more review fixes

stevhliu · stevhliu · commit 9779237db53e · 2023-07-18T11:37:55.000-07:00
diff --git a/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py b/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
@@ -228,8 +228,8 @@ def __call__(
 
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.ImagePipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated images.
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
         # 0. Prepare call parameters
         img_size = self.unet.config.sample_size
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
@@ -136,35 +136,32 @@ def __init__(
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
         r"""
-        Enable sliced VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.vae.enable_slicing()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
     def disable_vae_slicing(self):
         r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_slicing()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
     def enable_vae_tiling(self):
         r"""
-        Enable tiled VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
-        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
         """
         self.vae.enable_tiling()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
     def disable_vae_tiling(self):
         r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_tiling()
diff --git a/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py b/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py
@@ -94,8 +94,8 @@ def __call__(
 
         Returns:
             [`~pipelines.AudioPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.AudioPipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated audio.
+                If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated audio.
         """
 
         if audio_length_in_s is None:
diff --git a/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py b/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py
@@ -103,7 +103,7 @@ def __call__(
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~ImagePipelineOutput`] instead of a plain tuple.
+                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
 
         Example:
 
diff --git a/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py b/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py
@@ -94,7 +94,7 @@ def __call__(
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~ImagePipelineOutput`] instead of a plain tuple.
+                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
 
         Example:
 
diff --git a/src/diffusers/pipelines/pndm/pipeline_pndm.py b/src/diffusers/pipelines/pndm/pipeline_pndm.py
@@ -91,8 +91,8 @@ def __call__(
 
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.ImagePipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated images.
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf
diff --git a/src/diffusers/pipelines/repaint/pipeline_repaint.py b/src/diffusers/pipelines/repaint/pipeline_repaint.py
@@ -180,8 +180,8 @@ def __call__(
 
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.ImagePipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated images.
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
 
         original_image = image
diff --git a/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py b/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py
@@ -68,8 +68,8 @@ def __call__(
 
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.ImagePipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated images.
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
 
         img_size = self.unet.config.sample_size
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py
@@ -598,9 +598,7 @@ def __call__(
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
+                otherwise a `tuple` is returned where the first element is a list with the generated images.
         """
         # 1. Check inputs
         self.check_inputs(
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
@@ -357,9 +357,7 @@ def __call__(
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
+                otherwise a `tuple` is returned where the first element is a list with the generated images.
         """
 
         # 1. Check inputs
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
@@ -614,8 +614,8 @@ def __call__(
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a
-                plain tuple.
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
             callback (`Callable`, *optional*):
                 A function that will be called every `callback_steps` steps during inference. The function will be
                 called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
@@ -641,11 +641,9 @@ def __call__(
         Examples:
 
         Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
-            element is a list of `bool`s denoting whether the corresponding generated image likely represents
-            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
         """
         # 0. Default height and width to unet
         height = height or self.default_sample_size * self.vae_scale_factor
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
@@ -258,35 +258,32 @@ def __init__(
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
         r"""
-        Enable sliced VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.vae.enable_slicing()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
     def disable_vae_slicing(self):
         r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_slicing()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
     def enable_vae_tiling(self):
         r"""
-        Enable tiled VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
-        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
         """
         self.vae.enable_tiling()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
     def disable_vae_tiling(self):
         r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_tiling()
diff --git a/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py b/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py
@@ -72,8 +72,8 @@ def __call__(
 
         Returns:
             [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.utils.ImagePipelineOutput`] is returned, otherwise a `tuple`
-                is returned where the first element is a list with the generated images.
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
 
         img_size = self.unet.config.sample_size
diff --git a/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py b/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py
@@ -203,17 +203,15 @@ def __init__(
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
         r"""
-        Enable sliced VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.vae.enable_slicing()
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
     def disable_vae_slicing(self):
         r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
         computing decoding in one step.
         """
         self.vae.disable_slicing()
diff --git a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py
@@ -391,9 +391,9 @@ def dual_guided(
         ```
 
         Returns:
-            [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.ImagePipelineOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is a list with the generated images.
+            [`~pipelines.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
 
         expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
diff --git a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py
@@ -430,8 +430,7 @@ def __call__(
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
             callback (`Callable`, *optional*):
                 A function that calls every `callback_steps` steps during inference. The function is called with the
                 following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
@@ -471,9 +470,9 @@ def __call__(
         ```
 
         Returns:
-            [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.ImagePipelineOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is a list with the generated images.
+            [`~pipelines.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
         # 0. Default height and width to unet
         height = height or self.image_unet.config.sample_size * self.vae_scale_factor
diff --git a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py
@@ -319,9 +319,7 @@ def __call__(
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
+                otherwise a `tuple` is returned where the first element is a list with the generated images.
         """
         # 0. Default height and width to unet
         height = height or self.image_unet.config.sample_size * self.vae_scale_factor
diff --git a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py
@@ -393,9 +393,7 @@ def __call__(
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
+                otherwise a `tuple` is returned where the first element is a list with the generated images.
         """
         # 0. Default height and width to unet
         height = height or self.image_unet.config.sample_size * self.vae_scale_factor
diff --git a/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py b/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py
@@ -212,9 +212,9 @@ def __call__(
                 every step.
 
         Returns:
-            [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.ImagePipelineOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is a list with the generated images.
+            [`~pipelines.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images.
         """
         if isinstance(prompt, str):
             batch_size = 1
