@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
...
@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
# Schedulers
# Schedulers
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
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 are the [Schedulers](../api/schedulers/overview.mdx).
a pipeline to one's use case. The best example of this is the [Schedulers](../api/schedulers/overview.mdx).
Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample,
Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample,
schedulers define the whole denoising process, *i.e.*:
schedulers define the whole denoising process, *i.e.*:
They can be quite complex and often define a trade-off between **denoising speed** and **denoising quality**.
They can be quite complex and often define a trade-off between **denoising speed** and **denoising quality**.
It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion pipeline, so it is often recommended to simply try out which works best.
It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion pipeline, so it is often recommended to simply try out which works best.
The following paragraphs shows how to do so with the 🧨 Diffusers library.
The following paragraphs show how to do so with the 🧨 Diffusers library.
