Errata (#8322)

* Fix typos

* Trim trailing whitespaces

* Remove a trailing whitespace

* chore: Update MarigoldDepthPipeline checkpoint to prs-eth/marigold-lcm-v1-0

* Revert "chore: Update MarigoldDepthPipeline checkpoint to prs-eth/marigold-lcm-v1-0"

This reverts commit fd742b30b4258106008a6af4d0dd4664904f8595.

* pokemon -> naruto

* `DPMSolverMultistep` -> `DPMSolverMultistepScheduler`

* Improve Markdown stylization

* Improve style

* Improve style

* Refactor pipeline variable names for consistency

* up style

docs/source/en/api/pipelines/pixart.md

@@ -75,7 +75,7 @@ with torch.no_grad():
     prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
 ```
 
-Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up som GPU VRAM:
+Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:
 
 ```python
 import gc
@@ -146,4 +146,3 @@ While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could al
 [[autodoc]] PixArtAlphaPipeline
 	- all
 	- __call__
-	
\ No newline at end of file

docs/source/en/api/pipelines/pixart_sigma.md

@@ -59,7 +59,6 @@ text_encoder = T5EncoderModel.from_pretrained(
     subfolder="text_encoder",
     load_in_8bit=True,
     device_map="auto",
-
 )
 pipe = PixArtSigmaPipeline.from_pretrained(
     "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
@@ -77,7 +76,7 @@ with torch.no_grad():
     prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
 ```
 
-Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up som GPU VRAM:
+Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:
 
 ```python
 import gc
@@ -148,4 +147,3 @@ While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could al
 [[autodoc]] PixArtSigmaPipeline
 	- all
 	- __call__
-	
\ No newline at end of file

docs/source/en/api/pipelines/stable_diffusion/overview.md

@@ -177,7 +177,7 @@ inpaint = StableDiffusionInpaintPipeline(**text2img.components)
 
 The Stable Diffusion pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:
 
-```
+```sh
 pip install -U gradio
 ```
 

docs/source/en/api/schedulers/edm_multistep_dpm_solver.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # EDMDPMSolverMultistepScheduler
 
-`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistep`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
+`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
 
 DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
 samples, and it can generate quite good samples even in 10 steps.

docs/source/en/api/schedulers/multistep_dpm_solver.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # DPMSolverMultistepScheduler
 
-`DPMSolverMultistep` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
+`DPMSolverMultistepScheduler` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
 
 DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
 samples, and it can generate quite good samples even in 10 steps.

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

@@ -415,7 +415,7 @@ image = diffusers.utils.load_image(
 
 pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
     "prs-eth/marigold-depth-lcm-v1-0", torch_dtype=torch.float16, variant="fp16"
-).to("cuda")
+).to(device)
 
 depth_image = pipe(image, generator=generator).prediction
 depth_image = pipe.image_processor.visualize_depth(depth_image, color_map="binary")
@@ -423,10 +423,10 @@ depth_image[0].save("motorcycle_controlnet_depth.png")
 
 controlnet = diffusers.ControlNetModel.from_pretrained(
     "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
-).to("cuda")
+).to(device)
 pipe = diffusers.StableDiffusionXLControlNetPipeline.from_pretrained(
     "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnet
-).to("cuda")
+).to(device)
 pipe.scheduler = diffusers.DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
 
 controlnet_out = pipe(

docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md

@@ -34,7 +34,7 @@ Stable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattman
 SDXL을 사용하기 전에 `transformers`, `accelerate`, `safetensors` 와 `invisible_watermark`를 설치하세요.
 다음과 같이 라이브러리를 설치할 수 있습니다:
 
-```
+```sh
 pip install transformers
 pip install accelerate
 pip install safetensors
@@ -46,7 +46,7 @@ pip install invisible-watermark>=0.2.0
 Stable Diffusion XL로 이미지를 생성할 때 워터마크가 보이지 않도록 추가하는 것을 권장하는데, 이는 다운스트림(downstream) 어플리케이션에서 기계에 합성되었는지를 식별하는데 도움을 줄 수 있습니다. 그렇게 하려면 [invisible_watermark 라이브러리](https://pypi.org/project/invisible-watermark/)를 통해 설치해주세요:
 
 
-```
+```sh
 pip install invisible-watermark>=0.2.0
 ```
 
@@ -352,7 +352,7 @@ out-of-memory 에러가 난다면, [`StableDiffusionXLPipeline.enable_model_cpu_
 
 **참고** Stable Diffusion XL을 `torch`가 2.0 버전 미만에서 실행시키고 싶을 때, xformers 어텐션을 사용해주세요:
 
-```
+```sh
 pip install xformers
 ```
 

docs/source/ko/installation.md

@@ -93,13 +93,13 @@ cd diffusers
 
 **PyTorch의 경우**
 
-```
+```sh
 pip install -e ".[torch]"
 ```
 
 **Flax의 경우**
 
-```
+```sh
 pip install -e ".[flax]"
 ```
 

docs/source/ko/optimization/onnx.md

@@ -19,7 +19,7 @@ specific language governing permissions and limitations under the License.
 
 다음 명령어로 ONNX Runtime를 지원하는 🤗 Optimum를 설치합니다:
 
-```
+```sh
 pip install optimum["onnxruntime"]
 ```
 

docs/source/ko/optimization/open_vino.md

@@ -19,7 +19,7 @@ specific language governing permissions and limitations under the License.
 
 다음 명령어로 🤗 Optimum을 설치합니다:
 
-```
+```sh
 pip install optimum["openvino"]
 ```
 

docs/source/ko/using-diffusers/weighted_prompts.md

@@ -59,7 +59,7 @@ image
 
 먼저 `compel` 라이브러리를 설치해야합니다:
 
-```
+```sh
 pip install compel
 ```
 

docs/source/zh/installation.md

@@ -95,13 +95,13 @@ cd diffusers
 
 **PyTorch**
 
-```
+```sh
 pip install -e ".[torch]"
 ```
 
 **Flax**
 
-```
+```sh
 pip install -e ".[flax]"
 ```
 

examples/community/clip_guided_stable_diffusion_img2img.py

@@ -25,7 +25,7 @@ from diffusers.utils.torch_utils import randn_tensor
 
 EXAMPLE_DOC_STRING = """
     Examples:
-        ```
+        ```py
         from io import BytesIO
 
         import requests