[Stable Diffusion] Add components function (huggingface#889)

* [Stable Diffusion] Add components function

* uP

docs/source/api/diffusion_pipeline.mdx

@@ -32,6 +32,9 @@ Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrain
 [[autodoc]] DiffusionPipeline
 	- from_pretrained
 	- save_pretrained
+	- to
+	- device
+	- components
 
 ## ImagePipelineOutput
 By default diffusion pipelines return an object of class

docs/source/api/pipelines/stable_diffusion.mdx

@@ -17,6 +17,26 @@ For more details about how Stable Diffusion works and how it differs from the ba
 | [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
 | [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
 
+## Tips
+
+If you want to use all possible use cases in a single `DiffusionPipeline` you can either:
+- Make use of the [Stable Diffusion Mega Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#stable-diffusion-mega) or 
+- Make use of the `components` functionality to instantiate all components in the most memory-efficient way:
+
+```python
+>>> from diffusers import (
+...     StableDiffusionPipeline,
+...     StableDiffusionImg2ImgPipeline,
+...     StableDiffusionInpaintPipeline,
+... )
+
+>>> img2text = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+>>> img2img = StableDiffusionImg2ImgPipeline(**img2text.components)
+>>> inpaint = StableDiffusionInpaintPipeline(**img2text.components)
+
+>>> # now you can use img2text(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
+```
+
 ## StableDiffusionPipelineOutput
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
 

src/diffusers/pipeline_utils.py

@@ -18,7 +18,7 @@
 import inspect
 import os
 from dataclasses import dataclass
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 import torch
@@ -564,6 +564,41 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         model = pipeline_class(**init_kwargs)
         return model
 
+    @property
+    def components(self) -> Dict[str, Any]:
+        r"""
+
+        The `self.compenents` property can be useful to run different pipelines with the same weights and
+        configurations to not have to re-allocate memory.
+
+        Examples:
+
+        ```py
+        >>> from diffusers import (
+        ...     StableDiffusionPipeline,
+        ...     StableDiffusionImg2ImgPipeline,
+        ...     StableDiffusionInpaintPipeline,
+        ... )
+
+        >>> img2text = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> img2img = StableDiffusionImg2ImgPipeline(**img2text.components)
+        >>> inpaint = StableDiffusionInpaintPipeline(**img2text.components)
+        ```
+
+        Returns:
+            A dictionaly containing all the modules needed to initialize the pipleline.
+        """
+        components = {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
+        expected_modules = set(inspect.signature(self.__init__).parameters.keys()) - set(["self"])
+
+        if set(components.keys()) != expected_modules:
+            raise ValueError(
+                f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
+                f" {expected_modules} to be defined, but {components} are defined."
+            )
+
+        return components
+
     @staticmethod
     def numpy_to_pil(images):
         """

tests/test_pipelines.py

@@ -1391,6 +1391,59 @@ def test_stable_diffusion_inpaint_fp16(self):
 
         assert image.shape == (1, 128, 128, 3)
 
+    def test_components(self):
+        """Test that components property works correctly"""
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = Image.fromarray(np.uint8(image)).convert("RGB")
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # make sure here that pndm scheduler skips prk
+        inpaint = StableDiffusionInpaintPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=self.dummy_safety_checker,
+            feature_extractor=self.dummy_extractor,
+        )
+        img2img = StableDiffusionImg2ImgPipeline(**inpaint.components)
+        text2img = StableDiffusionPipeline(**inpaint.components)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        image_inpaint = inpaint(
+            [prompt],
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+        ).images
+        image_img2img = img2img(
+            [prompt],
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        ).images
+        image_text2img = text2img(
+            [prompt],
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+        ).images
+
+        assert image_inpaint.shape == (1, 32, 32, 3)
+        assert image_img2img.shape == (1, 32, 32, 3)
+        assert image_text2img.shape == (1, 128, 128, 3)
+
 
 class PipelineTesterMixin(unittest.TestCase):
     def tearDown(self):