3333 UNet2DConditionModel ,
3434)
3535from diffusers .utils .testing_utils import (
36+ backend_empty_cache ,
3637 enable_full_determinism ,
3738 floats_tensor ,
3839 load_image ,
39- require_torch_gpu ,
40+ require_torch_accelerator ,
4041 slow ,
4142 torch_device ,
4243)
@@ -395,17 +396,17 @@ def test_marigold_depth_dummy_no_processing_resolution(self):
395396
396397
397398@slow
398- @require_torch_gpu
399+ @require_torch_accelerator
399400class MarigoldIntrinsicsPipelineIntegrationTests (unittest .TestCase ):
400401 def setUp (self ):
401402 super ().setUp ()
402403 gc .collect ()
403- torch . cuda . empty_cache ( )
404+ backend_empty_cache ( torch_device )
404405
405406 def tearDown (self ):
406407 super ().tearDown ()
407408 gc .collect ()
408- torch . cuda . empty_cache ( )
409+ backend_empty_cache ( torch_device )
409410
410411 def _test_marigold_intrinsics (
411412 self ,
@@ -424,7 +425,7 @@ def _test_marigold_intrinsics(
424425 from_pretrained_kwargs ["torch_dtype" ] = torch .float16
425426
426427 pipe = MarigoldIntrinsicsPipeline .from_pretrained (model_id , ** from_pretrained_kwargs )
427- if device == "cuda" :
428+ if device in [ "cuda" , "xpu" ] :
428429 pipe .enable_model_cpu_offload ()
429430 pipe .set_progress_bar_config (disable = None )
430431
@@ -464,10 +465,10 @@ def test_marigold_intrinsics_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):
464465 match_input_resolution = True ,
465466 )
466467
467- def test_marigold_intrinsics_einstein_f32_cuda_G0_S1_P768_E1_B1_M1 (self ):
468+ def test_marigold_intrinsics_einstein_f32_accelerator_G0_S1_P768_E1_B1_M1 (self ):
468469 self ._test_marigold_intrinsics (
469470 is_fp16 = False ,
470- device = "cuda" ,
471+ device = torch_device ,
471472 generator_seed = 0 ,
472473 expected_slice = np .array ([0.62127 , 0.61906 , 0.61687 , 0.61946 , 0.61903 , 0.61961 , 0.61808 , 0.62099 , 0.62894 ]),
473474 num_inference_steps = 1 ,
@@ -477,10 +478,10 @@ def test_marigold_intrinsics_einstein_f32_cuda_G0_S1_P768_E1_B1_M1(self):
477478 match_input_resolution = True ,
478479 )
479480
480- def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E1_B1_M1 (self ):
481+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P768_E1_B1_M1 (self ):
481482 self ._test_marigold_intrinsics (
482483 is_fp16 = True ,
483- device = "cuda" ,
484+ device = torch_device ,
484485 generator_seed = 0 ,
485486 expected_slice = np .array ([0.62109 , 0.61914 , 0.61719 , 0.61963 , 0.61914 , 0.61963 , 0.61816 , 0.62109 , 0.62891 ]),
486487 num_inference_steps = 1 ,
@@ -490,10 +491,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E1_B1_M1(self):
490491 match_input_resolution = True ,
491492 )
492493
493- def test_marigold_intrinsics_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1 (self ):
494+ def test_marigold_intrinsics_einstein_f16_accelerator_G2024_S1_P768_E1_B1_M1 (self ):
494495 self ._test_marigold_intrinsics (
495496 is_fp16 = True ,
496- device = "cuda" ,
497+ device = torch_device ,
497498 generator_seed = 2024 ,
498499 expected_slice = np .array ([0.64111 , 0.63916 , 0.63623 , 0.63965 , 0.63916 , 0.63965 , 0.6377 , 0.64062 , 0.64941 ]),
499500 num_inference_steps = 1 ,
@@ -503,10 +504,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1(self):
503504 match_input_resolution = True ,
504505 )
505506
506- def test_marigold_intrinsics_einstein_f16_cuda_G0_S2_P768_E1_B1_M1 (self ):
507+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S2_P768_E1_B1_M1 (self ):
507508 self ._test_marigold_intrinsics (
508509 is_fp16 = True ,
509- device = "cuda" ,
510+ device = torch_device ,
510511 generator_seed = 0 ,
511512 expected_slice = np .array ([0.60254 , 0.60059 , 0.59961 , 0.60156 , 0.60107 , 0.60205 , 0.60254 , 0.60449 , 0.61133 ]),
512513 num_inference_steps = 2 ,
@@ -516,10 +517,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G0_S2_P768_E1_B1_M1(self):
516517 match_input_resolution = True ,
517518 )
518519
519- def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P512_E1_B1_M1 (self ):
520+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P512_E1_B1_M1 (self ):
520521 self ._test_marigold_intrinsics (
521522 is_fp16 = True ,
522- device = "cuda" ,
523+ device = torch_device ,
523524 generator_seed = 0 ,
524525 expected_slice = np .array ([0.64551 , 0.64453 , 0.64404 , 0.64502 , 0.64844 , 0.65039 , 0.64502 , 0.65039 , 0.65332 ]),
525526 num_inference_steps = 1 ,
@@ -529,10 +530,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P512_E1_B1_M1(self):
529530 match_input_resolution = True ,
530531 )
531532
532- def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E3_B1_M1 (self ):
533+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P768_E3_B1_M1 (self ):
533534 self ._test_marigold_intrinsics (
534535 is_fp16 = True ,
535- device = "cuda" ,
536+ device = torch_device ,
536537 generator_seed = 0 ,
537538 expected_slice = np .array ([0.61572 , 0.61377 , 0.61182 , 0.61426 , 0.61377 , 0.61426 , 0.61279 , 0.61572 , 0.62354 ]),
538539 num_inference_steps = 1 ,
@@ -543,10 +544,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E3_B1_M1(self):
543544 match_input_resolution = True ,
544545 )
545546
546- def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E4_B2_M1 (self ):
547+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P768_E4_B2_M1 (self ):
547548 self ._test_marigold_intrinsics (
548549 is_fp16 = True ,
549- device = "cuda" ,
550+ device = torch_device ,
550551 generator_seed = 0 ,
551552 expected_slice = np .array ([0.61914 , 0.6167 , 0.61475 , 0.61719 , 0.61719 , 0.61768 , 0.61572 , 0.61914 , 0.62695 ]),
552553 num_inference_steps = 1 ,
@@ -557,10 +558,10 @@ def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P768_E4_B2_M1(self):
557558 match_input_resolution = True ,
558559 )
559560
560- def test_marigold_intrinsics_einstein_f16_cuda_G0_S1_P512_E1_B1_M0 (self ):
561+ def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P512_E1_B1_M0 (self ):
561562 self ._test_marigold_intrinsics (
562563 is_fp16 = True ,
563- device = "cuda" ,
564+ device = torch_device ,
564565 generator_seed = 0 ,
565566 expected_slice = np .array ([0.65332 , 0.64697 , 0.64648 , 0.64844 , 0.64697 , 0.64111 , 0.64941 , 0.64209 , 0.65332 ]),
566567 num_inference_steps = 1 ,
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