[`Tests`] Update a deprecated parameter in test files and fix several typos (#7277)

* Add properties and `IPAdapterTesterMixin` tests for `StableDiffusionPanoramaPipeline`

* Fix variable name typo and update comments

* Update deprecated `output_type="numpy"` to "np" in test files

* Discard changes to src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py

* Update test_stable_diffusion_panorama.py

* Update numbers in README.md

* Update get_guidance_scale_embedding method to use timesteps instead of w

* Update number of checkpoints in README.md

* Add type hints and fix var name

* Fix PyTorch's convention for inplace functions

* Fix a typo

* Revert "Fix PyTorch's convention for inplace functions"

This reverts commit 74350cf65b2c9aa77f08bec7937d7a8b13edb509.

* Fix typos

* Indent

* Refactor get_guidance_scale_embedding method in LEditsPPPipelineStableDiffusionXL class

tests/pipelines/dit/test_dit.py

@@ -74,7 +74,7 @@ class DiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "class_labels": [1],
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/latent_diffusion/test_latent_diffusion.py

@@ -113,7 +113,7 @@ class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -153,7 +153,7 @@ class LDMTextToImagePipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -189,7 +189,7 @@ class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 50,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py

@@ -84,7 +84,7 @@ class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
         init_image = self.dummy_image.to(device)
 
         generator = torch.Generator(device=device).manual_seed(0)
-        image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="numpy").images
+        image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -109,7 +109,7 @@ class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
 
         init_image = self.dummy_image.to(torch_device)
 
-        image = ldm(init_image, num_inference_steps=2, output_type="numpy").images
+        image = ldm(init_image, num_inference_steps=2, output_type="np").images
 
         assert image.shape == (1, 64, 64, 3)
 
@@ -128,7 +128,7 @@ class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):
         ldm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="numpy").images
+        image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/paint_by_example/test_paint_by_example.py

@@ -117,7 +117,7 @@ class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/pndm/test_pndm.py

@@ -49,10 +49,10 @@ class PNDMPipelineFastTests(unittest.TestCase):
         pndm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
+        image = pndm(generator=generator, num_inference_steps=20, output_type="np").images
 
         generator = torch.manual_seed(0)
-        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
+        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="np", return_dict=False)[0]
 
         image_slice = image[0, -3:, -3:, -1]
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
@@ -77,7 +77,7 @@ class PNDMPipelineIntegrationTests(unittest.TestCase):
         pndm.to(torch_device)
         pndm.set_progress_bar_config(disable=None)
         generator = torch.manual_seed(0)
-        image = pndm(generator=generator, output_type="numpy").images
+        image = pndm(generator=generator, output_type="np").images
 
         image_slice = image[0, -3:, -3:, -1]
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py

@@ -46,7 +46,7 @@ class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.Tes
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py

@@ -55,7 +55,7 @@ class OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unitt
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py

@@ -55,7 +55,7 @@ class OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unitt
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -775,7 +775,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -950,7 +950,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             generator=generator,
             guidance_scale=7.5,
             num_inference_steps=2,
-            output_type="numpy",
+            output_type="np",
         )
         image_chunked = output_chunked.images
 
@@ -966,7 +966,7 @@ class StableDiffusionPipelineSlowTests(unittest.TestCase):
             generator=generator,
             guidance_scale=7.5,
             num_inference_steps=2,
-            output_type="numpy",
+            output_type="np",
         )
         image = output.images
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -179,7 +179,7 @@ class StableDiffusionImg2ImgPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -199,7 +199,7 @@ class StableDiffusionInpaintPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -470,7 +470,7 @@ class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipeli
             "generator": [generator1, generator2],
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -586,7 +586,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -847,7 +847,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -1072,7 +1072,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 50,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py

@@ -131,7 +131,7 @@ class StableDiffusionInstructPix2PixPipelineFastTests(
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "image_guidance_scale": 1,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -288,7 +288,7 @@ class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase):
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "image_guidance_scale": 1.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion.py

@@ -151,7 +151,7 @@ class StableDiffusion2PipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -336,7 +336,7 @@ class StableDiffusion2PipelineSlowTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -557,7 +557,7 @@ class StableDiffusion2PipelineNightlyTests(unittest.TestCase):
             "generator": generator,
             "num_inference_steps": 50,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py

@@ -138,7 +138,7 @@ class StableDiffusionAttendAndExcitePipelineFastTests(
             "generator": generator,
             "num_inference_steps": 1,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "max_iter_to_alter": 2,
             "thresholds": {0: 0.7},
         }
@@ -225,7 +225,7 @@ class StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):
             generator=generator,
             num_inference_steps=5,
             max_iter_to_alter=5,
-            output_type="numpy",
+            output_type="np",
         ).images[0]
 
         expected_image = load_numpy(

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -174,7 +174,7 @@ class StableDiffusionDepth2ImgPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -395,7 +395,7 @@ class StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase):
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -534,7 +534,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_diffedit.py

@@ -143,7 +143,7 @@ class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, Pipeli
             "num_inference_steps": 2,
             "inpaint_strength": 1.0,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs
@@ -165,7 +165,7 @@ class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, Pipeli
             "num_maps_per_mask": 2,
             "mask_encode_strength": 1.0,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
 
         return inputs
@@ -186,7 +186,7 @@ class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, Pipeli
             "inpaint_strength": 1.0,
             "guidance_scale": 6.0,
             "decode_latents": True,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -417,7 +417,7 @@ class StableDiffusionDiffEditPipelineNightlyTests(unittest.TestCase):
             negative_prompt=source_prompt,
             inpaint_strength=0.7,
             num_inference_steps=25,
-            output_type="numpy",
+            output_type="np",
         ).images[0]
 
         expected_image = (

tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py

@@ -129,7 +129,7 @@ class StableDiffusion2InpaintPipelineFastTests(
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py

@@ -155,7 +155,7 @@ class StableDiffusionLatentUpscalePipelineFastTests(
             "image": self.dummy_image.cpu(),
             "generator": generator,
             "num_inference_steps": 2,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py

@@ -308,7 +308,7 @@ class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
 
-        output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy")
+        output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="np")
         image = output.images
 
         image_slice = image[0, 253:256, 253:256, -1]
@@ -335,7 +335,7 @@ class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
         prompt = "a photograph of an astronaut riding a horse"
         generator = torch.manual_seed(0)
         image = sd_pipe(
-            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy"
+            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="np"
         ).images
 
         image_slice = image[0, 253:256, 253:256, -1]
@@ -357,7 +357,7 @@ class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
         pipe.enable_attention_slicing()
         generator = torch.manual_seed(0)
         output_chunked = pipe(
-            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
+            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="np"
         )
         image_chunked = output_chunked.images
 
@@ -369,7 +369,7 @@ class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
         # disable slicing
         pipe.disable_attention_slicing()
         generator = torch.manual_seed(0)
-        output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")
+        output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="np")
         image = output.images
 
         # make sure that more than 3.0 GB is allocated

tests/pipelines/stable_diffusion_adapter/test_stable_diffusion_adapter.py

@@ -246,7 +246,7 @@ class AdapterTests:
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs