Fix typo: 'will ge generated' -> 'will be generated' (#12231)

src/diffusers/pipelines/visualcloze/pipeline_visualcloze_combined.py

@@ -319,7 +319,7 @@ class VisualClozePipeline(
             latents (`torch.FloatTensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will be generated by sampling using the supplied random `generator`.
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.

src/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

@@ -736,7 +736,7 @@ class VisualClozeGenerationPipeline(
             latents (`torch.FloatTensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will be generated by sampling using the supplied random `generator`.
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py

@@ -263,7 +263,7 @@ class WuerstchenDecoderPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
             latents (`torch.Tensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will be generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
                 (`np.array`) or `"pt"` (`torch.Tensor`).

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py

@@ -222,7 +222,7 @@ class WuerstchenCombinedPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
             latents (`torch.Tensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will be generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
                 (`np.array`) or `"pt"` (`torch.Tensor`).

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py

@@ -348,7 +348,7 @@ class WuerstchenPriorPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin)
             latents (`torch.Tensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will be generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
                 (`np.array`) or `"pt"` (`torch.Tensor`).