[docs] Clean up pipeline apis (huggingface#3905)

* start with stable diffusion

* fix

* finish stable diffusion pipelines

* fix path to pipeline output

* fix flax paths

* fix copies

* add up to score sde ve

* finish first pass of pipelines

* fix copies

* second review

* align doc titles

* more review fixes

* final review

Author: stevhliu

docs/source/en/_toctree.yml

@@ -181,7 +181,7 @@
     - local: api/pipelines/alt_diffusion
       title: AltDiffusion
     - local: api/pipelines/attend_and_excite
-      title: Attend and Excite
+      title: Attend-and-Excite
     - local: api/pipelines/audio_diffusion
       title: Audio Diffusion
     - local: api/pipelines/audioldm
@@ -211,7 +211,7 @@
     - local: api/pipelines/latent_diffusion
       title: Latent Diffusion
     - local: api/pipelines/panorama
-      title: MultiDiffusion Panorama
+      title: MultiDiffusion
     - local: api/pipelines/paint_by_example
       title: PaintByExample
     - local: api/pipelines/paradigms
@@ -238,25 +238,25 @@
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
       - local: api/pipelines/stable_diffusion/text2img
-        title: Text-to-Image
+        title: Text-to-image
       - local: api/pipelines/stable_diffusion/img2img
-        title: Image-to-Image
+        title: Image-to-image
       - local: api/pipelines/stable_diffusion/inpaint
-        title: Inpaint
+        title: Inpainting
       - local: api/pipelines/stable_diffusion/depth2img
-        title: Depth-to-Image
+        title: Depth-to-image
       - local: api/pipelines/stable_diffusion/image_variation
-        title: Image-Variation
+        title: Image variation
       - local: api/pipelines/stable_diffusion/stable_diffusion_safe
         title: Safe Stable Diffusion
       - local: api/pipelines/stable_diffusion/stable_diffusion_2
         title: Stable Diffusion 2
       - local: api/pipelines/stable_diffusion/stable_diffusion_xl
         title: Stable Diffusion XL
       - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Stable-Diffusion-Latent-Upscaler
+        title: Latent upscaler
       - local: api/pipelines/stable_diffusion/upscale
-        title: Super-Resolution
+        title: Super-resolution
       - local: api/pipelines/stable_diffusion/ldm3d_diffusion
         title: LDM3D Text-to-(RGB, Depth)
       - local: api/pipelines/stable_diffusion/adapter
@@ -267,11 +267,11 @@
     - local: api/pipelines/stochastic_karras_ve
       title: Stochastic Karras VE
     - local: api/pipelines/model_editing
-      title: Text-to-Image Model Editing
+      title: Text-to-image model editing
     - local: api/pipelines/text_to_video
-      title: Text-to-Video
+      title: Text-to-video
     - local: api/pipelines/text_to_video_zero
-      title: Text-to-Video Zero
+      title: Text2Video-Zero
     - local: api/pipelines/unclip
       title: UnCLIP
     - local: api/pipelines/latent_diffusion_uncond

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

@@ -12,72 +12,36 @@ specific language governing permissions and limitations under the License.
 
 # AltDiffusion
 
-AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
+AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
 
-The abstract of the paper is the following:
+The abstract from the paper is:
 
 *In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*
 
-
-*Overview*:
-
-| Pipeline | Tasks | Colab | Demo
-|---|---|:---:|:---:|
-| [pipeline_alt_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py) | *Text-to-Image Generation* | - | -
-| [pipeline_alt_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | - |-
-
 ## Tips
 
-- AltDiffusion is conceptually exactly the same as [Stable Diffusion](./stable_diffusion/overview).
-
-- *Run AltDiffusion*
-
-AltDiffusion can be tested very easily with the [`AltDiffusionPipeline`], [`AltDiffusionImg2ImgPipeline`] and the `"BAAI/AltDiffusion-m9"` checkpoint exactly in the same way it is shown in the [Conditional Image Generation Guide](../../using-diffusers/conditional_image_generation) and the [Image-to-Image Generation Guide](../../using-diffusers/img2img).
-
-- *How to load and use different schedulers.*
-
-The alt diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the alt diffusion pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
-To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:
-
-```python
->>> from diffusers import AltDiffusionPipeline, EulerDiscreteScheduler
-
->>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("BAAI/AltDiffusion-m9", subfolder="scheduler")
->>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", scheduler=euler_scheduler)
-```
-
-
-- *How to convert all use cases with multiple or single pipeline*
-
-If you want to use all possible use cases in a single `DiffusionPipeline` we recommend using the `components` functionality to instantiate all components in the most memory-efficient way:
+`AltDiffusion` is conceptually the same as [Stable Diffusion](./stable_diffusion/overview).
 
-```python
->>> from diffusers import (
-...     AltDiffusionPipeline,
-...     AltDiffusionImg2ImgPipeline,
-... )
+<Tip>
 
->>> text2img = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9")
->>> img2img = AltDiffusionImg2ImgPipeline(**text2img.components)
+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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
 
->>> # now you can use text2img(...) and img2img(...) just like the call methods of each respective pipeline
-```
-
-## AltDiffusionPipelineOutput
-[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
-	- all
-	- __call__
+</Tip>
 
 ## AltDiffusionPipeline
+
 [[autodoc]] AltDiffusionPipeline
 	- all
 	- __call__
 
 ## AltDiffusionImg2ImgPipeline
+
 [[autodoc]] AltDiffusionImg2ImgPipeline
 	- all
 	- __call__
+
+## AltDiffusionPipelineOutput
+
+[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
+	- all
+	- __call__

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

@@ -10,66 +10,28 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Attend and Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
+# Attend-and-Excite
 
-## Overview
+Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.
 
-Attend and Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over the image generation.
-
-The abstract of the paper is the following:
+The abstract from the paper is:
 
 *Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
 
-Resources
-
-* [Project Page](https://attendandexcite.github.io/Attend-and-Excite/)
-* [Paper](https://arxiv.org/abs/2301.13826)
-* [Original Code](https://github.com/AttendAndExcite/Attend-and-Excite)
-* [Demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite)
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab | Demo
-|---|---|:---:|:---:|
-| [pipeline_semantic_stable_diffusion_attend_and_excite.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_semantic_stable_diffusion_attend_and_excite) | *Text-to-Image Generation* | - | https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite
-
-
-### Usage example
-
+You can find additional information about Attend-and-Excite on the [project page](https://attendandexcite.github.io/Attend-and-Excite/), the [original codebase](https://github.com/AttendAndExcite/Attend-and-Excite), or try it out in a [demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite).
 
-```python
-import torch
-from diffusers import StableDiffusionAttendAndExcitePipeline
+<Tip>
 
-model_id = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-pipe = pipe.to("cuda")
-
-prompt = "a cat and a frog"
-
-# use get_indices function to find out indices of the tokens you want to alter
-pipe.get_indices(prompt)
-
-token_indices = [2, 5]
-seed = 6141
-generator = torch.Generator("cuda").manual_seed(seed)
-
-images = pipe(
-    prompt=prompt,
-    token_indices=token_indices,
-    guidance_scale=7.5,
-    generator=generator,
-    num_inference_steps=50,
-    max_iter_to_alter=25,
-).images
-
-image = images[0]
-image.save(f"../images/{prompt}_{seed}.png")
-```
+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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
 
+</Tip>
 
 ## StableDiffusionAttendAndExcitePipeline
+
 [[autodoc]] StableDiffusionAttendAndExcitePipeline
 	- all
 	- __call__
+
+## StableDiffusionPipelineOutput
+
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -12,87 +12,26 @@ specific language governing permissions and limitations under the License.
 
 # Audio Diffusion
 
-## Overview
+[Audio Diffusion](https://github.com/teticio/audio-diffusion) is by Robert Dargavel Smith, and it leverages the recent advances in image generation from diffusion models by converting audio samples to and from Mel spectrogram images.
 
-[Audio Diffusion](https://github.com/teticio/audio-diffusion) by Robert Dargavel Smith.
+The original codebase, training scripts and example notebooks can be found at [teticio/audio-diffusion](https://github.com/teticio/audio-diffusion).
 
-Audio Diffusion leverages the recent advances in image generation using diffusion models by converting audio samples to
-and from mel spectrogram images.
+<Tip>
 
-The original codebase of this implementation can be found [here](https://github.com/teticio/audio-diffusion), including
-training scripts and example notebooks.
+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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
 
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_audio_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py) | *Unconditional Audio Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb) |
-
-
-## Examples:
-
-### Audio Diffusion
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-### Latent Audio Diffusion
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-### Audio Diffusion with DDIM (faster)
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-### Variations, in-painting, out-painting etc.
-
-```python
-output = pipe(
-    raw_audio=output.audios[0, 0],
-    start_step=int(pipe.get_default_steps() / 2),
-    mask_start_secs=1,
-    mask_end_secs=1,
-)
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
+</Tip>
 
 ## AudioDiffusionPipeline
 [[autodoc]] AudioDiffusionPipeline
 	- all
 	- __call__
 
+## AudioPipelineOutput
+[[autodoc]] pipelines.AudioPipelineOutput
+
+## ImagePipelineOutput
+[[autodoc]] pipelines.ImagePipelineOutput
+
 ## Mel
 [[autodoc]] Mel