Errata: Fix typos & `\s+$` (#9008)

* Fix typos

* chore: Fix typos

* chore: Update README.md for promptdiffusion example

* Trim trailing white spaces

* Fix a typo

* update number

* chore: update number

* Trim trailing white space

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

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

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

.github/workflows/benchmark.yml

@@ -13,13 +13,13 @@ env:
 
 jobs:
   torch_pipelines_cuda_benchmark_tests:
-    env: 
+    env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
     name: Torch Core Pipelines CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
-    runs-on: 
+    runs-on:
       group: aws-g6-4xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-compile-cuda
@@ -59,7 +59,7 @@ jobs:
         if: ${{ success() }}
         run: |
           pip install requests && python utils/notify_benchmarking_status.py --status=success
-        
+
       - name: Report failure status
         if: ${{ failure() }}
         run: |

.github/workflows/mirror_community_pipeline.yml

@@ -24,7 +24,7 @@ jobs:
   mirror_community_pipeline:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
-    
+
     runs-on: ubuntu-latest
     steps:
       # Checkout to correct ref
@@ -95,7 +95,7 @@ jobs:
         if: ${{ success() }}
         run: |
           pip install requests && python utils/notify_community_pipelines_mirror.py --status=success
-      
+
       - name: Report failure status
         if: ${{ failure() }}
         run: |

CONTRIBUTING.md

@@ -63,7 +63,7 @@ In the same spirit, you are of immense help to the community by answering such q
 
 **Please** keep in mind that the more effort you put into asking or answering a question, the higher
 the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
+In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
 **NOTE about channels**:
 [*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.

README.md

@@ -67,7 +67,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 27.000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 30,000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
@@ -209,7 +209,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +12.000 other amazing GitHub repositories 💪
+- +14,000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

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

@@ -18,7 +18,7 @@ It was developed by the Fal team and more details about it can be found in [this
 
 <Tip>
 
-AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. 
+AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details.
 
 </Tip>
 

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

@@ -12,13 +12,13 @@ specific language governing permissions and limitations under the License.
 
 # Flux
 
-Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs. 
+Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs.
 
 Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
 
 <Tip>
 
-Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. 
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.
 
 </Tip>
 
@@ -27,11 +27,11 @@ Flux comes in two variants:
 * Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
 * Guidance-distilled (`black-forest-labs/FLUX.1-dev`)
 
-Both checkpoints have slightly difference usage which we detail below. 
+Both checkpoints have slightly difference usage which we detail below.
 
 ### Timestep-distilled
 
-* `max_sequence_length` cannot be more than 256. 
+* `max_sequence_length` cannot be more than 256.
 * `guidance_scale` needs to be 0.
 * As this is a timestep-distilled model, it benefits from fewer sampling steps.
 
@@ -44,11 +44,11 @@ pipe.enable_model_cpu_offload()
 
 prompt = "A cat holding a sign that says hello world"
 out = pipe(
-    prompt=prompt, 
-    guidance_scale=0., 
-    height=768, 
-    width=1360, 
-    num_inference_steps=4, 
+    prompt=prompt,
+    guidance_scale=0.,
+    height=768,
+    width=1360,
+    num_inference_steps=4,
     max_sequence_length=256,
 ).images[0]
 out.save("image.png")
@@ -57,7 +57,7 @@ out.save("image.png")
 ### Guidance-distilled
 
 * The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
-* It doesn't have any limitations around the `max_sequence_length`. 
+* It doesn't have any limitations around the `max_sequence_length`.
 
 ```python
 import torch
@@ -68,10 +68,10 @@ pipe.enable_model_cpu_offload()
 
 prompt = "a tiny astronaut hatching from an egg on the moon"
 out = pipe(
-    prompt=prompt, 
-    guidance_scale=3.5, 
-    height=768, 
-    width=1360, 
+    prompt=prompt,
+    guidance_scale=3.5,
+    height=768,
+    width=1360,
     num_inference_steps=50,
 ).images[0]
 out.save("image.png")

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

@@ -59,7 +59,7 @@ First, load the pipeline:
 
 ```python
 from diffusers import LuminaText2ImgPipeline
-import torch 
+import torch
 
 pipeline = LuminaText2ImgPipeline.from_pretrained(
 	"Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
@@ -87,4 +87,4 @@ image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit w
 [[autodoc]] LuminaText2ImgPipeline
 	- all
 	- __call__
-	
+

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

@@ -16,7 +16,7 @@ Stable Audio was proposed in [Stable Audio Open](https://arxiv.org/abs/2407.1435
 
 Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three components: an autoencoder that compresses waveforms into a manageable sequence length, a T5-based text embedding for text conditioning, and a transformer-based diffusion (DiT) model that operates in the latent space of the autoencoder.
 
-Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT. 
+Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT.
 
 The abstract of the paper is the following:
 *Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*

docs/source/en/tutorials/fast_diffusion.md

@@ -35,7 +35,7 @@ pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu
 ```
 
 > [!TIP]
-> The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum. 
+> The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum.
 > If you're interested in the full benchmarking code, take a look at [huggingface/diffusion-fast](https://github.com/huggingface/diffusion-fast).
 
 
@@ -168,7 +168,7 @@ Using SDPA attention and compiling both the UNet and VAE cuts the latency from 3
 </div>
 
 > [!TIP]
-> From PyTorch 2.3.1, you can control the caching behavior of `torch.compile()`. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial. 
+> From PyTorch 2.3.1, you can control the caching behavior of `torch.compile()`. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial.
 
 ### Prevent graph breaks
 

docs/source/en/tutorials/inference_with_big_models.md

@@ -18,13 +18,13 @@ A modern diffusion model, like [Stable Diffusion XL (SDXL)](../using-diffusers/s
 * Two text encoders
 * A UNet for denoising
 
-Usually, the text encoders and the denoiser are much larger compared to the VAE. 
+Usually, the text encoders and the denoiser are much larger compared to the VAE.
 
 As models get bigger and better, it’s possible your model is so big that even a single copy won’t fit in memory. But that doesn’t mean it can’t be loaded. If you have more than one GPU, there is more memory available to store your model. In this case, it’s better to split your model checkpoint into several smaller *checkpoint shards*.
 
 When a text encoder checkpoint has multiple shards, like [T5-xxl for SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/tree/main/text_encoder_3), it is automatically handled by the [Transformers](https://huggingface.co/docs/transformers/index) library as it is a required dependency of Diffusers when using the [`StableDiffusion3Pipeline`]. More specifically, Transformers will automatically handle the loading of multiple shards within the requested model class and get it ready so that inference can be performed.
 
-The denoiser checkpoint can also have multiple shards and supports inference thanks to the [Accelerate](https://huggingface.co/docs/accelerate/index) library. 
+The denoiser checkpoint can also have multiple shards and supports inference thanks to the [Accelerate](https://huggingface.co/docs/accelerate/index) library.
 
 > [!TIP]
 > Refer to the [Handling big models for inference](https://huggingface.co/docs/accelerate/main/en/concept_guides/big_model_inference) guide for general guidance when working with big models that are hard to fit into memory.
@@ -43,7 +43,7 @@ unet.save_pretrained("sdxl-unet-sharded", max_shard_size="5GB")
 The size of the fp32 variant of the SDXL UNet checkpoint is ~10.4GB. Set the `max_shard_size` parameter to 5GB to create 3 shards. After saving, you can load them in [`StableDiffusionXLPipeline`]:
 
 ```python
-from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline 
+from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline
 import torch
 
 unet = UNet2DConditionModel.from_pretrained(
@@ -57,14 +57,14 @@ image = pipeline("a cute dog running on the grass", num_inference_steps=30).imag
 image.save("dog.png")
 ```
 
-If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you: 
+If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you:
 
 ```diff
 - pipeline.to("cuda")
 + pipeline.enable_model_cpu_offload()
 ```
 
-In general, we recommend sharding when a checkpoint is more than 5GB (in fp32). 
+In general, we recommend sharding when a checkpoint is more than 5GB (in fp32).
 
 ## Device placement
 

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

@@ -130,10 +130,10 @@ prompt = "a dog catching a frisbee in the jungle"
 
 generator = torch.Generator(device="cpu").manual_seed(0)
 image = pipeline(
-    prompt, 
-    image=init_image, 
-    strength=0.8, 
-    guidance_scale=guidance_scale, 
+    prompt,
+    image=init_image,
+    strength=0.8,
+    guidance_scale=guidance_scale,
     pag_scale=pag_scale,
     generator=generator).images[0]
 ```
@@ -161,14 +161,14 @@ pipeline_inpaint = AutoPipelineForInpaiting.from_pretrained("stabilityai/stable-
 pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_inpaint, enable_pag=True)
 ```
 
-This still works when your pipeline has a different task: 
+This still works when your pipeline has a different task:
 
 ```py
 pipeline_t2i = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
 pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_t2i, enable_pag=True)
 ```
 
-Let's generate an image! 
+Let's generate an image!
 
 ```py
 img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
@@ -258,7 +258,7 @@ for pag_scale in [0.0, 3.0]:
   </div>
 </div>
 
-## PAG with IP-Adapter 
+## PAG with IP-Adapter
 
 [IP-Adapter](https://hf.co/papers/2308.06721) is a popular model that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. You can enable PAG on a pipeline with IP-Adapter loaded.
 
@@ -317,7 +317,7 @@ PAG reduces artifacts and improves the overall compposition.
 </div>
 
 
-## Configure parameters 
+## Configure parameters
 
 ### pag_applied_layers
 

docs/source/ko/conceptual/philosophy.md

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# 철학 [[philosophy]] 
+# 철학 [[philosophy]]
 
 🧨 Diffusers는 다양한 모달리티에서 **최신의** 사전 훈련된 diffusion 모델을 제공합니다.
 그 목적은 추론과 훈련을 위한 **모듈식 툴박스**로 사용되는 것입니다.

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

@@ -52,7 +52,7 @@ pipeline = pipeline.to("cuda")
 
 Text-to-image의 경우 텍스트 프롬프트를 전달합니다. 기본적으로 SDXL Turbo는 512x512 이미지를 생성하며, 이 해상도에서 최상의 결과를 제공합니다. `height` 및 `width` 매개 변수를 768x768 또는 1024x1024로 설정할 수 있지만 이 경우 품질 저하를 예상할 수 있습니다.
 
-모델이 `guidance_scale` 없이 학습되었으므로 이를 0.0으로 설정해 비활성화해야 합니다. 단일 추론 스텝만으로도 고품질 이미지를 생성할 수 있습니다. 
+모델이 `guidance_scale` 없이 학습되었으므로 이를 0.0으로 설정해 비활성화해야 합니다. 단일 추론 스텝만으로도 고품질 이미지를 생성할 수 있습니다.
 스텝 수를 2, 3 또는 4로 늘리면 이미지 품질이 향상됩니다.
 
 ```py
@@ -74,7 +74,7 @@ image
 
 ## Image-to-image
 
-Image-to-image 생성의 경우 `num_inference_steps * strength`가 1보다 크거나 같은지 확인하세요. 
+Image-to-image 생성의 경우 `num_inference_steps * strength`가 1보다 크거나 같은지 확인하세요.
 Image-to-image 파이프라인은 아래 예제에서 `0.5 * 2.0 = 1` 스텝과 같이 `int(num_inference_steps * strength)` 스텝으로 실행됩니다.
 
 ```py

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

@@ -21,7 +21,7 @@ specific language governing permissions and limitations under the License.
 시작하기 전에 다음 라이브러리가 설치되어 있는지 확인하세요:
 
 ```py
-!pip install -q -U diffusers transformers accelerate 
+!pip install -q -U diffusers transformers accelerate
 ```
 
 이 모델에는 [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid)와 [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) 두 가지 종류가 있습니다. SVD 체크포인트는 14개의 프레임을 생성하도록 학습되었고, SVD-XT 체크포인트는 25개의 프레임을 생성하도록 파인튜닝되었습니다.

examples/community/fresco_v2v.py

@@ -2436,7 +2436,7 @@ class FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py

@@ -1002,7 +1002,7 @@ class StableDiffusionXLInstantIDImg2ImgPipeline(StableDiffusionXLControlNetImg2I
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

examples/community/pipeline_stable_diffusion_xl_instantid.py

@@ -991,7 +991,7 @@ class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

examples/community/rerender_a_video.py

@@ -864,7 +864,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
                 )
 
                 if guess_mode and do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
@@ -1038,7 +1038,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
                         )
 
                         if guess_mode and do_classifier_free_guidance:
-                            # Infered ControlNet only for the conditional batch.
+                            # Inferred ControlNet only for the conditional batch.
                             # To apply the output of ControlNet to both the unconditional and conditional batches,
                             # add 0 to the unconditional batch to keep it unchanged.
                             down_block_res_samples = [

examples/community/stable_diffusion_controlnet_reference.py

@@ -752,7 +752,7 @@ class StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeli
                 )
 
                 if guess_mode and do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

examples/dreambooth/README_sd3.md

@@ -148,12 +148,12 @@ accelerate launch train_dreambooth_lora_sd3.py \
 ```
 
 ### Text Encoder Training
-Alongside the transformer, LoRA fine-tuning of the CLIP text encoders is now also supported. 
+Alongside the transformer, LoRA fine-tuning of the CLIP text encoders is now also supported.
 To do so, just specify `--train_text_encoder` while launching training. Please keep the following points in mind:
 
 > [!NOTE]
-> SD3 has three text encoders (CLIP L/14, OpenCLIP bigG/14, and T5-v1.1-XXL). 
-By enabling `--train_text_encoder`, LoRA fine-tuning of both **CLIP encoders** is performed. At the moment, T5 fine-tuning is not supported and weights remain frozen when text encoder training is enabled. 
+> SD3 has three text encoders (CLIP L/14, OpenCLIP bigG/14, and T5-v1.1-XXL).
+By enabling `--train_text_encoder`, LoRA fine-tuning of both **CLIP encoders** is performed. At the moment, T5 fine-tuning is not supported and weights remain frozen when text encoder training is enabled.
 
 To perform DreamBooth LoRA with text-encoder training, run:
 ```bash
@@ -185,4 +185,4 @@ accelerate launch train_dreambooth_lora_sd3.py \
 
 1. We default to the "logit_normal" weighting scheme for the loss following the SD3 paper. Thanks to @bghira for helping us discover that for other weighting schemes supported from the training script, training may incur numerical instabilities.
 2. Thanks to `bghira`, `JinxuXiang`, and `bendanzzc` for helping us discover a bug in how VAE encoding was being done previously. This has been fixed in [#8917](https://github.com/huggingface/diffusers/pull/8917).
-3. Additionally, we now have the option to control if we want to apply preconditioning to the model outputs via a `--precondition_outputs` CLI arg. It affects how the model `target` is calculated as well. 
+3. Additionally, we now have the option to control if we want to apply preconditioning to the model outputs via a `--precondition_outputs` CLI arg. It affects how the model `target` is calculated as well.
\ No newline at end of file