AutoPipeline

docs/source/en/_toctree.yml

@@ -186,6 +186,8 @@
       title: Audio Diffusion
     - local: api/pipelines/audioldm
       title: AudioLDM
+    - local: api/pipelines/auto_pipeline
+      title: AutoPipeline
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet

docs/source/en/api/pipelines/auto_pipeline.mdx

@@ -0,0 +1,68 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# AutoPipeline
+
+In many cases, one checkpoint can be used for multiple tasks. For example, you may be able to use the same checkpoint for Text-to-Image, Image-to-Image, and Inpainting. However, you'll need to know the pipeline class names linked to your checkpoint. 
+
+AutoPipeline is designed to make it easy for you to use multiple pipelines in your workflow. We currently provide 3 AutoPipeline classes to perform three different tasks, i.e. [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]. You'll need to choose the AutoPipeline class based on the task you want to perform and use it to automatically retrieve the relevant pipeline given the name/path to the pre-trained weights. 
+
+For example, to perform Image-to-Image with the SD1.5 checkpoint, you can do
+
+```python
+from diffusers import PipelineForImageToImage
+
+pipe_i2i = PipelineForImageoImage.from_pretrained("runwayml/stable-diffusion-v1-5")
+```
+
+It will also help you switch between tasks seamlessly using the same checkpoint without reallocating additional memory. For example, to re-use the Image-to-Image pipeline we just created for inpainting, you can do 
+
+```python
+from diffusers import PipelineForInpainting
+
+pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_i2i)
+```
+All the components will be transferred to the inpainting pipeline with zero cost.
+
+
+Currently AutoPipeline support the Text-to-Image, Image-to-Image, and Inpainting tasks for below diffusion models:
+- [stable Diffusion](./stable_diffusion)
+- [Stable Diffusion Controlnet](./api/pipelines/controlnet)
+- [Stable Diffusion XL](./stable_diffusion/stable_diffusion_xl)
+- [IF](./if) 
+- [Kandinsky](./kandinsky)(./kandinsky)(./kandinsky)(./kandinsky)(./kandinsky)
+- [Kandinsky 2.2]()(./kandinsky)
+
+
+## AutoPipelineForText2Image
+
+[[autodoc]] AutoPipelineForText2Image
+	- all
+	- from_pretrained
+	- from_pipe
+
+
+## AutoPipelineForImage2Image
+
+[[autodoc]] AutoPipelineForImage2Image
+	- all
+	- from_pretrained
+	- from_pipe
+
+## AutoPipelineForInpainting
+
+[[autodoc]] AutoPipelineForInpainting
+	- all
+	- from_pretrained
+	- from_pipe
+
+

src/diffusers/__init__.py

@@ -61,6 +61,9 @@
     )
     from .pipelines import (
         AudioPipelineOutput,
+        AutoPipelineForImage2Image,
+        AutoPipelineForInpainting,
+        AutoPipelineForText2Image,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,

src/diffusers/pipelines/__init__.py

@@ -17,6 +17,7 @@
 except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
 else:
+    from .auto_pipeline import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image
     from .consistency_models import ConsistencyModelPipeline
     from .dance_diffusion import DanceDiffusionPipeline
     from .ddim import DDIMPipeline