[Docs] Fix typos, improve, update at Tutorials page (#5586)

* Fix typos, improve, update

* Update autopipeline.md

* Update docs/source/en/tutorials/using_peft_for_inference.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/tutorials/using_peft_for_inference.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/tutorials/using_peft_for_inference.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/en/tutorials/autopipeline.md

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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+
 # AutoPipeline
 
 🤗 Diffusers is able to complete many different tasks, and you can often reuse the same pretrained weights for multiple tasks such as text-to-image, image-to-image, and inpainting. If you're new to the library and diffusion models though, it may be difficult to know which pipeline to use for a task. For example, if you're using the [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image, you might not know that you could also use it for image-to-image and inpainting by loading the checkpoint with the [`StableDiffusionImg2ImgPipeline`] and [`StableDiffusionInpaintPipeline`] classes respectively.
@@ -6,7 +18,7 @@ The `AutoPipeline` class is designed to simplify the variety of pipelines in
 
 <Tip>
 
-Take a look at the [AutoPipeline](./pipelines/auto_pipeline) reference to see which tasks are supported. Currently, it supports text-to-image, image-to-image, and inpainting.
+Take a look at the [AutoPipeline](../api/pipelines/auto_pipeline) reference to see which tasks are supported. Currently, it supports text-to-image, image-to-image, and inpainting.
 
 </Tip>
 
@@ -26,6 +38,7 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
 prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"
 
 image = pipeline(prompt, num_inference_steps=25).images[0]
+image
 ```
 
 <div class="flex justify-center">
@@ -35,12 +48,16 @@ image = pipeline(prompt, num_inference_steps=25).images[0]
 Under the hood, [`AutoPipelineForText2Image`]:
 
 1. automatically detects a `"stable-diffusion"` class from the [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) file
-2. loads the corresponding text-to-image [`StableDiffusionPipline`] based on the `"stable-diffusion"` class name
+2. loads the corresponding text-to-image [`StableDiffusionPipeline`] based on the `"stable-diffusion"` class name
 
 Likewise, for image-to-image, [`AutoPipelineForImage2Image`] detects a `"stable-diffusion"` checkpoint from the `model_index.json` file and it'll load the corresponding [`StableDiffusionImg2ImgPipeline`] behind the scenes. You can also pass any additional arguments specific to the pipeline class such as `strength`, which determines the amount of noise or variation added to an input image: 
 
 ```py
 from diffusers import AutoPipelineForImage2Image
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
@@ -56,6 +73,7 @@ image = Image.open(BytesIO(response.content)).convert("RGB")
 image.thumbnail((768, 768))
 
 image = pipeline(prompt, image, num_inference_steps=200, strength=0.75, guidance_scale=10.5).images[0]
+image
 ```
 
 <div class="flex justify-center">
@@ -67,6 +85,7 @@ And if you want to do inpainting, then [`AutoPipelineForInpainting`] loads the u
 ```py
 from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image
+import torch
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True
@@ -80,6 +99,7 @@ mask_image = load_image(mask_url).convert("RGB")
 
 prompt = "A majestic tiger sitting on a bench"
 image = pipeline(prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]
+image
 ```
 
 <div class="flex justify-center">
@@ -106,6 +126,7 @@ The [`~AutoPipelineForImage2Image.from_pipe`] method detects the original pipeli
 
 ```py
 from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
+import torch
 
 pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
@@ -126,6 +147,7 @@ If you passed an optional argument - like disabling the safety checker - to the
 
 ```py
 from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
+import torch
 
 pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
@@ -135,7 +157,7 @@ pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
 ).to("cuda")
 
 pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)
-print(pipe.config.requires_safety_checker)
+print(pipeline_img2img.config.requires_safety_checker)
 "False"
 ```
 
@@ -143,4 +165,6 @@ You can overwrite any of the arguments and even configuration from the original
 
 ```py
 pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img, requires_safety_checker=True, strength=0.3)
+print(pipeline_img2img.config.requires_safety_checker)
+"True"
 ```

docs/source/en/tutorials/basic_training.md

@@ -31,7 +31,7 @@ Before you begin, make sure you have 🤗 Datasets installed to load and preproc
 #!pip install diffusers[training]
 ```
 
-We encourage you to share your model with the community, and in order to do that, you'll need to login to your Hugging Face account (create one [here](https://hf.co/join) if you don't already have one!). You can login from a notebook and enter your token when prompted:
+We encourage you to share your model with the community, and in order to do that, you'll need to login to your Hugging Face account (create one [here](https://hf.co/join) if you don't already have one!). You can login from a notebook and enter your token when prompted. Make sure your token has the write role.
 
 ```py
 >>> from huggingface_hub import notebook_login
@@ -59,7 +59,6 @@ For convenience, create a `TrainingConfig` class containing the training hyperpa
 ```py
 >>> from dataclasses import dataclass
 
-
 >>> @dataclass
 ... class TrainingConfig:
 ...     image_size = 128  # the generated image resolution
@@ -75,6 +74,7 @@ For convenience, create a `TrainingConfig` class containing the training hyperpa
 ...     output_dir = "ddpm-butterflies-128"  # the model name locally and on the HF Hub
 
 ...     push_to_hub = True  # whether to upload the saved model to the HF Hub
+...     hub_model_id = "<your-username>/<my-awesome-model>"  # the name of the repository to create on the HF Hub
 ...     hub_private_repo = False
 ...     overwrite_output_dir = True  # overwrite the old model when re-running the notebook
 ...     seed = 0
@@ -253,10 +253,8 @@ Then, you'll need a way to evaluate the model. For evaluation, you can use the [
 ```py
 >>> from diffusers import DDPMPipeline
 >>> from diffusers.utils import make_image_grid
->>> import math
 >>> import os
 
-
 >>> def evaluate(config, epoch, pipeline):
 ...     # Sample some images from random noise (this is the backward diffusion process).
 ...     # The default pipeline output type is `List[PIL.Image]`

docs/source/en/tutorials/using_peft_for_inference.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 # Inference with PEFT
 
-There are many adapters trained in different styles to achieve different effects. You can even combine multiple adapters to create new and unique images. With the 🤗 [PEFT](https://huggingface.co/docs/peft/index) integration in 🤗 Diffusers, it is really easy to load and manage adapters for inference. In this guide, you'll learn how to use different adapters with [Stable Diffusion XL (SDXL)](./pipelines/stable_diffusion/stable_diffusion_xl) for inference.
+There are many adapters trained in different styles to achieve different effects. You can even combine multiple adapters to create new and unique images. With the 🤗 [PEFT](https://huggingface.co/docs/peft/index) integration in 🤗 Diffusers, it is really easy to load and manage adapters for inference. In this guide, you'll learn how to use different adapters with [Stable Diffusion XL (SDXL)](../api/pipelines/stable_diffusion/stable_diffusion_xl) for inference.
 
 Throughout this guide, you'll use LoRA as the main adapter technique, so we'll use the terms LoRA and adapter interchangeably. You should have some familiarity with LoRA, and if you don't, we welcome you to check out the [LoRA guide](https://huggingface.co/docs/peft/conceptual_guides/lora).
 
@@ -63,7 +63,7 @@ image
 
 With the `adapter_name` parameter, it is really easy to use another adapter for inference! Load the [nerijs/pixel-art-xl](https://huggingface.co/nerijs/pixel-art-xl) adapter that has been fine-tuned to generate pixel art images, and let's call it `"pixel"`.
 
-The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter. But you can activate the `"pixel"` adapter with the [`~diffusers.loaders.set_adapters`] method as shown below:
+The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter. But you can activate the `"pixel"` adapter with the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method as shown below:
 
 ```python
 pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
@@ -86,7 +86,7 @@ image
 
 You can also perform multi-adapter inference where you combine different adapter checkpoints for inference.
 
-Once again, use the [`~diffusers.loaders.set_adapters`] method to activate two LoRA checkpoints and specify the weight for how the checkpoints should be combined.
+Once again, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method to activate two LoRA checkpoints and specify the weight for how the checkpoints should be combined.
 
 ```python
 pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0])
@@ -116,7 +116,7 @@ image
     
 Impressive! As you can see, the model was able to generate an image that mixes the characteristics of both adapters.
 
-If you want to go back to using only one adapter, use the [`~diffusers.loaders.set_adapters`] method to activate the `"toy"` adapter:
+If you want to go back to using only one adapter, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method to activate the `"toy"` adapter:
 
 ```python
 # First, set the adapter.
@@ -134,7 +134,7 @@ image
 ![toy-face-again](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_18_1.png)
 
 
-If you want to switch to only the base model, disable all LoRAs with the [`~diffusers.loaders.disable_lora`] method.
+If you want to switch to only the base model, disable all LoRAs with the [`~diffusers.loaders.UNet2DConditionLoadersMixin.disable_lora`] method.
 
 
 ```python
@@ -150,16 +150,18 @@ image
 
 ## Monitoring active adapters
 
-You have attached multiple adapters in this tutorial, and if you're feeling a bit lost on what adapters have been attached to the pipeline's components, you can easily check the list of active adapters using the [`~diffusers.loaders.get_active_adapters`] method:
+You have attached multiple adapters in this tutorial, and if you're feeling a bit lost on what adapters have been attached to the pipeline's components, you can easily check the list of active adapters using the [`~diffusers.loaders.LoraLoaderMixin.get_active_adapters`] method:
 
-```python
+```py
 active_adapters = pipe.get_active_adapters()
->>> ["toy", "pixel"]
+active_adapters
+["toy", "pixel"]
 ```
 
-You can also get the active adapters of each pipeline component with [`~diffusers.loaders.get_list_adapters`]:
+You can also get the active adapters of each pipeline component with [`~diffusers.loaders.LoraLoaderMixin.get_list_adapters`]:
 
-```python
+```py
 list_adapters_component_wise = pipe.get_list_adapters()
->>> {"text_encoder": ["toy", "pixel"], "unet": ["toy", "pixel"], "text_encoder_2": ["toy", "pixel"]}
+list_adapters_component_wise
+{"text_encoder": ["toy", "pixel"], "unet": ["toy", "pixel"], "text_encoder_2": ["toy", "pixel"]}
 ```

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

@@ -290,5 +290,5 @@ This is really what 🧨 Diffusers is designed for: to make it intuitive and eas
 
 For your next steps, feel free to:
 
-* Learn how to [build and contribute a pipeline](contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!
+* Learn how to [build and contribute a pipeline](../using-diffusers/contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!
 * Explore [existing pipelines](../api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately.