Fix typos and grammar errors in documentation (#13391)

- Fix 'allows to generate' -> 'allows you to generate' in controlling_generation.md
- Fix 'it's refiner' -> 'its refiner' (possessive) in sdxl.md
- Fix 'it's state' -> 'its state' (possessive) in reusing_seeds.md
- Fix missing word 'you'll a function' -> 'you'll create a function' in sdxl.md

Author: GalacticAvenger

docs/source/en/training/sdxl.md

@@ -100,7 +100,7 @@ accelerate launch train_text_to_image_sdxl.py \
 
 The training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support SDXL training. This guide will focus on the code that is unique to the SDXL training script.
 
-It starts by creating functions to [tokenize the prompts](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L478) to calculate the prompt embeddings, and to compute the image embeddings with the [VAE](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L519). Next, you'll a function to [generate the timesteps weights](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L531) depending on the number of timesteps and the timestep bias strategy to apply.
+It starts by creating functions to [tokenize the prompts](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L478) to calculate the prompt embeddings, and to compute the image embeddings with the [VAE](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L519). Next, you'll create a function to [generate the timesteps weights](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L531) depending on the number of timesteps and the timestep bias strategy to apply.
 
 Within the [`main()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L572) function, in addition to loading a tokenizer, the script loads a second tokenizer and text encoder because the SDXL architecture uses two of each:
 
@@ -250,5 +250,5 @@ print(f'Inference time is {time()-start} sec after compilation')
 
 Congratulations on training a SDXL model! To learn more about how to use your new model, the following guides may be helpful:
 
-- Read the [Stable Diffusion XL](../using-diffusers/sdxl) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting), how to use it's refiner model, and the different types of micro-conditionings.
+- Read the [Stable Diffusion XL](../using-diffusers/sdxl) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting), how to use its refiner model, and the different types of micro-conditionings.
 - Check out the [DreamBooth](dreambooth) and [LoRA](lora) training guides to learn how to train a personalized SDXL model with just a few example images. These two training techniques can even be combined!

docs/source/en/using-diffusers/controlling_generation.md

@@ -111,7 +111,7 @@ It conditions on a monocular depth estimate of the original image.
 [Paper](https://huggingface.co/papers/2302.08113)
 
 MultiDiffusion Panorama defines a new generation process over a pre-trained diffusion model. This process binds together multiple diffusion generation methods that can be readily applied to generate high quality and diverse images. Results adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.
-MultiDiffusion Panorama allows to generate high-quality images at arbitrary aspect ratios (e.g., panoramas).
+MultiDiffusion Panorama allows you to generate high-quality images at arbitrary aspect ratios (e.g., panoramas).
 
 ## Fine-tuning your own models
 

docs/source/en/using-diffusers/reusing_seeds.md

@@ -60,7 +60,7 @@ print(np.abs(image).sum())
 </hfoption>
 </hfoptions>
 
-The `Generator` object should be passed to the pipeline instead of an integer seed. `Generator` maintains a *random state* that is consumed and modified when used. Once consumed, the same `Generator` object produces different results in subsequent calls, even across different pipelines, because it's *state* has changed.
+The `Generator` object should be passed to the pipeline instead of an integer seed. `Generator` maintains a *random state* that is consumed and modified when used. Once consumed, the same `Generator` object produces different results in subsequent calls, even across different pipelines, because its *state* has changed.
 
 ```py
 generator = torch.manual_seed(0)