init

Author: stevhliu

docs/source/en/_toctree.yml

@@ -9,11 +9,11 @@
   - local: stable_diffusion
     title: Basic performance
 
-- title: DiffusionPipeline
+- title: Pipelines
   isExpanded: false
   sections:
   - local: using-diffusers/loading
-    title: Load pipelines
+    title: DiffusionPipeline
   - local: tutorials/autopipeline
     title: AutoPipeline
   - local: using-diffusers/custom_pipeline_overview

docs/source/en/using-diffusers/loading.md

@@ -10,25 +10,17 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Load pipelines
-
 [[open-in-colab]]
 
-Diffusion systems consist of multiple components like parameterized models and schedulers that interact in complex ways. That is why we designed the [`DiffusionPipeline`] to wrap the complexity of the entire diffusion system into an easy-to-use API. At the same time, the [`DiffusionPipeline`] is entirely customizable so you can modify each component to build a diffusion system for your use case.
-
-This guide will show you how to load:
+# DiffusionPipeline
 
-- pipelines from the Hub and locally
-- different components into a pipeline
-- multiple pipelines without increasing memory usage
-- checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights
+Diffusion models consists of multiple components like UNets or diffusion transformers (DiTs), text encoders, variational autoencoders (VAEs), and schedulers. The [`DiffusionPipeline`] wraps all of these components into a single easy-to-use API without giving up the flexibility to modify it's components.
 
-## Load a pipeline
+This guide will show you how to load a [`DiffusionPipeline`].
 
-> [!TIP]
-> Skip to the [DiffusionPipeline explained](#diffusionpipeline-explained) section if you're interested in an explanation about how the [`DiffusionPipeline`] class works.
+## Loading a pipeline
 
-There are two ways to load a pipeline for a task:
+There are two ways to load a pipeline.
 
 1. Load the generic [`DiffusionPipeline`] class and allow it to automatically detect the correct pipeline class from the checkpoint.
 2. Load a specific pipeline class for a specific task.
@@ -80,21 +72,6 @@ pipeline = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5
 
 Use the Space below to gauge a pipeline's memory requirements before you download and load it to see if it runs on your hardware.
 
-<div class="block dark:hidden">
-	<iframe
-        src="https://diffusers-compute-pipeline-size.hf.space?__theme=light"
-        width="850"
-        height="1600"
-    ></iframe>
-</div>
-<div class="hidden dark:block">
-    <iframe
-        src="https://diffusers-compute-pipeline-size.hf.space?__theme=dark"
-        width="850"
-        height="1600"
-    ></iframe>
-</div>
-
 ### Specifying Component-Specific Data Types
 
 You can customize the data types for individual sub-models by passing a dictionary to the `torch_dtype` parameter. This allows you to load different components of a pipeline in different floating point precisions. For instance, if you want to load the transformer with `torch.bfloat16` and all other components with `torch.float16`, you can pass a dictionary mapping:

src/diffusers/__init__.py

@@ -36,6 +36,15 @@
     "configuration_utils": ["ConfigMixin"],
     "guiders": [],
     "hooks": [],
+    "image_processor": [
+        "VaeImageProcessor",
+        "VaeImageProcessorLDM3D", 
+        "PixArtImageProcessor",
+        "IPAdapterMaskProcessor",
+    ],
+    "video_processor": [
+        "VideoProcessor",
+    ],
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "modular_pipelines": [],
@@ -940,6 +949,13 @@
             ScoreSdeVePipeline,
             StableDiffusionMixin,
         )
+        from .image_processor import (
+            VaeImageProcessor,
+            VaeImageProcessorLDM3D,
+            PixArtImageProcessor,
+            IPAdapterMaskProcessor,
+        )
+        from .video_processor import VideoProcessor
         from .quantizers import DiffusersQuantizer
         from .schedulers import (
             AmusedScheduler,
@@ -1336,4 +1352,4 @@
         _import_structure,
         module_spec=__spec__,
         extra_objects={"__version__": __version__},
-    )
+    )