feedback

stevhliu · stevhliu · commit 613057b58ebe · 2023-10-17T15:20:27.000-07:00
diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md
@@ -60,8 +60,8 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [Semantic Guidance](semantic_stable_diffusion) | text2image |
 | [Shap-E](shap_e) | text-to-3D, image-to-3D |
 | [Spectrogram Diffusion](spectrogram_diffusion) |  |
-| [StableDiffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
-| [StableDiffusionModelEditing](model_editing) | model editing |
+| [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
+| [Stable Diffusion Model Editing](model_editing) | model editing |
 | [Stable Diffusion XL](stable_diffusion_xl) | text2image, image2image, inpainting |
 | [Stable unCLIP](stable_unclip) | text2image, image variation |
 | [KarrasVe](karras_ve) | unconditional image generation |
diff --git a/docs/source/en/index.md b/docs/source/en/index.md
@@ -22,7 +22,7 @@ specific language governing permissions and limitations under the License.
 
 The library has three main components:
 
-- State-of-the-art [diffusion pipelines](api/pipelines/overview) for inference with just a few lines of code.
+- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
 - Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
 - Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
 
diff --git a/docs/source/en/using-diffusers/custom_pipeline_examples.md b/docs/source/en/using-diffusers/custom_pipeline_examples.md
@@ -20,7 +20,7 @@ For more context about the design choices behind community pipelines, please hav
 
 </Tip>
 
-Community pipelines allow you to get creative and build your own unique pipelines to share with the community. All community pipelines are stored in the [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) folder along with inference and training examples for how to use them. This guide will showcase some of the community pipelines and hopefully it'll inspire you to create your own (feel free to open a PR with your own pipeline and we will merge it!).
+Community pipelines allow you to get creative and build your own unique pipelines to share with the community. You can find all community pipelines in the [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) folder along with inference and training examples for how to use them. This guide showcases some of the community pipelines and hopefully it'll inspire you to create your own (feel free to open a PR with your own pipeline and we will merge it!).
 
 To load a community pipeline, use the `custom_pipeline` argument in [`DiffusionPipeline`] to specify one of the files in [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community):
 
diff --git a/docs/source/en/using-diffusers/textual_inversion_inference.md b/docs/source/en/using-diffusers/textual_inversion_inference.md
@@ -4,7 +4,7 @@
 
 The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).
 
-This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](./training/text_inversion) training guide.
+This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](../training/text_inversion) training guide.
 
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