Where did this 'x' come from, Elon? (huggingface#4277)

* why mdx? * why mdx? * why mdx? * no x for kandinksy either --------- Co-authored-by: Patrick von Platen <[email protected]>
heyalexchoi · Jul 26, 2023 · c6ae9b7 · c6ae9b7
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diff --git a/PHILOSOPHY.md b/PHILOSOPHY.md
@@ -102,7 +102,7 @@ The following design principles are followed:
 - One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper).
 - If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
 - Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.mdx).
+- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
 - Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
 - Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon
 - The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).

diff --git a/docs/README.md b/docs/README.md
@@ -68,7 +68,7 @@ The `preview` command only works with existing doc files. When you add a complet
 
 ## Adding a new element to the navigation bar
 
-Accepted files are Markdown (.md or .mdx).
+Accepted files are Markdown (.md).
 
 Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
 the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/_toctree.yml) file.
@@ -96,7 +96,7 @@ Sections that were moved:
 
 Use the relative style to link to the new file so that the versioned docs continue to work.
 
-For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.mdx).
+For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
 
 
 ## Writing Documentation - Specification
@@ -119,8 +119,8 @@ depending on the intended targets (beginners, more advanced users, or researcher
 
 When adding a new pipeline:
 
-- create a file `xxx.mdx` under `docs/source/api/pipelines` (don't hesitate to copy an existing file as template).
-- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.mdx`, along with the link to the paper, and a colab notebook (if available).
+- create a file `xxx.md` under `docs/source/api/pipelines` (don't hesitate to copy an existing file as template).
+- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.md`, along with the link to the paper, and a colab notebook (if available).
 - Write a short overview of the diffusion model:
     - Overview with paper & authors
     - Paper abstract

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diff --git a/...source/en/api/schedulers/score_sde_ve.mdx → .../source/en/api/schedulers/score_sde_ve.md b/...source/en/api/schedulers/score_sde_ve.mdx → .../source/en/api/schedulers/score_sde_ve.md
diff --git a/...source/en/api/schedulers/score_sde_vp.mdx → .../source/en/api/schedulers/score_sde_vp.md b/...source/en/api/schedulers/score_sde_vp.mdx → .../source/en/api/schedulers/score_sde_vp.md
diff --git a/.../api/schedulers/singlestep_dpm_solver.mdx → ...n/api/schedulers/singlestep_dpm_solver.md b/.../api/schedulers/singlestep_dpm_solver.mdx → ...n/api/schedulers/singlestep_dpm_solver.md
diff --git a/...n/api/schedulers/stochastic_karras_ve.mdx → ...en/api/schedulers/stochastic_karras_ve.md b/...n/api/schedulers/stochastic_karras_ve.mdx → ...en/api/schedulers/stochastic_karras_ve.md
diff --git a/docs/source/en/api/schedulers/unipc.mdx → docs/source/en/api/schedulers/unipc.md b/docs/source/en/api/schedulers/unipc.mdx → docs/source/en/api/schedulers/unipc.md
diff --git a/...source/en/api/schedulers/vq_diffusion.mdx → .../source/en/api/schedulers/vq_diffusion.md b/...source/en/api/schedulers/vq_diffusion.mdx → .../source/en/api/schedulers/vq_diffusion.md
diff --git a/docs/source/en/api/utilities.mdx → docs/source/en/api/utilities.md b/docs/source/en/api/utilities.mdx → docs/source/en/api/utilities.md
diff --git a/docs/source/en/conceptual/contribution.mdx → docs/source/en/conceptual/contribution.md b/docs/source/en/conceptual/contribution.mdx → docs/source/en/conceptual/contribution.md
diff --git a/...urce/en/conceptual/ethical_guidelines.mdx → ...ource/en/conceptual/ethical_guidelines.md b/...urce/en/conceptual/ethical_guidelines.mdx → ...ource/en/conceptual/ethical_guidelines.md
@@ -1,51 +1,51 @@
-# 🧨 Diffusers’ Ethical Guidelines
-
-## Preamble
-
-[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. 
-
-Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.
-
-The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.
-We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.
-
-
-## Scope
-
-The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.
-
-
-## Ethical guidelines
-
-The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.
-
-- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.
-
-- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.
-
-- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.
-
-- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.
-
-- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.
-
-- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.
-
-
-## Examples of implementations: Safety features and Mechanisms
-
-The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. 
-
-- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.
-
-- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.
-
-- **Encouraging safety in deployment**
-
-  - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
-
-  - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.
-
-- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.
-
-- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. 
+# 🧨 Diffusers’ Ethical Guidelines
+
+## Preamble
+
+[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. 
+
+Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.
+
+The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.
+We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.
+
+
+## Scope
+
+The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.
+
+
+## Ethical guidelines
+
+The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.
+
+- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.
+
+- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.
+
+- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.
+
+- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.
+
+- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.
+
+- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.
+
+
+## Examples of implementations: Safety features and Mechanisms
+
+The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. 
+
+- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.
+
+- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.
+
+- **Encouraging safety in deployment**
+
+  - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
+
+  - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.
+
+- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.
+
+- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. 
diff --git a/docs/source/en/conceptual/evaluation.mdx → docs/source/en/conceptual/evaluation.md b/docs/source/en/conceptual/evaluation.mdx → docs/source/en/conceptual/evaluation.md
diff --git a/docs/source/en/conceptual/philosophy.mdx → docs/source/en/conceptual/philosophy.md b/docs/source/en/conceptual/philosophy.mdx → docs/source/en/conceptual/philosophy.md
@@ -102,7 +102,7 @@ The following design principles are followed:
 - One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper). 
 - If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
 - Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.mdx).
+- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
 - Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
 - Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
 - The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).

diff --git a/docs/source/en/index.mdx → docs/source/en/index.md b/docs/source/en/index.mdx → docs/source/en/index.md
diff --git a/docs/source/en/installation.mdx → docs/source/en/installation.md b/docs/source/en/installation.mdx → docs/source/en/installation.md
diff --git a/docs/source/en/optimization/coreml.mdx → docs/source/en/optimization/coreml.md b/docs/source/en/optimization/coreml.mdx → docs/source/en/optimization/coreml.md
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diff --git a/docs/source/en/optimization/habana.mdx → docs/source/en/optimization/habana.md b/docs/source/en/optimization/habana.mdx → docs/source/en/optimization/habana.md
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diff --git a/docs/source/en/optimization/onnx.mdx → docs/source/en/optimization/onnx.md b/docs/source/en/optimization/onnx.mdx → docs/source/en/optimization/onnx.md
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diff --git a/docs/source/en/optimization/opt_overview.mdx → docs/source/en/optimization/opt_overview.md b/docs/source/en/optimization/opt_overview.mdx → docs/source/en/optimization/opt_overview.md
diff --git a/docs/source/en/optimization/tome.mdx → docs/source/en/optimization/tome.md b/docs/source/en/optimization/tome.mdx → docs/source/en/optimization/tome.md
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diff --git a/docs/source/en/tutorials/basic_training.mdx → docs/source/en/tutorials/basic_training.md b/docs/source/en/tutorials/basic_training.mdx → docs/source/en/tutorials/basic_training.md
diff --git a/...source/en/tutorials/tutorial_overview.mdx → .../source/en/tutorials/tutorial_overview.md b/...source/en/tutorials/tutorial_overview.mdx → .../source/en/tutorials/tutorial_overview.md
diff --git a/...iffusers/conditional_image_generation.mdx → ...diffusers/conditional_image_generation.md b/...iffusers/conditional_image_generation.mdx → ...diffusers/conditional_image_generation.md
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diff --git a/...en/using-diffusers/control_brightness.mdx → .../en/using-diffusers/control_brightness.md b/...en/using-diffusers/control_brightness.mdx → .../en/using-diffusers/control_brightness.md
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diff --git a/...ng-diffusers/custom_pipeline_overview.mdx → ...ing-diffusers/custom_pipeline_overview.md b/...ng-diffusers/custom_pipeline_overview.mdx → ...ing-diffusers/custom_pipeline_overview.md
diff --git a/docs/source/en/using-diffusers/depth2img.mdx → docs/source/en/using-diffusers/depth2img.md b/docs/source/en/using-diffusers/depth2img.mdx → docs/source/en/using-diffusers/depth2img.md
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diff --git a/docs/source/en/using-diffusers/loading.mdx → docs/source/en/using-diffusers/loading.md b/docs/source/en/using-diffusers/loading.mdx → docs/source/en/using-diffusers/loading.md
diff --git a/...e/en/using-diffusers/loading_overview.mdx → ...ce/en/using-diffusers/loading_overview.md b/...e/en/using-diffusers/loading_overview.mdx → ...ce/en/using-diffusers/loading_overview.md
diff --git a/...urce/en/using-diffusers/other-formats.mdx → ...ource/en/using-diffusers/other-formats.md b/...urce/en/using-diffusers/other-formats.mdx → ...ource/en/using-diffusers/other-formats.md
diff --git a/...e/en/using-diffusers/other-modalities.mdx → ...ce/en/using-diffusers/other-modalities.md b/...e/en/using-diffusers/other-modalities.mdx → ...ce/en/using-diffusers/other-modalities.md
diff --git a/.../en/using-diffusers/pipeline_overview.mdx → ...e/en/using-diffusers/pipeline_overview.md b/.../en/using-diffusers/pipeline_overview.mdx → ...e/en/using-diffusers/pipeline_overview.md
diff --git a/...ce/en/using-diffusers/reproducibility.mdx → ...rce/en/using-diffusers/reproducibility.md b/...ce/en/using-diffusers/reproducibility.mdx → ...rce/en/using-diffusers/reproducibility.md
diff --git a/...urce/en/using-diffusers/reusing_seeds.mdx → ...ource/en/using-diffusers/reusing_seeds.md b/...urce/en/using-diffusers/reusing_seeds.mdx → ...ource/en/using-diffusers/reusing_seeds.md
diff --git a/.../source/en/using-diffusers/schedulers.mdx → docs/source/en/using-diffusers/schedulers.md b/.../source/en/using-diffusers/schedulers.mdx → docs/source/en/using-diffusers/schedulers.md
@@ -15,7 +15,7 @@ specific language governing permissions and limitations under the License.
 [[open-in-colab]]
 
 Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize 
-a pipeline to one's use case. The best example of this is the [Schedulers](../api/schedulers/overview.mdx).
+a pipeline to one's use case. The best example of this is the [Schedulers](../api/schedulers/overview.md).
 
 Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample, 
 schedulers define the whole denoising process, *i.e.*:

diff --git a/...diffusers/stable_diffusion_jax_how_to.mdx → ...-diffusers/stable_diffusion_jax_how_to.md b/...diffusers/stable_diffusion_jax_how_to.mdx → ...-diffusers/stable_diffusion_jax_how_to.md
diff --git a/...diffusers/textual_inversion_inference.mdx → ...-diffusers/textual_inversion_inference.md b/...diffusers/textual_inversion_inference.mdx → ...-diffusers/textual_inversion_inference.md
diff --git a/...fusers/unconditional_image_generation.mdx → ...ffusers/unconditional_image_generation.md b/...fusers/unconditional_image_generation.mdx → ...ffusers/unconditional_image_generation.md
diff --git a/.../en/using-diffusers/using_safetensors.mdx → ...e/en/using-diffusers/using_safetensors.md b/.../en/using-diffusers/using_safetensors.mdx → ...e/en/using-diffusers/using_safetensors.md
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diff --git a/docs/source/ko/quicktour.mdx → docs/source/ko/quicktour.md b/docs/source/ko/quicktour.mdx → docs/source/ko/quicktour.md
diff --git a/docs/source/ko/training/adapt_a_model.mdx → docs/source/ko/training/adapt_a_model.md b/docs/source/ko/training/adapt_a_model.mdx → docs/source/ko/training/adapt_a_model.md
diff --git a/docs/source/ko/training/dreambooth.mdx → docs/source/ko/training/dreambooth.md b/docs/source/ko/training/dreambooth.mdx → docs/source/ko/training/dreambooth.md
diff --git a/docs/source/ko/training/lora.mdx → docs/source/ko/training/lora.md b/docs/source/ko/training/lora.mdx → docs/source/ko/training/lora.md
diff --git a/docs/source/ko/training/overview.mdx → docs/source/ko/training/overview.md b/docs/source/ko/training/overview.mdx → docs/source/ko/training/overview.md
diff --git a/docs/source/ko/training/text2image.mdx → docs/source/ko/training/text2image.md b/docs/source/ko/training/text2image.mdx → docs/source/ko/training/text2image.md
diff --git a/docs/source/ko/training/text_inversion.mdx → docs/source/ko/training/text_inversion.md b/docs/source/ko/training/text_inversion.mdx → docs/source/ko/training/text_inversion.md
diff --git a/...ce/ko/training/unconditional_training.mdx → ...rce/ko/training/unconditional_training.md b/...ce/ko/training/unconditional_training.mdx → ...rce/ko/training/unconditional_training.md
diff --git a/docs/source/ko/tutorials/basic_training.mdx → docs/source/ko/tutorials/basic_training.md b/docs/source/ko/tutorials/basic_training.mdx → docs/source/ko/tutorials/basic_training.md
diff --git a/...source/ko/tutorials/tutorial_overview.mdx → .../source/ko/tutorials/tutorial_overview.md b/...source/ko/tutorials/tutorial_overview.mdx → .../source/ko/tutorials/tutorial_overview.md
diff --git a/...ng-diffusers/custom_pipeline_examples.mdx → ...ing-diffusers/custom_pipeline_examples.md b/...ng-diffusers/custom_pipeline_examples.mdx → ...ing-diffusers/custom_pipeline_examples.md
diff --git a/...ng-diffusers/custom_pipeline_overview.mdx → ...ing-diffusers/custom_pipeline_overview.md b/...ng-diffusers/custom_pipeline_overview.mdx → ...ing-diffusers/custom_pipeline_overview.md
diff --git a/docs/source/ko/using-diffusers/depth2img.mdx → docs/source/ko/using-diffusers/depth2img.md b/docs/source/ko/using-diffusers/depth2img.mdx → docs/source/ko/using-diffusers/depth2img.md
diff --git a/docs/source/ko/using-diffusers/img2img.mdx → docs/source/ko/using-diffusers/img2img.md b/docs/source/ko/using-diffusers/img2img.mdx → docs/source/ko/using-diffusers/img2img.md
diff --git a/docs/source/ko/using-diffusers/inpaint.mdx → docs/source/ko/using-diffusers/inpaint.md b/docs/source/ko/using-diffusers/inpaint.mdx → docs/source/ko/using-diffusers/inpaint.md
diff --git a/docs/source/ko/using-diffusers/loading.mdx → docs/source/ko/using-diffusers/loading.md b/docs/source/ko/using-diffusers/loading.mdx → docs/source/ko/using-diffusers/loading.md
diff --git a/...urce/ko/using-diffusers/other-formats.mdx → ...ource/ko/using-diffusers/other-formats.md b/...urce/ko/using-diffusers/other-formats.mdx → ...ource/ko/using-diffusers/other-formats.md
diff --git a/.../ko/using-diffusers/pipeline_overview.mdx → ...e/ko/using-diffusers/pipeline_overview.md b/.../ko/using-diffusers/pipeline_overview.mdx → ...e/ko/using-diffusers/pipeline_overview.md
diff --git a/...urce/ko/using-diffusers/reusing_seeds.mdx → ...ource/ko/using-diffusers/reusing_seeds.md b/...urce/ko/using-diffusers/reusing_seeds.mdx → ...ource/ko/using-diffusers/reusing_seeds.md
diff --git a/.../source/ko/using-diffusers/schedulers.mdx → docs/source/ko/using-diffusers/schedulers.md b/.../source/ko/using-diffusers/schedulers.mdx → docs/source/ko/using-diffusers/schedulers.md
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # 스케줄러
 
-diffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다.  이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.mdx)를 교체하는 것입니다.
+diffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다.  이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.md)를 교체하는 것입니다.
 
 
 

diff --git a/...fusers/unconditional_image_generation.mdx → ...ffusers/unconditional_image_generation.md b/...fusers/unconditional_image_generation.mdx → ...ffusers/unconditional_image_generation.md
diff --git a/.../ko/using-diffusers/using_safetensors.mdx → ...e/ko/using-diffusers/using_safetensors.md b/.../ko/using-diffusers/using_safetensors.mdx → ...e/ko/using-diffusers/using_safetensors.md
diff --git a/...ko/using-diffusers/write_own_pipeline.mdx → .../ko/using-diffusers/write_own_pipeline.md b/...ko/using-diffusers/write_own_pipeline.mdx → .../ko/using-diffusers/write_own_pipeline.md
diff --git a/docs/source/zh/index.mdx → docs/source/zh/index.md b/docs/source/zh/index.mdx → docs/source/zh/index.md
diff --git a/docs/source/zh/installation.mdx → docs/source/zh/installation.md b/docs/source/zh/installation.mdx → docs/source/zh/installation.md
diff --git a/docs/source/zh/quicktour.mdx → docs/source/zh/quicktour.md b/docs/source/zh/quicktour.mdx → docs/source/zh/quicktour.md
diff --git a/utils/check_repo.py b/utils/check_repo.py
@@ -536,7 +536,7 @@ def find_all_documented_objects():
             content = f.read()
         raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content)
         documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs]
-    for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"):
+    for doc_file in Path(PATH_TO_DOC).glob("**/*.md"):
         with open(doc_file, "r", encoding="utf-8", newline="\n") as f:
             content = f.read()
         raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content)
@@ -674,7 +674,7 @@ def check_all_objects_are_documented():
 def check_model_type_doc_match():
     """Check all doc pages have a corresponding model type."""
     model_doc_folder = Path(PATH_TO_DOC) / "model_doc"
-    model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")]
+    model_docs = [m.stem for m in model_doc_folder.glob("*.md")]
 
     model_types = list(diffusers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys())
     model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types]

diff --git a/utils/check_table.py b/utils/check_table.py
@@ -161,19 +161,19 @@ def get_model_table_from_auto_modules():
 def check_model_table(overwrite=False):
     """Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`."""
     current_table, start_index, end_index, lines = _find_text_in_file(
-        filename=os.path.join(PATH_TO_DOCS, "index.mdx"),
+        filename=os.path.join(PATH_TO_DOCS, "index.md"),
         start_prompt="<!--This table is updated automatically from the auto modules",
         end_prompt="<!-- End table-->",
     )
     new_table = get_model_table_from_auto_modules()
 
     if current_table != new_table:
         if overwrite:
-            with open(os.path.join(PATH_TO_DOCS, "index.mdx"), "w", encoding="utf-8", newline="\n") as f:
+            with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f:
                 f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
         else:
             raise ValueError(
-                "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."
+                "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."
             )
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.

		# 스케줄러

		diffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다. 이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.mdx)를 교체하는 것입니다.
		diffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다. 이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.md)를 교체하는 것입니다.



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