[docs] Cache link

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

@@ -25,6 +25,8 @@ Original model checkpoints for Flux can be found [here](https://huggingface.co/b
 
 Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
 
+[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+
 </Tip>
 
 Flux comes in the following variants:

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

@@ -18,7 +18,7 @@
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 </Tip>
 

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

@@ -88,7 +88,7 @@ export_to_video(video, "output.mp4", fps=24)
 </hfoption>
 <hfoption id="inference speed">
 
-[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 ```py
 import torch

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

@@ -20,7 +20,7 @@ Check out the model card [here](https://huggingface.co/Qwen/Qwen-Image) to learn
 
 <Tip>
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+[Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 </Tip>
 

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

@@ -119,7 +119,7 @@ export_to_video(output, "output.mp4", fps=16)
 </hfoption>
 <hfoption id="T2V inference speed">
 
-[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 ```py
 # pip install ftfy