Chore: Typo fixed in multiple files (#5422)

docs/source/en/training/create_dataset.md

@@ -87,4 +87,4 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
 
 Now that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.
 
-For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](uncondtional_training) or [text-to-image generation](text2image)!
+For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)!
\ No newline at end of file

docs/source/en/training/lora.md

@@ -527,8 +527,8 @@ base_model_id = "stabilityai/stable-diffusion-xl-base-0.9"
 pipeline = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to("cuda")
 pipeline.load_lora_weights(".", weight_name="Kamepan.safetensors")
 
-prompt = "anime screencap, glint, drawing, best quality, light smile, shy, a full body of a girl wearing wedding dress in the middle of the forest beneath the trees, fireflies, big eyes, 2d, cute, anime girl, waifu, cel shading, magical girl, vivid colors, (outline:1.1), manga anime artstyle, masterpiece, offical wallpaper, glint <lora:kame_sdxl_v2:1>"
-negative_prompt = "(deformed, bad quality, sketch, depth of field, blurry:1.1), grainy, bad anatomy, bad perspective, old, ugly, realistic, cartoon, disney, bad propotions"
+prompt = "anime screencap, glint, drawing, best quality, light smile, shy, a full body of a girl wearing wedding dress in the middle of the forest beneath the trees, fireflies, big eyes, 2d, cute, anime girl, waifu, cel shading, magical girl, vivid colors, (outline:1.1), manga anime artstyle, masterpiece, official wallpaper, glint <lora:kame_sdxl_v2:1>"
+negative_prompt = "(deformed, bad quality, sketch, depth of field, blurry:1.1), grainy, bad anatomy, bad perspective, old, ugly, realistic, cartoon, disney, bad proportions"
 generator = torch.manual_seed(2947883060)
 num_inference_steps = 30
 guidance_scale = 7

docs/source/en/using-diffusers/img2img.md

@@ -68,7 +68,7 @@ The most popular image-to-image models are [Stable Diffusion v1.5](https://huggi
 
 ### Stable Diffusion v1.5
 
-Stable Diffusion v1.5 is a latent diffusion model intialized from an earlier checkpoint, and further finetuned for 595K steps on 512x512 images. To use this pipeline for image-to-image, you'll need to prepare an initial image to pass to the pipeline. Then you can pass a prompt and the image to the pipeline to generate a new image:
+Stable Diffusion v1.5 is a latent diffusion model initialized from an earlier checkpoint, and further finetuned for 595K steps on 512x512 images. To use this pipeline for image-to-image, you'll need to prepare an initial image to pass to the pipeline. Then you can pass a prompt and the image to the pipeline to generate a new image:
 
 ```py
 import torch

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

@@ -574,7 +574,7 @@ image
 
 ## Optimize
 
-It can be difficult and slow to run diffusion models if you're resource constrained, but it dosen't have to be with a few optimization tricks. One of the biggest (and easiest) optimizations you can enable is switching to memory-efficient attention. If you're using PyTorch 2.0, [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention) is automatically enabled and you don't need to do anything else. For non-PyTorch 2.0 users, you can install and use [xFormers](../optimization/xformers)'s implementation of memory-efficient attention. Both options reduce memory usage and accelerate inference.
+It can be difficult and slow to run diffusion models if you're resource constrained, but it doesn't have to be with a few optimization tricks. One of the biggest (and easiest) optimizations you can enable is switching to memory-efficient attention. If you're using PyTorch 2.0, [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention) is automatically enabled and you don't need to do anything else. For non-PyTorch 2.0 users, you can install and use [xFormers](../optimization/xformers)'s implementation of memory-efficient attention. Both options reduce memory usage and accelerate inference.
 
 You can also offload the model to the GPU to save even more memory:
 

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

@@ -153,7 +153,7 @@ exactly the same hardware and PyTorch version for full reproducibility.
 
 You can also configure PyTorch to use deterministic algorithms to create a reproducible pipeline. However, you should be aware that deterministic algorithms may be slower than nondeterministic ones and you may observe a decrease in performance. But if reproducibility is important to you, then this is the way to go!
 
-Nondeterministic behavior occurs when operations are launched in more than one CUDA stream. To avoid this, set the environment varibale [`CUBLAS_WORKSPACE_CONFIG`](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during runtime.
+Nondeterministic behavior occurs when operations are launched in more than one CUDA stream. To avoid this, set the environment variable [`CUBLAS_WORKSPACE_CONFIG`](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during runtime.
 
 PyTorch typically benchmarks multiple algorithms to select the fastest one, but if you want reproducibility, you should disable this feature because the benchmark may select different algorithms each time. Lastly, pass `True` to [`torch.use_deterministic_algorithms`](https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html) to enable deterministic algorithms.
 

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

@@ -169,7 +169,7 @@ Feel free to choose any prompt you like if you want to generate something else!
 >>> width = 512  # default width of Stable Diffusion
 >>> num_inference_steps = 25  # Number of denoising steps
 >>> guidance_scale = 7.5  # Scale for classifier-free guidance
->>> generator = torch.manual_seed(0)  # Seed generator to create the inital latent noise
+>>> generator = torch.manual_seed(0)  # Seed generator to create the initial latent noise
 >>> batch_size = len(prompt)
 ```
 

examples/community/README.md

@@ -41,7 +41,7 @@ If a community doesn't work as expected, please open an issue and ping the autho
 |   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon) 
 |   Zero1to3 Pipeline                                                                                                    | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Zero1to3 Pipeline](#Zero1to3-pipeline)      | - |              [Xin Kong](https://github.com/kxhit) |
 Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | - | [Andrew Zhu](https://xhinker.medium.com/) | 
-FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipline](#stable-diffusion-fabric-pipeline) | - | [Shauray Singh](https://shauray8.github.io/about_shauray/) | 
+FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | - | [Shauray Singh](https://shauray8.github.io/about_shauray/) | 
 sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) | 
 prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) | 
 
@@ -765,7 +765,7 @@ pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom
 #There are multiple possible scenarios:
 #The pipeline with the merged checkpoints is returned in all the scenarios
 
-#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparision.( attrs with _ as prefix )
+#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparison.( attrs with _ as prefix )
 merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","CompVis/stable-diffusion-v1-2"], interp = "sigmoid", alpha = 0.4)
 
 #Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility
@@ -1529,14 +1529,14 @@ print("Latency of StableDiffusionPipeline--fp32",latency)
 
 ![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)
 
-CLIP guided stable diffusion images mixing pipline allows to combine two images using standard diffusion models.  
+CLIP guided stable diffusion images mixing pipeline allows to combine two images using standard diffusion models.  
 This approach is using (optional) CoCa model to avoid writing image description.  
 [More code examples](https://github.com/TheDenk/images_mixing)
 
 
 ### Stable Diffusion XL Long Weighted Prompt Pipeline
 
-This SDXL pipeline support unlimted length prompt and negative prompt, compatible with A1111 prompt weighted style. 
+This SDXL pipeline support unlimited length prompt and negative prompt, compatible with A1111 prompt weighted style. 
 
 You can provide both `prompt` and `prompt_2`. if only one prompt is provided, `prompt_2` will be a copy of the provided `prompt`. Here is a sample code to use this pipeline. 
 
@@ -1605,7 +1605,7 @@ coca_transform = open_clip.image_transform(
 )
 coca_tokenizer = SimpleTokenizer()
 
-# Pipline creating
+# Pipeline creating
 mixing_pipeline = DiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     custom_pipeline="clip_guided_images_mixing_stable_diffusion",
@@ -1619,7 +1619,7 @@ mixing_pipeline = DiffusionPipeline.from_pretrained(
 mixing_pipeline.enable_attention_slicing()
 mixing_pipeline = mixing_pipeline.to("cuda")
 
-# Pipline running
+# Pipeline running
 generator = torch.Generator(device="cuda").manual_seed(17) 
 
 def download_image(url):