargs is not None:
-                dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2"]})
-            else:
-                dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2"]})
-            self.assertDictEqual(adapter_names, dicts_to_be_checked)
+            self.assertDictEqual(
+                adapter_names, {"text_encoder": ["adapter-1", "adapter-2"], "unet": ["adapter-1", "adapter-2"]}
+            )
 
             pipe.set_adapters(["adapter-1", "adapter-2"])
-            dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
-            if self.unet_kwargs is not None:
-                dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2"]})
-            else:
-                dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2"]})
             self.assertDictEqual(
                 pipe.get_list_adapters(),
-                dicts_to_be_checked,
+                {"unet": ["adapter-1", "adapter-2"], "text_encoder": ["adapter-1", "adapter-2"]},
             )
 
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-3")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-3")
-
-            dicts_to_be_checked = {"text_encoder": ["adapter-1", "adapter-2"]}
-            if self.unet_kwargs is not None:
-                dicts_to_be_checked.update({"unet": ["adapter-1", "adapter-2", "adapter-3"]})
-            else:
-                dicts_to_be_checked.update({"transformer": ["adapter-1", "adapter-2", "adapter-3"]})
-            self.assertDictEqual(pipe.get_list_adapters(), dicts_to_be_checked)
+            pipe.unet.add_adapter(unet_lora_config, "adapter-3")
+            self.assertDictEqual(
+                pipe.get_list_adapters(),
+                {"unet": ["adapter-1", "adapter-2", "adapter-3"], "text_encoder": ["adapter-1", "adapter-2"]},
+            )
 
     @require_peft_version_greater(peft_version="0.6.2")
-    def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
+    def test_simple_inference_with_text_lora_unet_fused_multi(self):
         """
         Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
         and makes sure it works as expected - with unet and multi-adapter case
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-1")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
 
             # Attach a second adapter
             pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config, "adapter-2")
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config, "adapter-2")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-2")
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                 pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                 self.assertTrue(
@@ -1675,35 +1359,23 @@ class PeftLoraLoaderMixinTests:
 
     @require_peft_version_greater(peft_version="0.9.0")
     def test_simple_inference_with_dora(self):
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(
-                scheduler_cls, use_dora=True
-            )
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls, use_dora=True)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)
             pipe.set_progress_bar_config(disable=None)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             output_no_dora_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
-            shape_to_be_checked = (1, 64, 64, 3) if self.unet_kwargs is not None else (1, 32, 32, 3)
-            self.assertTrue(output_no_dora_lora.shape == shape_to_be_checked)
+            self.assertTrue(output_no_dora_lora.shape == (1, 64, 64, 3))
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -1717,34 +1389,25 @@ class PeftLoraLoaderMixinTests:
             )
 
     @unittest.skip("This is failing for now - need to investigate")
-    def test_simple_inference_with_text_denoiser_lora_unfused_torch_compile(self):
+    def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
         and makes sure it works as expected
         """
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
-            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, text_lora_config, unet_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)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
             pipe.text_encoder.add_adapter(text_lora_config)
-            if self.unet_kwargs is not None:
-                pipe.unet.add_adapter(denoiser_lora_config)
-            else:
-                pipe.transformer.add_adapter(denoiser_lora_config)
+            pipe.unet.add_adapter(unet_lora_config)
 
             self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            denoiser_to_checked = pipe.unet if self.unet_kwargs is not None else pipe.transformer
-            self.assertTrue(check_if_lora_correctly_set(denoiser_to_checked), "Lora not correctly set in denoiser")
+            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2.add_adapter(text_lora_config)
                 self.assertTrue(
                     check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
@@ -1753,27 +1416,19 @@ class PeftLoraLoaderMixinTests:
             pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
             pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
+            if self.has_two_text_encoders:
                 pipe.text_encoder_2 = torch.compile(pipe.text_encoder_2, mode="reduce-overhead", fullgraph=True)
 
             # Just makes sure it works..
             _ = pipe(**inputs, generator=torch.manual_seed(0)).images
 
     def test_modify_padding_mode(self):
-        if self.pipeline_class.__name__ == "StableDiffusion3Pipeline":
-            return
-
         def set_pad_mode(network, mode="circular"):
             for _, module in network.named_modules():
                 if isinstance(module, torch.nn.Conv2d):
                     module.padding_mode = mode
 
-        scheduler_classes = (
-            [FlowMatchEulerDiscreteScheduler]
-            if self.has_three_text_encoders and self.transformer_kwargs
-            else [DDIMScheduler, LCMScheduler]
-        )
-        for scheduler_cls in scheduler_classes:
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
             components, _, _ = self.get_dummy_components(scheduler_cls)
             pipe = self.pipeline_class(**components)
             pipe = pipe.to(torch_device)