Upload test_corrections.txt with huggingface_hub

test_corrections.txt

@@ -1,59 +1,3 @@
-tests/pipelines/animatediff/test_animatediff.py;AnimateDiffPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6631, 0.3638, 0.7018,  ..., 0.4205, 0.3786, 0.5323])
-tests/pipelines/animatediff/test_animatediff_video2video.py;AnimateDiffVideoToVideoPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5371, 0.4619, 0.4810,  ..., 0.5299, 0.5213, 0.5017])
-tests/pipelines/controlnet/test_controlnet.py;ControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4028, 0.3965, 0.4546,  ..., 0.5955, 0.3306, 0.4665])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4108, 0.4594, 0.3714,  ..., 0.4975, 0.4487, 0.3804])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetOneModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.3981, 0.3988, 0.4494,  ..., 0.6102, 0.3263, 0.4780])
-tests/pipelines/controlnet/test_controlnet_img2img.py;ControlNetImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5670, 0.4974, 0.5090,  ..., 0.6239, 0.3544, 0.4213])
-tests/pipelines/controlnet/test_controlnet_img2img.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4471, 0.4789, 0.4430,  ..., 0.4075, 0.6176, 0.5182])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionXLControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5472, 0.5017, 0.5610,  ..., 0.4896, 0.5703, 0.5090])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionSSD1BControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5351, 0.4945, 0.5729,  ..., 0.4832, 0.5497, 0.5036])
-tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py;ControlNetPipelineSDXLImg2ImgFastTests;test_ip_adapter_single;expected_slice = np.array([0.5032, 0.4914, 0.4156,  ..., 0.4288, 0.5438, 0.5353])
-tests/pipelines/controlnet/test_controlnet.py;ControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4028, 0.3965, 0.4546,  ..., 0.5955, 0.3306, 0.4665])
-tests/pipelines/controlnet/test_controlnet_img2img.py;ControlNetImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5670, 0.4974, 0.5090,  ..., 0.6239, 0.3544, 0.4213])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4108, 0.4594, 0.3714,  ..., 0.4975, 0.4487, 0.3804])
-tests/pipelines/controlnet/test_controlnet_img2img.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4471, 0.4789, 0.4430,  ..., 0.4075, 0.6176, 0.5182])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetOneModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.3981, 0.3988, 0.4494,  ..., 0.6102, 0.3263, 0.4780])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionXLControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5472, 0.5017, 0.5610,  ..., 0.4896, 0.5703, 0.5090])
-tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py;StableDiffusionInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5252, 0.5157, 0.5404,  ..., 0.5183, 0.3829, 0.4640])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionSSD1BControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5351, 0.4945, 0.5729,  ..., 0.4832, 0.5497, 0.5036])
-tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py;StableDiffusionSimpleInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5193, 0.4510, 0.4699,  ..., 0.4309, 0.5689, 0.5211])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py;StableDiffusionXLAdapterPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5402, 0.5313, 0.4827,  ..., 0.4965, 0.4010, 0.4730])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py;StableDiffusionXLMultiAdapterPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5429, 0.5245, 0.4855,  ..., 0.4962, 0.3978, 0.4718])
-tests/pipelines/animatediff/test_animatediff.py;AnimateDiffPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6631, 0.3638, 0.7018,  ..., 0.4205, 0.3786, 0.5323])
-tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py;ControlNetPipelineSDXLImg2ImgFastTests;test_ip_adapter_single;expected_slice = np.array([0.5032, 0.4914, 0.4156,  ..., 0.4288, 0.5438, 0.5353])
-tests/pipelines/latent_consistency_models/test_latent_consistency_models.py;LatentConsistencyModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4673, 0.5478, 0.4212,  ..., 0.3952, 0.5166, 0.3645])
-tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py;LatentConsistencyModelImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4727, 0.4837, 0.3874,  ..., 0.4643, 0.5034, 0.4417])
-tests/pipelines/animatediff/test_animatediff_video2video.py;AnimateDiffVideoToVideoPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5371, 0.4619, 0.4810,  ..., 0.5299, 0.5213, 0.5017])
-tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4187, 0.4723, 0.6115,  ..., 0.5074, 0.5739, 0.5441])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py;StableDiffusionXLPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5559, 0.5192, 0.5225,  ..., 0.4868, 0.4070, 0.4700])
-tests/pipelines/pia/test_pia.py;PIAPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6319, 0.3310, 0.6785,  ..., 0.4153, 0.3993, 0.4803])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py;StableDiffusionXLImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4268, 0.4839, 0.6225,  ..., 0.4946, 0.5580, 0.5675])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py;StableDiffusionXLInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5139, 0.5046, 0.4932,  ..., 0.4111, 0.6005, 0.5261])
-tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4446, 0.5244, 0.4132,  ..., 0.3766, 0.4483, 0.4045])
-tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py;StableDiffusionPanoramaPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5701, 0.4671, 0.4628,  ..., 0.4668, 0.4121, 0.4758])
-tests/pipelines/controlnet/test_controlnet.py;ControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4028, 0.3965, 0.4546,  ..., 0.5955, 0.3306, 0.4665])
-tests/pipelines/controlnet/test_controlnet_img2img.py;ControlNetImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5670, 0.4974, 0.5090,  ..., 0.6239, 0.3544, 0.4213])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4108, 0.4594, 0.3714,  ..., 0.4975, 0.4487, 0.3804])
-tests/pipelines/controlnet/test_controlnet_img2img.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4471, 0.4789, 0.4430,  ..., 0.4075, 0.6176, 0.5182])
-tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetOneModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.3981, 0.3988, 0.4494,  ..., 0.6102, 0.3263, 0.4780])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionXLControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5472, 0.5017, 0.5610,  ..., 0.4896, 0.5703, 0.5090])
-tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py;StableDiffusionInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5252, 0.5157, 0.5404,  ..., 0.5183, 0.3829, 0.4640])
-tests/pipelines/controlnet/test_controlnet_sdxl.py;StableDiffusionSSD1BControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5351, 0.4945, 0.5729,  ..., 0.4832, 0.5497, 0.5036])
-tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py;StableDiffusionSimpleInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5193, 0.4510, 0.4699,  ..., 0.4309, 0.5689, 0.5211])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py;StableDiffusionXLAdapterPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5402, 0.5313, 0.4827,  ..., 0.4965, 0.4010, 0.4730])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py;StableDiffusionXLMultiAdapterPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5429, 0.5245, 0.4855,  ..., 0.4962, 0.3978, 0.4718])
-tests/pipelines/animatediff/test_animatediff.py;AnimateDiffPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6631, 0.3638, 0.7018,  ..., 0.4205, 0.3786, 0.5323])
-tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py;ControlNetPipelineSDXLImg2ImgFastTests;test_ip_adapter_single;expected_slice = np.array([0.5032, 0.4914, 0.4156,  ..., 0.4288, 0.5438, 0.5353])
-tests/pipelines/latent_consistency_models/test_latent_consistency_models.py;LatentConsistencyModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4673, 0.5478, 0.4212,  ..., 0.3952, 0.5166, 0.3645])
-tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py;LatentConsistencyModelImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4727, 0.4837, 0.3874, 0.4416, 0.4228, 0.4248, 0.4020, 0.3948, 0.4728,        0.3820, 0.3425, 0.4783, 0.3932, 0.3890, 0.5569, 0.5104, 0.5353, 0.4812,        0.5141, 0.5837, 0.4172, 0.5164, 0.6265, 0.4387, 0.6224, 0.6619, 0.4802,        0.5347, 0.6933, 0.4395, 0.4898, 0.5329, 0.4273, 0.3806, 0.3944, 0.4154,        0.6457, 0.2074, 0.3649, 0.4737, 0.2574, 0.3800, 0.2404, 0.3031, 0.4435,        0.3691, 0.3242, 0.4944, 0.5168, 0.3498, 0.5396, 0.3967, 0.4675, 0.4959,        0.2979, 0.3906, 0.6241, 0.5349, 0.6297, 0.5059, 0.5763, 0.8097, 0.2908,        0.4899, 0.5621, 0.3821, 0.4611, 0.4767, 0.3430, 0.4380, 0.4989, 0.4177,        0.5748, 0.4282, 0.5360, 0.5936, 0.4370, 0.5187, 0.5238, 0.4613, 0.4679,        0.4738, 0.3415, 0.4602, 0.3650, 0.4573, 0.4232, 0.4037, 0.5430, 0.3417,        0.5157, 0.4965, 0.3916, 0.4843, 0.4634, 0.4956, 0.5306, 0.6089, 0.3561,        0.4265, 0.5170, 0.2981, 0.4429, 0.5426, 0.3638, 0.4226, 0.4385, 0.3275,        0.4260, 0.4458, 0.4576, 0.5290, 0.6903, 0.4481, 0.5580, 0.6014, 0.4440,        0.5313, 0.4578, 0.5540, 0.5086, 0.4501, 0.6122, 0.5441, 0.5098, 0.5764,        0.7264, 0.4096, 0.4509, 0.7410, 0.4093, 0.4205, 0.7574, 0.3568, 0.3911,        0.4827, 0.3541, 0.1468, 0.3153, 0.4108, 0.1738, 0.4357, 0.4889, 0.1145,        0.5563, 0.3478, 0.3761, 0.4880, 0.4253, 0.4769, 0.4472, 0.4484, 0.6976,        0.7142, 0.8586, 0.6700, 0.5642, 0.9458, 0.6598, 0.5135, 0.7206, 0.6714,        0.4299, 0.7010, 0.3986, 0.5774, 0.6750, 0.3783, 0.6162, 0.5987, 0.3725,        0.5072, 0.4367, 0.4101, 0.6752, 0.4632, 0.5391, 0.6147, 0.3052, 0.4748,        0.3816, 0.3031, 0.6073, 0.6515, 0.6547, 0.5452, 0.5231, 0.7740, 0.3158,        0.4909, 0.5294, 0.4122, 0.5047, 0.5311, 0.4125, 0.4354, 0.4148, 0.4566,        0.4812, 0.5084, 0.4603, 0.4754, 0.5355, 0.3629, 0.4803, 0.5671, 0.4288,        0.6352, 0.8365, 0.4284, 0.6035, 0.6975, 0.2285, 0.4641, 0.4339, 0.3685,        0.5522, 0.3721, 0.4778, 0.6233, 0.4354, 0.6540, 0.6376, 0.3019, 0.7616,        0.8496, 0.2754, 0.8003, 0.7695, 0.3445, 0.8812, 0.7216, 0.3379, 0.5508,        0.6469, 0.5494, 0.3054, 0.5707, 0.8332, 0.2034, 0.6121, 0.9139, 0.2370,        0.4724, 0.6338, 0.2577, 0.5709, 0.3563, 0.4810, 0.6929, 0.6369, 0.3702,        0.6863, 0.6233, 0.4498, 0.5340, 0.4148, 0.6818, 0.2670, 0.4467, 0.6415,        0.4049, 0.5115, 0.7727, 0.5138, 0.5986, 0.5869, 0.4080, 0.4990, 0.5394,        0.9199, 0.5005, 0.4901, 0.8152, 0.5162, 0.2185, 0.5400, 0.3359, 0.6066,        0.7427, 0.5483, 0.7262, 0.5024, 0.7209, 0.6011, 0.4282, 0.5985, 0.6451,        0.4218, 0.6055, 0.3239, 0.3394, 0.3017, 0.3424, 0.4946, 0.3345, 0.6130,        0.5921, 0.4521, 0.5423, 0.4052, 0.4888, 0.5028, 0.7299, 0.7768, 0.5638,        0.6224, 0.7998, 0.1672, 0.5374, 0.5128, 0.3050, 0.4841, 0.4397, 0.2668,        0.4868, 0.3435, 0.3355, 0.4254, 0.3386, 0.6652, 0.4653, 0.4154, 0.7789,        0.6506, 0.4002, 0.9208, 0.7422, 0.3525, 0.7314, 0.6394, 0.2504, 0.7503,        0.7060, 0.5675, 0.8578, 0.8179, 0.8798, 0.4479, 0.5993, 0.5310, 0.1587,        0.2938, 0.3664, 0.3908, 0.3624, 0.5250, 0.4654, 0.7351, 0.4670, 0.3091,        0.4857, 0.2661, 0.2888, 0.2907, 0.3230, 0.5143, 0.6200, 0.5479, 0.6498,        0.6425, 0.8554, 0.5415, 0.5481, 0.6915, 0.8897, 0.7886, 0.6989, 0.7339,        0.5408, 0.6597, 0.3843, 0.6936, 0.3153, 0.4212, 0.7001, 0.3445, 0.4376,        0.5448, 0.4853, 0.5306, 0.5628, 0.4666, 0.5244, 0.3769, 0.6480, 0.1897,        0.4982, 0.5807, 0.1894, 0.7708, 0.6421, 0.3847, 0.6439, 0.5955, 0.2998,        0.5024, 0.4201, 0.5570, 0.6126, 0.6387, 0.6804, 0.6523, 0.5026, 0.4148,        0.5726, 0.3045, 0.4663, 0.3674, 0.4650, 0.2615, 0.3941, 0.2696, 0.2635,        0.3850, 0.4038, 0.3641, 0.4686, 0.4319, 0.3191, 0.5035, 0.4334, 0.3150,        0.6399, 0.8367, 0.2995, 0.8278, 0.5224, 0.6219, 0.5735, 0.4266, 0.6716,        0.8809, 0.6117, 0.5355, 0.7140, 0.4259, 0.2723, 0.4333, 0.2741, 0.2895,        0.4728, 0.6456, 0.4954, 0.6747, 0.6561, 0.4168, 0.4106, 0.4375, 0.1843,        0.4175, 0.2758, 0.3496, 0.6626, 0.4244, 0.4587, 0.6780, 0.6803, 0.5772,        0.7412, 0.7196, 0.6920, 0.6773, 0.6554, 0.6610, 0.6176, 0.5964, 0.8337,        0.7449, 0.6463, 0.5231, 0.6510, 0.5841, 0.2479, 0.5986, 0.4813, 0.3993,        0.5869, 0.4549, 0.4113, 0.4278, 0.6660, 0.2654, 0.6386, 0.6176, 0.4242,        0.7498, 0.6851, 0.3700, 0.4796, 0.4176, 0.3017, 0.4745, 0.2006, 0.4607,        0.5130, 0.4931, 0.5279, 0.6962, 0.5620, 0.5638, 0.5899, 0.3824, 0.5125,        0.4451, 0.5512, 0.4416, 0.5429, 0.5627, 0.4802, 0.4563, 0.5634, 0.4715,        0.5142, 0.6029, 0.5443, 0.5367, 0.7432, 0.5769, 0.8465, 1.0000, 0.5031,        0.7434, 0.8401, 0.3668, 0.3722, 0.5625, 0.5649, 0.8173, 0.6245, 0.6214,        0.8542, 0.3987, 0.3535, 0.5417, 0.1592, 0.3656, 0.3821, 0.4996, 0.4980,        0.4923, 0.5505, 0.5125, 0.3499, 0.3865, 0.2807, 0.6432, 0.2871, 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0.3450, 0.5359, 0.6621, 0.3775, 0.4966, 0.6092, 0.2151,        0.6458, 0.4389, 0.3449, 0.8817, 0.7024, 0.4380, 0.7162, 0.8040, 0.6769,        0.6702, 0.7156, 0.9591, 0.4874, 0.6743, 0.4401, 0.3839, 0.5183, 0.5283,        0.6850, 0.5444, 0.6421, 0.5339, 0.5230, 0.3522, 0.6448, 0.3572, 0.5234,        0.7551, 0.4904, 0.4847, 0.4549, 0.5784, 0.3936, 0.4989, 0.6498, 0.4534,        0.6229, 0.5364, 0.4595, 0.7154, 0.5827, 0.3751, 0.6620, 0.5963, 0.5176,        0.5681, 0.6005, 0.8772, 0.4812, 0.6244, 0.5355, 0.4050, 0.2833, 0.2950,        0.4756, 0.3060, 0.3294, 0.5505, 0.4957, 0.5157, 0.5865, 0.5995, 0.5241,        0.5532, 0.6122, 0.4270, 0.6252, 0.4135, 0.3819, 0.6932, 0.6494, 0.6627,        0.7514, 0.7545, 0.7347, 0.5654, 0.4263, 0.5137, 0.5334, 0.2314, 0.4750,        0.6062, 0.3866, 0.5300, 0.6435, 0.3303, 0.5061, 0.5052, 0.3452, 0.5009,        0.5377, 0.5495, 0.6370, 0.6274, 0.7272, 0.4819, 0.5550, 0.5462, 0.2701,        0.6233, 0.4362, 0.3090, 0.6177, 0.5008, 0.3740, 0.6038, 0.5106, 0.5381,        0.3698, 0.6000, 0.1618, 0.4388, 0.2734, 0.3244, 0.6152, 0.3810, 0.5200,        0.4747, 0.5764, 0.3925, 0.6318, 0.3247, 0.4231, 0.6486, 0.3201, 0.4255,        0.5947, 0.3015, 0.5483, 0.5636, 0.4331, 0.6404, 0.5720, 0.5391, 0.6640,        0.6880, 0.5430, 0.4580, 0.7524, 0.5093, 0.4046, 0.5173, 0.5065, 0.5306,        0.5678, 0.5780, 0.4989, 0.5951, 0.5206, 0.3905, 0.4915, 0.3855, 0.0566,        0.4061, 0.3554, 0.3234, 0.5687, 0.4067, 0.6293, 0.4662, 0.5865, 0.5577,        0.3482, 0.4083, 0.3415, 0.3699, 0.2853, 0.4362, 0.5451, 0.2516, 0.4806,        0.4925, 0.2733, 0.5905, 0.6514, 0.4243, 0.6581, 0.7146, 0.7410, 0.5491,        0.6085, 0.6351, 0.4507, 0.4739, 0.4484, 0.3526, 0.6897, 0.4101, 0.4079,        0.7061, 0.4668, 0.4512, 0.5072, 0.4918, 0.4034, 0.4510, 0.5015, 0.4931,        0.5071, 0.4536, 0.3887, 0.4395, 0.4240, 0.2735, 0.2900, 0.3806, 0.0000,        0.4927, 0.3690, 0.1561, 0.4049, 0.3492, 0.4046, 0.4479, 0.4654, 0.4420,        0.5791, 0.6208, 0.4898, 0.5788, 0.6577, 0.1894, 0.7032, 0.3445, 0.2081,        0.6216, 0.3121, 0.4803, 0.5078, 0.3493, 0.6281, 0.5338, 0.5805, 0.5211,        0.6493, 0.4611, 0.4857, 0.5933, 0.5104, 0.5289, 0.7214, 0.6470, 0.5640,        0.5522, 0.6409, 0.4637, 0.3679, 0.4813, 0.2115, 0.3770, 0.4199, 0.2558,        0.6524, 0.3712, 0.4241, 0.5968, 0.3808, 0.4587, 0.4121, 0.6824, 0.1787,        0.4057, 0.5428, 0.1544, 0.5830, 0.4779, 0.3097, 0.5148, 0.4422, 0.5177,        0.4249, 0.4783, 0.7293, 0.5124, 0.8366, 0.7509, 0.5803, 0.8377, 0.3831,        0.5807, 0.6065, 0.2713, 0.6415, 0.4880, 0.3139, 0.4900, 0.4274, 0.4308,        0.5406, 0.4311, 0.3661, 0.5330, 0.3236, 0.2628, 0.5279, 0.4235, 0.3488,        0.4546, 0.5180, 0.3543, 0.3224, 0.4554, 0.1697, 0.5995, 0.5904, 0.2439,        0.5058, 0.6253, 0.3916, 0.4451, 0.4900, 0.4383, 0.4494, 0.5161, 0.6127,        0.5653, 0.8897, 0.4468, 0.7854, 0.6024, 0.2876, 0.4589, 0.3529, 0.3478,        0.5233, 0.2445, 0.6665, 0.4960, 0.6529, 0.5410, 0.6721, 0.4759, 0.4655,        0.5652, 0.3893, 0.5406, 0.6767, 0.3961, 0.6651, 0.4085, 0.5719, 0.6448,        0.5038, 0.5059, 0.4041, 0.8074, 0.7087, 0.3172, 0.6261, 0.7464, 0.2320,        0.4057, 0.4490, 0.1763, 0.5958, 0.6496, 0.4832, 0.6792, 0.8416, 0.5804,        0.5051, 0.6835, 0.3011, 0.4259, 0.3919, 0.3424, 0.6088, 0.4027, 0.6129,        0.5131, 0.5461, 0.5660, 0.4521, 0.4666, 0.3851, 0.6089, 0.6036, 0.3950,        0.5510, 0.5627, 0.2985, 0.3838, 0.4777, 0.2909, 0.5699, 0.3445, 0.1906,        0.5326, 0.4331, 0.2399, 0.5558, 0.4685, 0.4515, 0.3540, 0.5379, 0.4754,        0.3399, 0.4073, 0.5545, 0.6355, 0.5650, 0.5567, 0.5059, 0.6703, 0.4637,        0.4348, 0.4690, 0.4300, 0.3968, 0.2779, 0.6269, 0.4605, 0.6223, 0.7900,        0.7344, 0.7838, 0.6767, 0.5514, 0.4727, 0.3654, 0.5336, 0.1945, 0.4948,        0.6855, 0.6538, 0.5045, 0.5598, 0.7072, 0.5773, 0.5621, 0.5858, 0.5226,        0.6859, 0.5247, 0.4417, 0.5028, 0.5928, 0.4021, 0.5596, 0.3688, 0.6665,        0.7836, 0.4779, 0.6766, 0.5036, 0.6646, 0.5811, 0.3643, 0.4480, 0.4544,        0.6206, 0.4330, 0.6121, 0.7002, 0.5556, 0.7895, 0.4000, 0.7148, 0.3143,        0.5150, 0.4836, 0.3662, 0.7012, 0.3882, 0.3376, 0.4641, 0.4317, 0.3990,        0.6089, 0.3519, 0.6927, 0.6514, 0.7893, 0.5629, 0.6903, 0.7673, 0.3089,        0.5147, 0.5801, 0.2060, 0.6284, 0.5278, 0.2345, 0.4683, 0.4765, 0.3171,        0.5530, 0.4677, 0.4599, 0.3073, 0.4679, 0.3568, 0.2789, 0.3656, 0.5288,        0.6148, 0.4860, 0.5727, 0.4898, 0.7212, 0.4323, 0.4197, 0.5701, 0.3980,        0.4658, 0.3411, 0.5175, 0.5407, 0.6876, 0.7612, 0.7090, 1.0000, 0.7764,        0.5650, 0.8015, 0.4179, 0.5865, 0.3666, 0.5023, 0.7631, 0.7219, 0.4558,        0.4778, 0.7318, 0.4551, 0.5864, 0.6892, 0.5773, 0.6152, 0.6010, 0.3787,        0.4618, 0.4869, 0.2553, 0.3689, 0.3552, 0.5004, 0.4454, 0.3074, 0.4828,        0.4655, 0.4242, 0.6244, 0.5239, 0.4552, 0.5090, 0.7016, 0.6088, 0.5262,        0.7252, 0.4271, 0.5745, 0.4758, 0.3735, 0.2902, 0.6070, 0.4271, 0.6115,        0.6368, 0.5890, 0.5551, 0.5725, 0.5810, 0.3408, 0.6166, 0.3238, 0.6235,        0.5853, 0.4646, 0.6514, 0.5642, 0.4979, 0.6640, 0.4804, 0.6369, 0.7174,        0.5630, 0.5524, 0.6413, 0.5047, 0.5233, 0.4846, 0.7040, 0.5571, 0.3364,        0.5356, 0.5294, 0.2137, 0.4224, 0.4513, 0.4347, 0.4890, 0.5933, 0.4674,        0.4224, 0.6345, 0.3650, 0.4307, 0.5913, 0.4782, 0.6225, 0.4445, 0.5038,        0.7047, 0.5046, 0.3634, 0.7345, 0.7886, 0.6381, 0.4987, 0.8319, 0.7656,        0.6886, 0.6241, 0.5325, 0.7138, 0.6680, 0.3892, 0.5413, 0.5038, 0.2948,        0.6171, 0.4938, 0.4984, 0.4924, 0.5402, 0.3802, 0.5137, 0.3816, 0.3524,        0.4184, 0.5276, 0.3131, 0.5149, 0.5118, 0.1041, 0.4973, 0.4154, 0.2655,        0.5447, 0.5236, 0.4294, 0.5983, 0.4937, 0.4436, 0.4986, 0.4291, 0.4777,        0.4561, 0.3940, 0.3527, 0.7233, 0.2438, 0.6409, 0.4393, 0.4861, 0.6453,        0.4000, 0.6778, 0.4553, 0.6054, 0.3776, 0.4178, 0.6426, 0.3852, 0.4423,        0.5227, 0.3302, 0.7330, 0.4794, 0.5983, 0.7435, 0.5718, 0.6403, 0.6419,        0.6371, 0.5847, 0.4629, 0.7555, 0.6010, 0.4023, 0.5511, 0.3948, 0.2411,        0.3896, 0.3310, 0.5163, 0.5533, 0.5864, 0.5197, 0.4540, 0.6198, 0.2601,        0.4728, 0.4873, 0.3785, 0.5561, 0.4106, 0.5141, 0.6498, 0.4003, 0.3315,        0.6963, 0.4753, 0.5145, 0.6562, 0.6078, 0.6569, 0.7532, 0.5875, 0.4261,        0.7033, 0.5947, 0.4142, 0.5168, 0.3775, 0.3398, 0.5376, 0.4822, 0.3910,        0.5321, 0.5980, 0.4668, 0.5674, 0.4592, 0.3129, 0.4629, 0.4752, 0.3925,        0.5847, 0.6381, 0.4003, 0.5896, 0.6131, 0.3718, 0.4524, 0.5152, 0.2863,        0.5096, 0.4460, 0.4155, 0.4130, 0.5205, 0.3520, 0.5280, 0.4925, 0.4221,        0.6807, 0.5079, 0.3355, 0.3148, 0.5609, 0.3470, 0.3511, 0.5344, 0.4553,        0.5690, 0.6794, 0.3717, 0.5811, 0.5703, 0.1677, 0.6258, 0.4046, 0.4165,        0.7381, 0.4309, 0.6070, 0.4338, 0.5440, 0.6394, 0.5251, 0.6306, 0.6471,        0.6227, 0.6332, 0.6080, 0.5401, 0.4501, 0.3760, 0.4988, 0.3484, 0.3413,        0.5723, 0.4903, 0.4196, 0.4339, 0.5795, 0.3934, 0.4871, 0.5387, 0.4201,        0.5522, 0.5204, 0.5079, 0.7109, 0.5737, 0.4260, 0.5440, 0.5041, 0.5355,        0.5832, 0.6359, 0.6559, 0.6531, 0.5047, 0.6802, 0.5886, 0.4052, 0.7402,        0.5522, 0.3753, 0.5463, 0.5840, 0.3782, 0.4872, 0.5344, 0.4647, 0.5034,        0.4677, 0.4173, 0.3197, 0.5061, 0.4639, 0.4907, 0.5897, 0.5079, 0.5500,        0.5335, 0.6176, 0.5587, 0.4411, 0.4918, 0.3772, 0.5053, 0.5759, 0.4761,        0.4835, 0.4493, 0.4314, 0.5224, 0.4812, 0.5748, 0.5392, 0.6067, 0.5156,        0.3929, 0.5364, 0.4548, 0.4926, 0.5257, 0.5178, 0.5438, 0.7917, 0.5773,        0.5188, 0.8783, 0.4678, 0.6196, 0.5876, 0.3045, 0.5458, 0.4273, 0.1633,        0.4467, 0.4481, 0.5367, 0.4965, 0.5763, 0.6864, 0.5712, 0.6513, 0.5730,        0.5039, 0.5275, 0.3780, 0.4799, 0.3634, 0.4330, 0.5412, 0.4338, 0.5033,        0.4766, 0.5475, 0.5306, 0.5434, 0.6523, 0.5196, 0.6034, 0.5517, 0.4345,        0.6558, 0.4838, 0.2313, 0.5662, 0.4185, 0.4143, 0.5303, 0.5792, 0.5758,        0.6508, 0.5242, 0.5528, 0.6065, 0.4692, 0.5048, 0.5784, 0.4995, 0.5797,        0.6436, 0.3655, 0.5871, 0.5821, 0.5080, 0.4838, 0.4789, 0.5510, 0.4219,        0.4761, 0.4802, 0.4414, 0.4957, 0.4607, 0.4617, 0.5009, 0.5583, 0.4961,        0.4643, 0.5034, 0.4417])
-tests/pipelines/animatediff/test_animatediff_video2video.py;AnimateDiffVideoToVideoPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5371, 0.4619, 0.4810,  ..., 0.5299, 0.5213, 0.5017])
-tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py;StableDiffusionPanoramaPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5701, 0.4671, 0.4628,  ..., 0.4668, 0.4121, 0.4758])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py;StableDiffusionXLPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5559, 0.5192, 0.5225,  ..., 0.4868, 0.4070, 0.4700])
-tests/pipelines/pia/test_pia.py;PIAPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6319, 0.3310, 0.6785,  ..., 0.4153, 0.3993, 0.4803])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py;StableDiffusionXLImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4268, 0.4839, 0.6225, 0.4833, 0.5842, 0.6271, 0.6348, 0.4880, 0.3769,        0.4883, 0.3334, 0.4445, 0.4066, 0.3843, 0.6783, 0.3823, 0.5708, 0.6232,        0.4779, 0.6799, 0.4163, 0.5136, 0.4608, 0.3669, 0.4662, 0.3222, 0.3027,        0.5589, 0.3275, 0.4823, 0.5896, 0.4030, 0.6444, 0.6134, 0.3049, 0.5492,        0.6752, 0.2849, 0.5590, 0.4189, 0.3228, 0.5003, 0.5009, 0.4121, 0.5976,        0.4985, 0.4689, 0.4741, 0.5126, 0.5028, 0.2950, 0.5795, 0.5361, 0.4508,        0.5831, 0.4235, 0.4617, 0.5733, 0.4391, 0.4454, 0.5310, 0.5912, 0.5436,        0.3767, 0.5004, 0.5389, 0.5085, 0.4970, 0.6830, 0.5539, 0.4790, 0.3807,        0.4582, 0.4171, 0.3415, 0.4272, 0.5233, 0.5609, 0.4821, 0.5680, 0.5037,        0.4704, 0.5234, 0.5169, 0.5513, 0.2724, 0.5759, 0.4514, 0.3168, 0.4625,        0.4586, 0.3985, 0.4831, 0.5451, 0.4160, 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0.4234, 0.4932, 0.5893, 0.3761, 0.5607, 0.4565, 0.5082,        0.5163, 0.4117, 0.3756, 0.5618, 0.4711, 0.3797, 0.5817, 0.5551, 0.5035,        0.5572, 0.5242, 0.6296, 0.5535, 0.4347, 0.6655, 0.6016, 0.4961, 0.5840,        0.4946, 0.5580, 0.5675])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py;StableDiffusionXLInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5139, 0.5046, 0.4932,  ..., 0.4111, 0.6005, 0.5261])
-tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4446, 0.5244, 0.4132,  ..., 0.3766, 0.4483, 0.4045])
-tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4187, 0.4723, 0.6115, 0.4732, 0.6281, 0.6558, 0.5885, 0.4857, 0.3319,        0.4996, 0.2946, 0.3899, 0.5099, 0.2973, 0.6799, 0.4544, 0.4880, 0.5956,        0.5434, 0.6524, 0.3132, 0.4954, 0.5411, 0.3645, 0.5039, 0.3972, 0.4258,        0.5455, 0.4031, 0.5206, 0.6499, 0.4357, 0.5946, 0.6794, 0.3721, 0.4176,        0.6314, 0.2659, 0.6089, 0.3804, 0.3809, 0.6302, 0.4882, 0.3994, 0.8052,        0.4036, 0.4795, 0.6829, 0.4945, 0.4617, 0.4217, 0.6677, 0.4400, 0.4698,        0.5329, 0.3903, 0.4421, 0.5593, 0.3651, 0.5001, 0.5137, 0.5304, 0.5959,        0.4130, 0.5209, 0.6452, 0.5299, 0.5284, 0.6070, 0.5437, 0.5505, 0.4562,        0.4784, 0.4008, 0.4409, 0.4282, 0.5417, 0.5370, 0.4934, 0.5464, 0.4872,        0.4754, 0.4932, 0.5313, 0.5136, 0.2906, 0.5220, 0.5573, 0.2948, 0.4066,        0.5245, 0.3553, 0.4587, 0.5152, 0.4206, 0.4220, 0.4843, 0.6173, 0.5865,        0.6728, 0.5896, 0.4671, 0.6921, 0.5249, 0.5100, 0.6747, 0.5130, 0.5961,        0.6672, 0.3560, 0.7273, 0.5839, 0.4446, 0.6666, 0.5311, 0.5908, 0.5465,        0.5696, 0.5045, 0.4929, 0.7326, 0.4755, 0.3917, 0.4482, 0.5832, 0.3875,        0.4344, 0.5181, 0.3573, 0.7740, 0.1777, 0.2418, 0.7044, 0.1088, 0.6033,        0.3674, 0.4325, 0.5128, 0.5653, 0.3448, 0.6685, 0.6430, 0.2543, 0.5667,        0.5394, 0.5169, 0.5568, 0.4714, 0.4327, 0.4763, 0.4821, 0.3060, 0.4990,        0.5235, 0.4264, 0.5366, 0.4393, 0.5357, 0.5238, 0.3755, 0.5209, 0.5513,        0.2736, 0.4851, 0.5389, 0.4684, 0.4999, 0.4565, 0.4857, 0.3868, 0.5489,        0.3823, 0.5660, 0.4763, 0.6750, 0.6398, 0.4855, 0.6618, 0.5888, 0.5324,        0.5110, 0.3728, 0.6058, 0.5407, 0.4721, 0.5987, 0.4429, 0.4285, 0.4803,        0.4973, 0.3474, 0.3977, 0.4961, 0.5830, 0.3958, 0.7465, 0.4151, 0.4583,        0.7607, 0.3993, 0.6202, 0.8084, 0.5949, 0.7009, 0.5412, 0.5593, 0.7091,        0.2425, 0.5553, 0.5782, 0.2229, 0.3767, 0.7145, 0.5322, 0.3301, 0.7463,        0.6159, 0.3180, 0.4866, 0.5069, 0.3944, 0.4008, 0.1355, 0.5426, 0.5614,        0.5622, 0.5396, 0.4694, 0.5395, 0.2682, 0.6818, 0.4521, 0.6149, 0.5607,        0.4325, 0.4569, 0.4210, 0.4831, 0.2483, 0.7692, 0.3045, 0.3404, 0.6021,        0.5578, 0.2515, 0.6840, 0.6577, 0.3710, 0.8662, 0.4950, 0.6275, 0.6008,        0.5036, 0.5819, 0.4623, 0.2445, 0.3592, 0.5654, 0.2892, 0.2344, 0.3796,        0.5604, 0.2748, 0.4526, 0.6099, 0.2555, 0.6546, 0.5700, 0.4287, 0.5696,        0.6622, 0.5205, 0.5925, 0.6958, 0.5209, 0.5907, 0.5248, 0.3359, 0.6266,        0.5309, 0.3768, 0.5485, 0.4764, 0.5716, 0.6209, 0.4449, 0.4937, 0.5619,        0.5131, 0.3472, 0.4157, 0.5440, 0.5398, 0.6432, 0.5721, 0.4665, 0.5982,        0.7315, 0.4157, 0.4840, 0.5805, 0.2572, 0.5292, 0.4213, 0.5041, 0.4496,        0.0700, 0.4775, 0.6086, 0.4182, 0.2243, 0.7072, 0.4915, 0.2705, 0.4061,        0.5516, 0.4390, 0.4856, 0.4039, 0.8276, 0.4955, 0.6512, 0.7849, 0.3723,        0.7494, 0.4734, 0.2308, 0.8142, 0.5528, 0.4408, 0.7538, 0.3637, 0.5570,        0.4191, 0.4579, 0.6873, 0.2885, 0.5069, 0.5369, 0.3645, 0.4748, 0.8841,        0.4601, 0.5119, 0.8920, 0.6442, 0.4549, 0.5399, 0.7773, 0.3784, 0.5833,        0.6002, 0.2133, 0.6193, 0.4071, 0.1191, 0.6675, 0.4929, 0.4061, 0.6907,        0.5390, 0.3606, 0.7042, 0.4878, 0.4087, 0.7083, 0.5524, 0.4190, 0.6616,        0.6198, 0.3213, 0.5088, 0.5516, 0.2955, 0.6146, 0.4717, 0.4123, 0.5271,        0.5182, 0.6627, 0.6477, 0.4664, 0.5932, 0.5899, 0.4754, 0.3816, 0.3807,        0.4900, 0.6408, 0.6488, 0.4278, 0.7307, 0.5183, 0.6565, 0.6280, 0.6218,        0.6839, 0.2495, 0.6195, 0.6827, 0.2841, 0.5837, 0.4497, 0.4789, 0.5273,        0.5695, 0.4736, 0.6674, 0.5827, 0.3122, 0.3079, 0.5235, 0.3393, 0.2434,        0.4645, 0.6304, 0.2721, 0.5660, 0.6581, 0.4589, 0.5074, 0.3792, 0.3013,        0.5379, 0.5130, 0.3998, 0.7850, 0.1533, 0.3895, 0.6489, 0.3712, 0.4300,        0.4947, 0.7201, 0.1710, 0.3801, 0.6727, 0.6237, 0.2400, 0.6739, 0.7475,        0.5669, 0.7516, 0.5386, 0.7927, 0.3891, 0.6944, 0.6602, 0.2502, 0.6734,        0.4968, 0.3178, 0.8466, 0.5647, 0.4883, 0.6763, 0.4210, 0.6419, 0.6783,        0.2227, 0.6709, 0.8118, 0.3772, 0.5801, 0.7698, 0.6791, 0.2458, 0.7319,        0.6846, 0.0627, 0.7951, 0.5367, 0.3581, 0.5393, 0.4884, 0.7128, 0.5139,        0.5068, 0.7683, 0.6965, 0.4710, 0.4959, 0.3840, 0.5054, 0.7034, 0.4939,        0.5746, 0.7637, 0.4336, 0.6802, 0.6172, 0.4267, 0.6884, 0.3733, 0.5748,        0.8018, 0.2773, 0.5011, 0.6878, 0.2009, 0.5261, 0.2832, 0.4958, 0.6537,        0.4584, 0.2970, 0.6312, 0.4979, 0.2181, 0.5058, 0.4799, 0.3642, 0.2190,        0.4698, 0.2657, 0.3374, 0.2901, 0.3228, 0.5511, 0.5512, 0.3254, 0.1858,        0.7038, 0.1834, 0.4234, 0.5075, 0.4966, 0.4199, 0.6063, 0.5793, 0.2862,        0.6436, 0.5390, 0.5789, 0.3519, 0.6034, 0.4965, 0.5196, 0.6599, 0.3083,        0.6772, 0.3544, 0.6491, 0.6084, 0.4011, 0.4801, 0.4425, 0.5102, 0.6394,        0.5467, 0.4524, 0.6481, 0.7030, 0.4629, 0.6226, 0.5068, 0.7400, 0.6498,        0.5008, 0.6544, 0.8718, 0.6077, 0.4636, 0.8846, 0.6302, 0.0716, 1.0000,        0.5505, 0.3327, 0.7831, 0.5398, 0.4716, 0.3726, 0.7012, 0.6026, 0.6499,        0.5356, 0.3668, 0.4148, 0.3824, 0.3974, 0.4558, 0.5801, 0.5315, 0.2937,        0.7029, 0.5564, 0.4394, 0.5471, 0.3891, 0.5437, 0.6748, 0.4779, 0.5089,        0.6569, 0.5230, 0.5614, 0.3133, 0.5944, 0.6082, 0.5567, 0.5044, 0.8216,        0.5713, 0.4658, 0.8054, 0.7317, 0.4207, 0.5424, 0.6656, 0.0000, 0.5172,        0.3960, 0.0945, 0.7080, 0.7085, 0.3213, 0.5803, 0.7198, 0.4176, 0.5697,        0.3983, 0.6205, 0.5578, 0.4702, 0.5926, 0.3957, 0.5628, 0.3352, 0.3349,        0.4098, 0.3963, 0.5715, 0.6799, 0.5752, 0.5247, 0.6315, 0.2302, 0.5928,        0.6327, 0.5293, 0.4541, 0.6698, 0.7955, 0.5595, 0.5286, 0.5682, 0.5981,        0.7638, 0.4183, 0.5602, 0.6632, 0.5499, 0.5230, 0.4837, 0.3079, 0.5981,        0.5183, 0.3274, 0.7085, 0.6562, 0.2709, 1.0000, 0.5966, 0.2918, 0.8340,        0.3982, 0.5110, 0.4654, 0.4861, 0.4390, 0.4981, 0.5617, 0.2062, 0.4825,        0.3737, 0.3230, 0.5367, 0.5569, 0.4531, 0.3795, 0.5967, 0.4795, 0.5677,        0.4198, 0.5137, 0.4919, 0.7565, 0.5857, 0.3383, 0.8714, 0.4352, 0.4086,        0.6632, 0.5557, 0.4896, 0.6842, 0.7379, 0.7575, 0.6229, 0.5430, 0.6680,        0.8645, 0.4757, 0.4918, 0.9931, 0.2308, 0.5357, 0.6331, 0.3666, 0.6061,        0.7017, 0.4692, 0.6392, 0.4208, 0.4729, 0.3348, 0.5358, 0.4529, 0.3804,        0.5483, 0.4574, 0.5787, 0.5310, 0.3365, 0.3072, 0.5866, 0.3764, 0.6054,        0.5432, 0.5414, 0.5744, 0.6400, 0.4599, 0.4850, 0.6017, 0.4858, 0.4947,        0.6724, 0.6419, 0.5789, 0.6113, 0.7537, 0.5520, 0.7227, 0.4331, 0.6595,        0.7183, 0.2904, 0.4374, 0.6050, 0.2222, 0.5248, 0.5326, 0.4110, 0.6349,        0.4993, 0.5580, 0.8244, 0.6911, 0.2450, 0.6787, 0.5469, 0.3907, 0.7481,        0.4756, 0.4324, 0.4370, 0.6810, 0.2620, 0.6827, 0.4865, 0.3800, 0.7787,        0.5551, 0.3692, 0.5663, 0.5568, 0.2346, 0.8290, 0.3633, 0.5774, 0.8668,        0.7508, 0.6222, 0.7160, 0.9099, 0.3513, 0.5147, 0.7126, 0.4644, 0.4455,        0.6855, 0.6623, 0.5682, 0.5712, 0.4713, 0.6220, 0.7619, 0.4405, 0.4495,        0.7127, 0.3205, 0.4314, 0.4370, 0.4909, 0.6633, 0.5767, 0.6359, 0.6857,        0.3273, 0.5234, 0.1727, 0.6135, 0.4823, 0.4808, 0.6105, 0.1191, 0.7046,        0.7174, 0.0556, 0.3942, 0.7011, 0.2373, 0.3510, 0.4790, 0.3254, 0.2398,        0.5664, 0.4685, 0.6381, 0.5759, 0.5516, 0.7588, 0.4422, 0.5845, 0.4590,        0.4021, 0.4681, 0.5531, 0.2839, 0.3284, 0.8860, 0.4187, 0.3755, 0.9099,        0.5836, 0.2806, 0.8078, 0.4768, 0.4705, 0.6632, 0.3550, 0.8537, 0.3144,        0.4609, 0.4700, 0.4987, 0.3976, 0.2360, 0.7363, 0.5566, 0.2192, 0.6553,        0.6664, 0.3859, 0.6683, 0.5866, 0.4581, 0.7810, 0.7160, 0.3832, 0.6326,        0.4961, 0.3463, 0.8553, 0.4208, 0.6737, 0.7524, 0.8030, 0.7204, 0.7460,        0.7752, 0.4196, 0.6394, 0.7426, 0.5102, 0.6605, 0.7665, 0.5631, 0.6287,        0.6428, 0.5539, 0.6983, 0.6047, 0.5473, 0.5731, 0.6127, 0.3505, 0.4260,        0.4618, 0.2621, 0.3186, 0.5125, 0.3742, 0.3299, 0.5346, 0.3605, 0.4926,        0.6419, 0.4169, 0.3948, 0.5411, 0.0482, 0.5819, 0.5146, 0.1126, 0.6147,        0.5632, 0.2341, 0.3603, 0.5763, 0.2039, 0.4583, 0.3015, 0.7110, 0.6424,        0.4779, 0.5998, 0.4499, 0.5132, 0.5037, 0.2896, 0.4412, 0.5522, 0.4888,        0.3308, 0.5116, 0.9239, 0.3867, 0.4799, 0.9324, 0.5108, 0.4019, 0.7120,        0.8200, 0.3496, 0.6770, 0.6130, 0.5732, 0.5754, 0.4571, 0.5281, 0.6283,        0.2581, 0.3564, 0.6714, 0.4583, 0.2776, 0.5938, 0.7024, 0.4217, 0.7541,        0.8012, 0.6000, 0.7648, 1.0000, 0.5288, 0.5753, 0.6362, 0.4306, 0.5986,        0.2370, 0.6459, 0.1612, 0.4752, 0.5767, 0.5793, 0.4916, 0.4362, 0.8696,        0.6978, 0.6403, 0.6847, 0.8008, 0.5597, 0.6870, 0.5994, 0.3830, 0.6171,        0.4818, 0.6296, 0.6633, 0.6258, 0.7733, 0.5804, 0.4788, 0.4524, 0.2805,        0.5097, 0.3202, 0.2308, 0.7342, 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0.4100,        0.5761, 0.4819, 0.3713, 0.6362, 0.5228, 0.7383, 0.7249, 0.1719, 0.9566,        0.5467, 0.2791, 0.8969, 0.5753, 0.0759, 0.6860, 0.6004, 0.1005, 0.6276,        0.6452, 0.3919, 0.4201, 0.5175, 0.4078, 0.3960, 0.3737, 0.4923, 0.4127,        0.3963, 0.3700, 0.3146, 0.4675, 0.1425, 0.3507, 0.5964, 0.2087, 0.0148,        0.4424, 0.4591, 0.1015, 0.4637, 0.6115, 0.2347, 0.3865, 0.3991, 0.5657,        0.3549, 0.4589, 0.3439, 0.7453, 0.1399, 0.4620, 0.4740, 0.5236, 0.4991,        0.4607, 0.6736, 0.5927, 0.4316, 0.5104, 0.6726, 0.8789, 0.2965, 0.6400,        0.8047, 0.5971, 0.4501, 0.8095, 0.7499, 0.6223, 0.6271, 0.6139, 0.7226,        0.3869, 0.5498, 0.7426, 0.4875, 0.5158, 0.7976, 0.5071, 0.3739, 0.5876,        0.6468, 0.4038, 0.4521, 0.4589, 0.3003, 0.4426, 0.3998, 0.3543, 0.3663,        0.4727, 0.5240, 0.5291, 0.4991, 0.4817, 0.5215, 0.6080, 0.4305, 0.2566,        0.5618, 0.5679, 0.5029, 0.5795, 0.2269, 0.8667, 0.5002, 0.4631, 0.7860,        0.6505, 0.4019, 0.6975, 0.5615, 0.3573, 0.8000, 0.6072, 0.4867, 0.7016,        0.7224, 0.4095, 0.5341, 0.5567, 0.6111, 0.4351, 0.5405, 0.5635, 0.4223,        0.4561, 0.3243, 0.3793, 0.5767, 0.3527, 0.2538, 0.3497, 0.4383, 0.0542,        0.4554, 0.4957, 0.2144, 0.5319, 0.2017, 0.6429, 0.5855, 0.4615, 0.6106,        0.7392, 0.1765, 0.4496, 0.5693, 0.2410, 0.3643, 0.3401, 0.6239, 0.6089,        0.5555, 0.7804, 0.7617, 0.9404, 0.6094, 0.5046, 0.6874, 0.3310, 0.3000,        0.3782, 0.2123, 0.6345, 0.6939, 0.4164, 0.7272, 0.4277, 0.3756, 0.5969,        0.3310, 0.4953, 0.5540, 0.2341, 0.4355, 0.3715, 0.4486, 0.3010, 0.4823,        0.4203, 0.2702, 0.5605, 0.6322, 0.2610, 0.4165, 0.5098, 0.5096, 0.5092,        0.4206, 0.4799, 0.5486, 0.5605, 0.5502, 0.2480, 0.5917, 0.4394, 0.4091,        0.6059, 0.2919, 0.4501, 0.4633, 0.4609, 0.3399, 0.6856, 0.5365, 0.7270,        0.4235, 0.6079, 0.5965, 0.3660, 0.5504, 0.5528, 0.6348, 0.4160, 0.6012,        0.4875, 0.6125, 0.3706, 0.4120, 0.6312, 0.5657, 0.1974, 0.4870, 0.4936,        0.4286, 0.2671, 0.1955, 0.4236, 0.3401, 0.2469, 0.4257, 0.5065, 0.2300,        0.4080, 0.3702, 0.4556, 0.5017, 0.5136, 0.4135, 0.6410, 0.3816, 0.1577,        0.5509, 0.3179, 0.4758, 0.5821, 0.7015, 0.6594, 0.6908, 0.7063, 0.7029,        0.7558, 0.5706, 0.4908, 0.6012, 0.4775, 0.5661, 0.4230, 0.2878, 0.6973,        0.5602, 0.3170, 0.6973, 0.5142, 0.2741, 0.5595, 0.4520, 0.2746, 0.4655,        0.4629, 0.4347, 0.4671, 0.4090, 0.5155, 0.5325, 0.4574, 0.2694, 0.4966,        0.6428, 0.1053, 0.4932, 0.5616, 0.5808, 0.5092, 0.4747, 0.6150, 0.5135,        0.4180, 0.6712, 0.2895, 0.4870, 0.5354, 0.3154, 0.5087, 0.2582, 0.4513,        0.5403, 0.4252, 0.3557, 0.6138, 0.4094, 0.4356, 0.5538, 0.4604, 0.3158,        0.4400, 0.6157, 0.4844, 0.3602, 0.4764, 0.4392, 0.3959, 0.5660, 0.2415,        0.3779, 0.6171, 0.4611, 0.3439, 0.5486, 0.3868, 0.5181, 0.3067, 0.2295,        0.5039, 0.2687, 0.2239, 0.4811, 0.3908, 0.3023, 0.4803, 0.2993, 0.2050,        0.4961, 0.3459, 0.2244, 0.4795, 0.4153, 0.3075, 0.3804, 0.5229, 0.5240,        0.5909, 0.6935, 0.4710, 0.6669, 0.4618, 0.6191, 0.4752, 0.4074, 0.4172,        0.4706, 0.5442, 0.6645, 0.3477, 0.5417, 0.7601, 0.4798, 0.6820, 0.7552,        0.5313, 0.4354, 0.5825, 0.4935, 0.3041, 0.3909, 0.5809, 0.4015, 0.4694,        0.5530, 0.5713, 0.6186, 0.6384, 0.3961, 0.5884, 0.4981, 0.2765, 0.5517,        0.4950, 0.5376, 0.5626, 0.5091, 0.5727, 0.6621, 0.4901, 0.5609, 0.4740,        0.4197, 0.5378, 0.4360, 0.4542, 0.4259, 0.5295, 0.5098, 0.5384, 0.3962,        0.5130, 0.4460, 0.2941, 0.5553, 0.4294, 0.3215, 0.4165, 0.5532, 0.5984,        0.4302, 0.4314, 0.5567, 0.4632, 0.4960, 0.2895, 0.5512, 0.5573, 0.3556,        0.5589, 0.4729, 0.3563, 0.4696, 0.5375, 0.3427, 0.4399, 0.4735, 0.4599,        0.3847, 0.4593, 0.5274, 0.4546, 0.4915, 0.4490, 0.4159, 0.4555, 0.3926,        0.4210, 0.4138, 0.4498, 0.4602, 0.5695, 0.5599, 0.5417, 0.6011, 0.3821,        0.5184, 0.3710, 0.5519, 0.4184, 0.3594, 0.4334, 0.4322, 0.4934, 0.4597,        0.4330, 0.5652, 0.4018, 0.4601, 0.6147, 0.4058, 0.5575, 0.5349, 0.5429,        0.4858, 0.4692, 0.4420, 0.6056, 0.4820, 0.4363, 0.6393, 0.5695, 0.5709,        0.6063, 0.5139, 0.6934, 0.5861, 0.3744, 0.6490, 0.6237, 0.5044, 0.5021,        0.5074, 0.5739, 0.5441])
 tests/pipelines/controlnet/test_controlnet.py;ControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5234, 0.3333, 0.1745, 0.7605, 0.6224, 0.4637, 0.6989, 0.7526, 0.4665])
 tests/pipelines/controlnet/test_controlnet_img2img.py;ControlNetImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.7096, 0.5149, 0.3571, 0.5897, 0.4715, 0.4052, 0.6098, 0.6886, 0.4213])
 tests/pipelines/controlnet/test_controlnet.py;StableDiffusionMultiControlNetPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.2422, 0.3425, 0.4048, 0.5351, 0.3503, 0.2419, 0.4645, 0.4570, 0.3804])
@@ -70,10 +14,10 @@ tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py;ControlNetPipelineSDX
 tests/pipelines/latent_consistency_models/test_latent_consistency_models.py;LatentConsistencyModelPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
 tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py;LatentConsistencyModelImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4003, 0.3718, 0.2863, 0.5500, 0.5587, 0.3772, 0.4617, 0.4961, 0.4417])
 tests/pipelines/animatediff/test_animatediff_video2video.py;AnimateDiffVideoToVideoPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4947, 0.4780, 0.4340, 0.4666, 0.4028, 0.4645, 0.4915, 0.4101, 0.4308,        0.4581, 0.3582, 0.4953, 0.4466, 0.5348, 0.5863, 0.5299, 0.5213, 0.5017])
-tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4932, 0.5092, 0.5135, 0.5517, 0.5626, 0.6621, 0.6490, 0.5021, 0.5441])
-tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py;StableDiffusionXLPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.4700])
+tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py;StableDiffusionPanoramaPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6185, 0.5373, 0.4915, 0.4134, 0.4114, 0.4564, 0.5128, 0.4977, 0.4758])
 tests/pipelines/pia/test_pia.py;PIAPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5609, 0.5756, 0.4830, 0.4420, 0.4547, 0.5129, 0.3779, 0.4042, 0.3772,        0.4450, 0.5710, 0.5536, 0.4835, 0.4308, 0.5578, 0.5578, 0.4395, 0.5440,        0.6051, 0.4651, 0.6258, 0.5662, 0.3988, 0.5108, 0.4153, 0.3993, 0.4803])
+tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py;StableDiffusionXLPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.4700])
 tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py;StableDiffusionXLImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.5174, 0.4512, 0.5006, 0.6273, 0.5160, 0.6825, 0.6655, 0.5840, 0.5675])
 tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py;StableDiffusionXLInpaintPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.7971, 0.5371, 0.5973, 0.5642, 0.6689, 0.6894, 0.5770, 0.6063, 0.5261])
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.3203, 0.4555, 0.4711, 0.3505, 0.3973, 0.4650, 0.5137, 0.3392, 0.4045])
-tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py;StableDiffusionPanoramaPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.6185, 0.5373, 0.4915, 0.4134, 0.4114, 0.4564, 0.5128, 0.4977, 0.4758])
+tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_ip_adapter_single;expected_slice = np.array([0.4932, 0.5092, 0.5135, 0.5517, 0.5626, 0.6621, 0.6490, 0.5021, 0.5441])