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

src/diffusers/pipelines/flux/pipeline_flux_fill.py

@@ -775,7 +775,7 @@ class FluxFillPipeline(
                 1)`, or `(H, W)`.
             mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`):
                 `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask
-                latents tensor will ge generated by `mask_image`.
+                latents tensor will be generated by `mask_image`.
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
@@ -807,7 +807,7 @@ class FluxFillPipeline(
             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/flux/pipeline_flux_img2img.py

@@ -787,7 +787,7 @@ class FluxImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
             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/flux/pipeline_flux_inpaint.py

@@ -834,7 +834,7 @@ class FluxInpaintPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FluxIPAdapterM
                 1)`, or `(H, W)`.
             mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`):
                 `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask
-                latents tensor will ge generated by `mask_image`.
+                latents tensor will be generated by `mask_image`.
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
@@ -873,7 +873,7 @@ class FluxInpaintPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FluxIPAdapterM
             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/flux/pipeline_flux_kontext.py

@@ -808,7 +808,7 @@ class FluxKontextPipeline(
             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/flux/pipeline_flux_kontext_inpaint.py

@@ -1029,7 +1029,7 @@ class FluxKontextInpaintPipeline(
             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/hidream_image/pipeline_hidream_image.py

@@ -789,7 +789,7 @@ class HiDreamImagePipeline(DiffusionPipeline, HiDreamImageLoraLoaderMixin):
             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/kandinsky/pipeline_kandinsky.py

@@ -291,7 +291,7 @@ class KandinskyPipeline(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/kandinsky/pipeline_kandinsky_combined.py

@@ -271,7 +271,7 @@ class KandinskyCombinedPipeline(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`).
@@ -502,7 +502,7 @@ class KandinskyImg2ImgCombinedPipeline(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`).
@@ -742,7 +742,7 @@ class KandinskyInpaintCombinedPipeline(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/kandinsky/pipeline_kandinsky_inpaint.py

@@ -469,7 +469,7 @@ class KandinskyInpaintPipeline(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/kandinsky/pipeline_kandinsky_prior.py

@@ -212,7 +212,7 @@ class KandinskyPriorPipeline(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`.
             negative_prior_prompt (`str`, *optional*):
                 The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
                 `guidance_scale` is less than `1`).
@@ -437,7 +437,7 @@ class KandinskyPriorPipeline(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`.
             guidance_scale (`float`, *optional*, defaults to 4.0):
                 Guidance scale as defined in [Classifier-Free Diffusion
                 Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py

@@ -175,7 +175,7 @@ class KandinskyV22Pipeline(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/kandinsky2_2/pipeline_kandinsky2_2_combined.py

@@ -262,7 +262,7 @@ class KandinskyV22CombinedPipeline(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`).
@@ -512,7 +512,7 @@ class KandinskyV22Img2ImgCombinedPipeline(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`).
@@ -749,7 +749,7 @@ class KandinskyV22InpaintCombinedPipeline(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/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py

@@ -211,7 +211,7 @@ class KandinskyV22ControlnetPipeline(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/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py

@@ -356,7 +356,7 @@ class KandinskyV22InpaintPipeline(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/kandinsky2_2/pipeline_kandinsky2_2_prior.py

@@ -171,7 +171,7 @@ class KandinskyV22PriorPipeline(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`.
             negative_prior_prompt (`str`, *optional*):
                 The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
                 `guidance_scale` is less than `1`).
@@ -412,7 +412,7 @@ class KandinskyV22PriorPipeline(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`.
             guidance_scale (`float`, *optional*, defaults to 4.0):
                 Guidance scale as defined in [Classifier-Free Diffusion
                 Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py

@@ -195,7 +195,7 @@ class KandinskyV22PriorEmb2EmbPipeline(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`.
             negative_prior_prompt (`str`, *optional*):
                 The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
                 `guidance_scale` is less than `1`).

src/diffusers/pipelines/kolors/pipeline_kolors.py

@@ -749,7 +749,7 @@ class KolorsPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionLor
             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`.
             prompt_embeds (`torch.Tensor`, *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/kolors/pipeline_kolors_img2img.py

@@ -900,7 +900,7 @@ class KolorsImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffu
             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`.
             prompt_embeds (`torch.Tensor`, *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/latte/pipeline_latte.py

@@ -679,7 +679,7 @@ class LattePipeline(DiffusionPipeline):
             latents (`torch.FloatTensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video
                 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/ltx/pipeline_ltx.py

@@ -601,7 +601,7 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
             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`.
             prompt_embeds (`torch.Tensor`, *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.