change syntax

Author: stevhliu

docs/source/en/api/configuration.md

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
 
-<Tip>
-
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
-
-</Tip>
+> [!TIP]
+> To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
 
 ## ConfigMixin
 

docs/source/en/api/loaders/ip_adapter.md

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 [IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
 
-<Tip>
-
-Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
-
-</Tip>
+> [!TIP]
+> Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
 
 ## IPAdapterMixin
 

docs/source/en/api/loaders/lora.md

@@ -33,11 +33,8 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
 ## LoraBaseMixin
 

docs/source/en/api/loaders/peft.md

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
 
-<Tip>
-
-Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
-
-</Tip>
+> [!TIP]
+> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
 
 ## PeftAdapterMixin
 

docs/source/en/api/loaders/textual_inversion.md

@@ -16,11 +16,8 @@ Textual Inversion is a training method for personalizing models by learning new
 
 [`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
 
-<Tip>
-
-To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
-
-</Tip>
+> [!TIP]
+> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
 
 ## TextualInversionLoaderMixin
 

docs/source/en/api/loaders/transformer_sd3.md

@@ -16,11 +16,8 @@ This class is useful when *only* loading weights into a [`SD3Transformer2DModel`
 
 The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
 
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
 ## SD3Transformer2DLoadersMixin
 

docs/source/en/api/loaders/unet.md

@@ -16,11 +16,8 @@ Some training methods - like LoRA and Custom Diffusion - typically target the UN
 
 The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
 
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
 ## UNet2DConditionLoadersMixin
 

docs/source/en/api/models/consistency_decoder_vae.md

@@ -16,11 +16,8 @@ Consistency decoder can be used to decode the latents from the denoising UNet in
 
 The original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder).
 
-<Tip warning={true}>
-
-Inference is only supported for 2 iterations as of now.
-
-</Tip>
+> [!WARNING]
+> Inference is only supported for 2 iterations as of now.
 
 The pipeline could not have been contributed without the help of [madebyollin](https://github.com/madebyollin) and [mrsteyk](https://github.com/mrsteyk) from [this issue](https://github.com/openai/consistencydecoder/issues/1).
 

docs/source/en/api/models/transformer2d.md

@@ -22,11 +22,8 @@ When the input is **continuous**:
 
 When the input is **discrete**:
 
-<Tip>
-
-It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
-
-</Tip>
+> [!TIP]
+> It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
 
 1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
 2. Apply the Transformer blocks in the standard way.

docs/source/en/api/outputs.md

@@ -39,11 +39,8 @@ For instance, retrieving an image by indexing into it returns the tuple `(output
 outputs[:1]
 ```
 
-<Tip>
-
-To check a specific pipeline or model output, refer to its corresponding API documentation.
-
-</Tip>
+> [!TIP]
+> To check a specific pipeline or model output, refer to its corresponding API documentation.
 
 ## BaseOutput