Merge branch 'main' into allow-non-list-component

Author: sayakpaul

docs/source/en/_toctree.yml

@@ -77,7 +77,7 @@
   - local: optimization/memory
     title: Reduce memory usage
   - local: optimization/speed-memory-optims
-    title: Compile and offloading quantized models
+    title: Compiling and offloading quantized models
   - title: Community optimizations
     sections:
     - local: optimization/pruna

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

@@ -20,7 +20,7 @@
   </div>
 </div>
 
-# Wan2.1
+# Wan
 
 [Wan-2.1](https://huggingface.co/papers/2503.20314) by the Wan Team.
 
@@ -42,7 +42,7 @@ The following Wan models are supported in Diffusers:
 - [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
 
 > [!TIP]
-> Click on the Wan2.1 models in the right sidebar for more examples of video generation.
+> Click on the Wan models in the right sidebar for more examples of video generation.
 
 ### Text-to-Video Generation
 

docs/source/en/optimization/speed-memory-optims.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.
 -->
 
-# Compile and offloading quantized models
+# Compiling and offloading quantized models
 
 Optimizing models often involves trade-offs between [inference speed](./fp16) and [memory-usage](./memory). For instance, while [caching](./cache) can boost inference speed, it also increases memory consumption since it needs to store the outputs of intermediate attention layers. A more balanced optimization strategy combines quantizing a model, [torch.compile](./fp16#torchcompile) and various [offloading methods](./memory#offloading).
 
@@ -28,7 +28,8 @@ The table below provides a comparison of optimization strategy combinations and
 | quantization  | 32.602 | 14.9453 |
 | quantization, torch.compile  | 25.847 | 14.9448 |
 | quantization, torch.compile, model CPU offloading | 32.312 | 12.2369 |
-<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the [benchmarking script](https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d) if you're interested in evaluating your own model.</small>
+
+<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the <a href="https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d">benchmarking script</a> if you're interested in evaluating your own model.</small>
 
 This guide will show you how to compile and offload a quantized model with [bitsandbytes](../quantization/bitsandbytes#torchcompile). Make sure you are using [PyTorch nightly](https://pytorch.org/get-started/locally/) and the latest version of bitsandbytes.