update

* support device type device_maps to work with offloading.

* add tests.

* fix tests

* skip tests where it's not supported.

* empty

* up

* up

* fix allegro.

src/diffusers/pipelines/pipeline_utils.py

@@ -112,7 +112,7 @@ LIBRARIES = []
 for library in LOADABLE_CLASSES:
     LIBRARIES.append(library)
 
-SUPPORTED_DEVICE_MAP = ["balanced"] + [get_device()]
+SUPPORTED_DEVICE_MAP = ["balanced"] + [get_device(), "cpu"]
 
 logger = logging.get_logger(__name__)
 
@@ -468,8 +468,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         pipeline_is_sequentially_offloaded = any(
             module_is_sequentially_offloaded(module) for _, module in self.components.items()
         )
-
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline which doesn't allow explicit device placement using `to()`. You can call `reset_device_map()` to remove the existing device map from the pipeline."
@@ -1188,7 +1187,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         """
         self._maybe_raise_error_if_group_offload_active(raise_error=True)
 
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_model_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_model_cpu_offload()`."
@@ -1312,7 +1311,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
         self.remove_all_hooks()
 
-        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self._is_pipeline_device_mapped()
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_sequential_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_sequential_cpu_offload()`."
@@ -2228,6 +2227,21 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 return True
         return False
 
+    def _is_pipeline_device_mapped(self):
+        # We support passing `device_map="cuda"`, for example. This is helpful, in case
+        # users want to pass `device_map="cpu"` when initializing a pipeline. This explicit declaration is desirable
+        # in limited VRAM environments because quantized models often initialize directly on the accelerator.
+        device_map = self.hf_device_map
+        is_device_type_map = False
+        if isinstance(device_map, str):
+            try:
+                torch.device(device_map)
+                is_device_type_map = True
+            except RuntimeError:
+                pass
+
+        return not is_device_type_map and isinstance(device_map, dict) and len(device_map) > 1
+
 
 class StableDiffusionMixin:
     r"""

tests/models/testing_utils/compile.py

@@ -81,7 +81,7 @@ class TorchCompileTesterMixin:
             _ = model(**inputs_dict)
 
     @torch.no_grad()
-    def test_torch_compile_repeated_blocks(self):
+    def test_torch_compile_repeated_blocks(self, recompile_limit=1):
         if self.model_class._repeated_blocks is None:
             pytest.skip("Skipping test as the model class doesn't have `_repeated_blocks` set.")
 
@@ -92,7 +92,6 @@ class TorchCompileTesterMixin:
         model.eval()
         model.compile_repeated_blocks(fullgraph=True)
 
-        recompile_limit = 1
         if self.model_class.__name__ == "UNet2DConditionModel":
             recompile_limit = 2
 

tests/models/testing_utils/quantization.py

@@ -628,6 +628,21 @@ class BitsAndBytesTesterMixin(BitsAndBytesConfigMixin, QuantizationTesterMixin):
         """Test that quantized models can be used for training with adapters."""
         self._test_quantization_training(BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"])
 
+    @pytest.mark.parametrize(
+        "config_name",
+        list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
+        ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
+    )
+    def test_cpu_device_map(self, config_name):
+        config_kwargs = BitsAndBytesConfigMixin.BNB_CONFIGS[config_name]
+        model_quantized = self._create_quantized_model(config_kwargs, device_map="cpu")
+
+        assert hasattr(model_quantized, "hf_device_map"), "Model should have hf_device_map attribute"
+        assert model_quantized.hf_device_map is not None, "hf_device_map should not be None"
+        assert model_quantized.device == torch.device("cpu"), (
+            f"Model should be on CPU, but is on {model_quantized.device}"
+        )
+
 
 @is_quantization
 @is_quanto

tests/models/transformers/test_models_transformer_wan_vace.py

@@ -147,22 +147,7 @@ class TestWanVACETransformer3DCompile(WanVACETransformer3DTesterConfig, TorchCom
     def test_torch_compile_repeated_blocks(self):
         # WanVACE has two block types (WanTransformerBlock and WanVACETransformerBlock),
         # so we need recompile_limit=2 instead of the default 1.
-        import torch._dynamo
-        import torch._inductor.utils
-
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-
-        model = self.model_class(**init_dict).to(torch_device)
-        model.eval()
-        model.compile_repeated_blocks(fullgraph=True)
-
-        with (
-            torch._inductor.utils.fresh_inductor_cache(),
-            torch._dynamo.config.patch(recompile_limit=2),
-        ):
-            _ = model(**inputs_dict)
-            _ = model(**inputs_dict)
+        super().test_torch_compile_repeated_blocks(recompile_limit=2)
 
 
 class TestWanVACETransformer3DBitsAndBytes(WanVACETransformer3DTesterConfig, BitsAndBytesTesterMixin):

tests/pipelines/allegro/test_allegro.py

@@ -158,6 +158,10 @@ class AllegroPipelineFastTests(PipelineTesterMixin, PyramidAttentionBroadcastTes
     def test_save_load_optional_components(self):
         pass
 
+    @unittest.skip("Decoding without tiling is not yet implemented")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass
+
     def test_inference(self):
         device = "cpu"
 

tests/pipelines/cogvideo/test_cogvideox.py

@@ -18,7 +18,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKLCogVideoX, CogVideoXPipeline, CogVideoXTransformer3DModel, DDIMScheduler
 
@@ -117,7 +117,9 @@ class CogVideoXPipelineFastTests(
 
         torch.manual_seed(0)
         scheduler = DDIMScheduler()
-        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder = T5EncoderModel(config)
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         components = {

tests/pipelines/controlnet_flux/test_controlnet_flux.py

@@ -19,7 +19,7 @@ import unittest
 import numpy as np
 import torch
 from huggingface_hub import hf_hub_download
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from transformers import AutoConfig, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from diffusers import (
     AutoencoderKL,
@@ -97,7 +97,9 @@ class FluxControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin, Fl
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = T5TokenizerFast.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/controlnet_sd3/test_controlnet_sd3.py

@@ -18,7 +18,14 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    CLIPTextConfig,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+)
 
 from diffusers import (
     AutoencoderKL,
@@ -117,7 +124,9 @@ class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTes
         text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_3 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

tests/pipelines/flux/test_pipeline_flux_control.py

@@ -3,7 +3,7 @@ import unittest
 import numpy as np
 import torch
 from PIL import Image
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxControlPipeline, FluxTransformer2DModel
 
@@ -53,7 +53,9 @@ class FluxControlPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_control_img2img.py

@@ -3,7 +3,7 @@ import unittest
 import numpy as np
 import torch
 from PIL import Image
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import (
     AutoencoderKL,
@@ -57,7 +57,9 @@ class FluxControlImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_control_inpaint.py

@@ -3,7 +3,7 @@ import unittest
 import numpy as np
 import torch
 from PIL import Image
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import (
     AutoencoderKL,
@@ -58,7 +58,9 @@ class FluxControlInpaintPipelineFastTests(unittest.TestCase, PipelineTesterMixin
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_fill.py

@@ -3,7 +3,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxFillPipeline, FluxTransformer2DModel
 
@@ -58,7 +58,9 @@ class FluxFillPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_img2img.py

@@ -3,7 +3,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxImg2ImgPipeline, FluxTransformer2DModel
 
@@ -55,7 +55,9 @@ class FluxImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin, FluxI
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_inpaint.py

@@ -3,7 +3,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxInpaintPipeline, FluxTransformer2DModel
 
@@ -55,7 +55,9 @@ class FluxInpaintPipelineFastTests(unittest.TestCase, PipelineTesterMixin, FluxI
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_kontext.py

@@ -3,7 +3,7 @@ import unittest
 import numpy as np
 import PIL.Image
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import (
     AutoencoderKL,
@@ -79,7 +79,9 @@ class FluxKontextPipelineFastTests(
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/flux/test_pipeline_flux_kontext_inpaint.py

@@ -3,7 +3,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import (
     AutoencoderKL,
@@ -79,7 +79,9 @@ class FluxKontextInpaintPipelineFastTests(
         text_encoder = CLIPTextModel(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

tests/pipelines/hidream_image/test_pipeline_hidream.py

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 import torch
 from transformers import (
+    AutoConfig,
     AutoTokenizer,
     CLIPTextConfig,
     CLIPTextModelWithProjection,
@@ -94,7 +95,9 @@ class HiDreamImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
 
         torch.manual_seed(0)
-        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_3 = T5EncoderModel(config)
 
         torch.manual_seed(0)
         text_encoder_4 = LlamaForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")

tests/pipelines/hunyuandit/test_hunyuan_dit.py

@@ -19,7 +19,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, BertModel, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, BertModel, T5EncoderModel
 
 from diffusers import AutoencoderKL, DDPMScheduler, HunyuanDiT2DModel, HunyuanDiTPipeline
 
@@ -74,7 +74,10 @@ class HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         scheduler = DDPMScheduler()
         text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
-        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        torch.manual_seed(0)
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_2 = T5EncoderModel(config)
         tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         components = {

tests/pipelines/kandinsky/test_kandinsky_combined.py

@@ -34,9 +34,7 @@ enable_full_determinism()
 
 class KandinskyPipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyCombinedPipeline
-    params = [
-        "prompt",
-    ]
+    params = ["prompt"]
     batch_params = ["prompt", "negative_prompt"]
     required_optional_params = [
         "generator",
@@ -148,6 +146,10 @@ class KandinskyPipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase)
     def test_dict_tuple_outputs_equivalent(self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)
 
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass
+
 
 class KandinskyPipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyImg2ImgCombinedPipeline
@@ -264,6 +266,10 @@ class KandinskyPipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.Te
     def test_save_load_optional_components(self):
         super().test_save_load_optional_components(expected_max_difference=5e-4)
 
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass
+
 
 class KandinskyPipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyInpaintCombinedPipeline
@@ -384,3 +390,7 @@ class KandinskyPipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.Te
 
     def test_save_load_local(self):
         super().test_save_load_local(expected_max_difference=5e-3)
+
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass

tests/pipelines/kandinsky2_2/test_kandinsky_combined.py

@@ -36,9 +36,7 @@ enable_full_determinism()
 
 class KandinskyV22PipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyV22CombinedPipeline
-    params = [
-        "prompt",
-    ]
+    params = ["prompt"]
     batch_params = ["prompt", "negative_prompt"]
     required_optional_params = [
         "generator",
@@ -70,12 +68,7 @@ class KandinskyV22PipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCa
     def get_dummy_inputs(self, device, seed=0):
         prior_dummy = PriorDummies()
         inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)
-        inputs.update(
-            {
-                "height": 64,
-                "width": 64,
-            }
-        )
+        inputs.update({"height": 64, "width": 64})
         return inputs
 
     def test_kandinsky(self):
@@ -155,12 +148,18 @@ class KandinskyV22PipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCa
     def test_save_load_optional_components(self):
         super().test_save_load_optional_components(expected_max_difference=5e-3)
 
+    @unittest.skip("Test not supported.")
     def test_callback_inputs(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_callback_cfg(self):
         pass
 
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass
+
 
 class KandinskyV22PipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyV22Img2ImgCombinedPipeline
@@ -279,12 +278,18 @@ class KandinskyV22PipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest
     def save_load_local(self):
         super().test_save_load_local(expected_max_difference=5e-3)
 
+    @unittest.skip("Test not supported.")
     def test_callback_inputs(self):
         pass
 
+    @unittest.skip("Test not supported.")
     def test_callback_cfg(self):
         pass
 
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass
+
 
 class KandinskyV22PipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = KandinskyV22InpaintCombinedPipeline
@@ -411,3 +416,7 @@ class KandinskyV22PipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest
 
     def test_callback_cfg(self):
         pass
+
+    @unittest.skip("`device_map` is not yet supported for connected pipelines.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass

tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py

@@ -296,6 +296,9 @@ class KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCas
         output = pipe(**inputs)[0]
         assert output.abs().sum() == 0
 
+    def test_pipeline_with_accelerator_device_map(self):
+        super().test_pipeline_with_accelerator_device_map(expected_max_difference=5e-3)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/kandinsky3/test_kandinsky3_img2img.py

@@ -194,6 +194,9 @@ class Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
     def test_save_load_dduf(self):
         super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
 
+    def test_pipeline_with_accelerator_device_map(self):
+        super().test_pipeline_with_accelerator_device_map(expected_max_difference=5e-3)
+
 
 @slow
 @require_torch_accelerator

tests/pipelines/ltx/test_ltx.py

@@ -17,7 +17,7 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel
 
@@ -88,7 +88,9 @@ class LTXPipelineFastTests(PipelineTesterMixin, FirstBlockCacheTesterMixin, unit
 
         torch.manual_seed(0)
         scheduler = FlowMatchEulerDiscreteScheduler()
-        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder = T5EncoderModel(config)
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         components = {

tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_img2img.py

@@ -4,7 +4,14 @@ import unittest
 
 import numpy as np
 import torch
-from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    CLIPTextConfig,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+)
 
 from diffusers import (
     AutoencoderKL,
@@ -73,7 +80,10 @@ class StableDiffusion3Img2ImgPipelineFastTests(PipelineLatentTesterMixin, unitte
         torch.manual_seed(0)
         text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
 
-        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        torch.manual_seed(0)
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder_3 = T5EncoderModel(config)
 
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

tests/pipelines/test_pipelines_common.py

@@ -2355,7 +2355,6 @@ class PipelineTesterMixin:
                     f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}",
                 )
 
-    @require_torch_accelerator
     def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-4):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)

tests/pipelines/visualcloze/test_pipeline_visualcloze_combined.py

@@ -342,3 +342,7 @@ class VisualClozePipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         self.assertLess(
             max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading."
         )
+
+    @unittest.skip("Test not supported.")
+    def test_pipeline_with_accelerator_device_map(self):
+        pass

tests/pipelines/visualcloze/test_pipeline_visualcloze_generation.py

@@ -310,3 +310,7 @@ class VisualClozeGenerationPipelineFastTests(unittest.TestCase, PipelineTesterMi
     @unittest.skip("Skipped due to missing layout_prompt. Needs further investigation.")
     def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=0.0001, rtol=0.0001):
         pass
+
+    @unittest.skip("Needs to be revisited later.")
+    def test_pipeline_with_accelerator_device_map(self, expected_max_difference=0.0001):
+        pass

tests/pipelines/wan/test_wan_22_image_to_video.py

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 import torch
 from PIL import Image
-from transformers import AutoTokenizer, T5EncoderModel
+from transformers import AutoConfig, AutoTokenizer, T5EncoderModel
 
 from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, WanImageToVideoPipeline, WanTransformer3DModel
 
@@ -64,7 +64,11 @@ class Wan22ImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
 
         torch.manual_seed(0)
         scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0)
-        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder = T5EncoderModel(config)
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         torch.manual_seed(0)
@@ -248,7 +252,11 @@ class Wan225BImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCas
 
         torch.manual_seed(0)
         scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0)
-        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        config.tie_word_embeddings = False
+        text_encoder = T5EncoderModel(config)
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         torch.manual_seed(0)