up

test_failures.txt

@@ -4,6 +4,10 @@ tests/pipelines/altdiffusion/test_alt_diffusion.py;AltDiffusionPipelineIntegrati
 tests/pipelines/altdiffusion/test_alt_diffusion.py;AltDiffusionPipelineIntegrationTests;test_alt_diffusion_fast_ddim;expected_slice = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])
 tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py;AltDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_default_case;expected_slice = np.array([0.4129, 0.3866, 0.4088, 0.4782, 0.4657, 0.4139, 0.4142, 0.4715, 0.4570])
 tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py;AltDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_pipeline_multiple_of_8;expected_slice = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])
+tests/pipelines/audio_diffusion/test_audio_diffusion.py;PipelineFastTests;test_audio_diffusion;expected_slice = np.array([255, 255, 255,   0, 181,   0, 124,   0,  15, 255], dtype=torch.uint8)
+tests/pipelines/audio_diffusion/test_audio_diffusion.py;PipelineFastTests;test_audio_diffusion;expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121], dtype=torch.uint8)
+tests/pipelines/audio_diffusion/test_audio_diffusion.py;PipelineFastTests;test_audio_diffusion;expected_slice = np.array([120, 139, 147, 123, 124,  96, 115, 121, 126, 144], dtype=torch.uint8)
+tests/pipelines/audio_diffusion/test_audio_diffusion.py;PipelineIntegrationTests;test_audio_diffusion;expected_slice = np.array([151, 167, 154, 144, 122, 134, 121, 105,  70,  26], dtype=torch.uint8)
 tests/pipelines/ddim/test_ddim.py;DDIMPipelineIntegrationTests;test_inference_cifar10;expected_slice = np.array([0.2060, 0.2042, 0.2022, 0.2193, 0.2147, 0.2111, 0.2472, 0.2447, 0.2388])
 tests/pipelines/ddim/test_ddim.py;DDIMPipelineIntegrationTests;test_inference_ema_bedroom;expected_slice = np.array([0.1547, 0.1562, 0.1596, 0.1565, 0.1570, 0.1586, 0.1555, 0.1550, 0.1575])
 tests/pipelines/ddpm/test_ddpm.py;DDPMPipelineFastTests;test_inference;expected_slice = np.array([5.5886e-01, 7.0888e-01, 2.6321e-01, 6.8414e-01, 8.2970e-05, 9.9992e-01,
@@ -29,11 +33,13 @@ tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelin
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_ddim_factor_8;expected_slice = np.array([0.5524, 0.5626, 0.6069, 0.4727, 0.3860, 0.3995, 0.4613, 0.4328, 0.4269])
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_k_euler;expected_slice = np.array([0.4708, 0.5372, 0.4562, 0.5221, 0.5734, 0.4795, 0.5466, 0.5074, 0.5042])
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_k_euler_ancestral;expected_slice = np.array([0.4707, 0.5372, 0.4563, 0.5220, 0.5734, 0.4795, 0.5464, 0.5074, 0.5044])
+tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_k_lms;expected_slice = np.array([0.4708, 0.5372, 0.4562, 0.5221, 0.5734, 0.4795, 0.5466, 0.5074, 0.5042])
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_negative_prompt;expected_slice = np.array([0.5108, 0.5688, 0.4685, 0.5098, 0.5658, 0.4631, 0.5226, 0.4913, 0.4899])
 tests/pipelines/stable_diffusion/test_stable_diffusion.py;StableDiffusionPipelineFastTests;test_stable_diffusion_pndm;expected_slice = np.array([0.5096, 0.5677, 0.4669, 0.5122, 0.5696, 0.4674, 0.5274, 0.4964, 0.4947])
 tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py;StableDiffusionImageVariationPipelineFastTests;test_stable_diffusion_img_variation_default_case;expected_slice = np.array([0.5167, 0.5746, 0.4835, 0.4914, 0.5605, 0.4691, 0.5201, 0.4898, 0.4958])
 tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py;StableDiffusionImageVariationPipelineFastTests;test_stable_diffusion_img_variation_multiple_images;expected_slice = np.array([0.6568, 0.5470, 0.5684, 0.5444, 0.5945, 0.6221, 0.5508, 0.5531, 0.5263])
 tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_default_case;expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.4800, 0.4218])
+tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_k_lms;expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.4440, 0.5864, 0.6019, 0.5203])
 tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_multiple_init_images;expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.6450, 0.5149, 0.4689])
 tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineFastTests;test_stable_diffusion_img2img_negative_prompt;expected_slice = np.array([0.4065, 0.3784, 0.4050, 0.5267, 0.4781, 0.4253, 0.4204, 0.4693, 0.4365])
 tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py;StableDiffusionImg2ImgPipelineSlowTests;test_stable_diffusion_img2img_pipeline_multiple_of_8;expected_slice = np.array([0.9393, 0.9500, 0.9399, 0.9438, 0.9458, 0.9400, 0.9455, 0.9414, 0.9423])
@@ -48,7 +54,12 @@ tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py;St
 tests/pipelines/stable_diffusion_2/test_stable_diffusion.py;StableDiffusion2PipelineFastTests;test_stable_diffusion_ddim;expected_slice = np.array([0.5650, 0.6022, 0.4805, 0.5270, 0.5585, 0.4644, 0.5160, 0.4963, 0.4793])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion.py;StableDiffusion2PipelineFastTests;test_stable_diffusion_k_euler;expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5226, 0.5734, 0.4798, 0.5467, 0.5074, 0.5044])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion.py;StableDiffusion2PipelineFastTests;test_stable_diffusion_k_euler_ancestral;expected_slice = np.array([0.4716, 0.5377, 0.4569, 0.5225, 0.5735, 0.4798, 0.5465, 0.5074, 0.5046])
+tests/pipelines/stable_diffusion_2/test_stable_diffusion.py;StableDiffusion2PipelineFastTests;test_stable_diffusion_k_lms;expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5226, 0.5734, 0.4798, 0.5467, 0.5074, 0.5044])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion.py;StableDiffusion2PipelineFastTests;test_stable_diffusion_pndm;expected_slice = np.array([0.5099, 0.5677, 0.4671, 0.5128, 0.5697, 0.4676, 0.5278, 0.4964, 0.4946])
+tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py;StableDiffusionDepth2ImgPipelineFastTests;test_stable_diffusion_depth2img_default_case;expected_slice = np.array([0.5237, 0.4408, 0.3875, 0.5113, 0.5039, 0.4586, 0.4093, 0.4871, 0.4667])
+tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py;StableDiffusionDepth2ImgPipelineFastTests;test_stable_diffusion_depth2img_multiple_init_images;expected_slice = np.array([0.6481, 0.5358, 0.5621, 0.5751, 0.4649, 0.5804, 0.4708, 0.4604, 0.4662])
+tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py;StableDiffusionDepth2ImgPipelineFastTests;test_stable_diffusion_depth2img_negative_prompt;expected_slice = np.array([0.5291, 0.4455, 0.3842, 0.5146, 0.5397, 0.4576, 0.3682, 0.4791, 0.4562])
+tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py;StableDiffusionDepth2ImgPipelineFastTests;test_stable_diffusion_depth2img_pil;expected_slice = np.array([0.5237, 0.4408, 0.3875, 0.5113, 0.5039, 0.4586, 0.4093, 0.4871, 0.4667])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py;StableDiffusion2InpaintPipelineFastTests;test_stable_diffusion_inpaint;expected_slice = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py;StableDiffusionUpscalePipelineFastTests;test_stable_diffusion_upscale;expected_slice = np.array([0.2563, 0.3607, 0.4205, 0.4470, 0.4822, 0.4648, 0.5316, 0.5749, 0.5607])
 tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py;StableDiffusion2VPredictionPipelineFastTests;test_stable_diffusion_v_pred_ddim;expected_slice = np.array([0.6424, 0.6109, 0.4940, 0.5088, 0.4984, 0.4525, 0.5059, 0.5068, 0.4474])