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docs/source/en/_toctree.yml
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- local: using-diffusers/custom_pipeline_examples - local: using-diffusers/custom_pipeline_examples
title: Community pipelines title: Community pipelines
- local: using-diffusers/contribute_pipeline - local: using-diffusers/contribute_pipeline
title: How to contribute a community pipeline title: Contribute a community pipeline
title: Pipelines for Inference title: Specific pipeline examples
- sections: - sections:
- local: training/overview - local: training/overview
title: Overview title: Overview
...@@ -168,8 +168,6 @@ ...@@ -168,8 +168,6 @@
title: Custom normalization layers title: Custom normalization layers
- local: api/attnprocessor - local: api/attnprocessor
title: Attention Processor title: Attention Processor
- local: api/diffusion_pipeline
title: Diffusion Pipeline
- local: api/logging - local: api/logging
title: Logging title: Logging
- local: api/configuration - local: api/configuration
......
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<!--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
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# Pipelines
The [`DiffusionPipeline`] is the quickest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) for inference.
<Tip>
You shouldn't use the [`DiffusionPipeline`] class for training or finetuning a diffusion model. Individual
components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
</Tip>
The pipeline type (for example [`StableDiffusionPipeline`]) of any diffusion pipeline loaded with [`~DiffusionPipeline.from_pretrained`] is automatically
detected and pipeline components are loaded and passed to the `__init__` function of the pipeline.
Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
## DiffusionPipeline
[[autodoc]] DiffusionPipeline
- all
- __call__
- device
- to
- components
docs/source/en/api/pipelines/overview.md
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...@@ -12,16 +12,74 @@ specific language governing permissions and limitations under the License. ...@@ -12,16 +12,74 @@ specific language governing permissions and limitations under the License.
# Pipelines # Pipelines
Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different scheduler or even model components. Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.
All pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components. All pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example [`StableDiffusionPipeline`]) loaded with [`~DiffusionPipeline.from_pretrained`] are automatically detected and the pipeline components are loaded and passed to the `__init__` function of the pipeline.
<Tip warning={true}> <Tip warning={true}>
Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [`~DiffusionPipeline.__call__`] method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../traininig/overview) guides instead! You shouldn't use the [`DiffusionPipeline`] class for training. Individual components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
<br>
Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [`~DiffusionPipeline.__call__`] method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../../training/overview) guides instead!
</Tip> </Tip>
The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.
| Pipeline | Tasks |
|---|---|
| [AltDiffusion](alt_diffusion) | image2image |
| [Attend-and-Excite](attend_and_excite) | text2image |
| [Audio Diffusion](audio_diffusion) | image2audio |
| [AudioLDM](audioldm) | text2audio |
| [AudioLDM2](audioldm2) | text2audio |
| [BLIP Diffusion](blip_diffusion) | text2image |
| [Consistency Models](consistency_models) | unconditional image generation |
| [ControlNet](controlnet) | text2image, image2image, inpainting |
| [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |
| [Cycle Diffusion](cycle_diffusion) | image2image |
| [Dance Diffusion](dance_diffusion) | unconditional audio generation |
| [DDIM](ddim) | unconditional image generation |
| [DDPM](ddpm) | unconditional image generation |
| [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
| [DiffEdit](diffedit) | inpainting |
| [DiT](dit) | text2image |
| [GLIGEN](gligen) | text2image |
| [InstructPix2Pix](pix2pix) | image editing |
| [Kandinsky](kandinsky) | text2image, image2image, inpainting, interpolation |
| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
| [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
| [LDM3D](ldm3d_diffusion) | text2image, text-to-3D |
| [MultiDiffusion](panorama) | text2image |
| [MusicLDM](musicldm) | text2audio |
| [PaintByExample](paint_by_example) | inpainting |
| [ParaDiGMS](paradigms) | text2image |
| [Pix2Pix Zero](pix2pix_zero) | image editing |
| [PNDM](pndm) | unconditional image generation |
| [RePaint](repaint) | inpainting |
| [ScoreSdeVe](score_sde_ve) | unconditional image generation |
| [Self-Attention Guidance](self_attention_guidance) | text2image |
| [Semantic Guidance](semantic_stable_diffusion) | text2image |
| [Shap-E](shap_e) | text-to-3D, image-to-3D |
| [Spectrogram Diffusion](spectrogram_diffusion) | |
| [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
| [Stable Diffusion Model Editing](model_editing) | model editing |
| [Stable Diffusion XL](stable_diffusion_xl) | text2image, image2image, inpainting |
| [Stable unCLIP](stable_unclip) | text2image, image variation |
| [KarrasVe](karras_ve) | unconditional image generation |
| [T2I Adapter](adapter) | text2image |
| [Text2Video](text_to_video) | text2video, video2video |
| [Text2Video Zero](text_to_video_zero) | text2video |
| [UnCLIP](unclip) | text2image, image variation |
| [Unconditional Latent Diffusion](latent_diffusion_uncond) | unconditional image generation |
| [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| [Value-guided planning](value_guided_sampling) | value guided sampling |
| [Versatile Diffusion](versatile_diffusion) | text2image, image variation |
| [VQ Diffusion](vq_diffusion) | text2image |
| [Wuerstchen](wuerstchen) | text2image |
## DiffusionPipeline ## DiffusionPipeline
[[autodoc]] DiffusionPipeline [[autodoc]] DiffusionPipeline
......
docs/source/en/index.md
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...@@ -22,7 +22,7 @@ specific language governing permissions and limitations under the License. ...@@ -22,7 +22,7 @@ specific language governing permissions and limitations under the License.
The library has three main components: The library has three main components:
- State-of-the-art [diffusion pipelines](api/pipelines/overview) for inference with just a few lines of code. - State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality. - Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. - Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
...@@ -45,54 +45,4 @@ The library has three main components: ...@@ -45,54 +45,4 @@ The library has three main components:
<p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p> <p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
</a> </a>
</div> </div>
</div> </div>
\ No newline at end of file
## Supported pipelines
| Pipeline | Paper/Repository | Tasks |
|---|---|:---:|
| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |
| [controlnet](./api/pipelines/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |
| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |
| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |
| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |
| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |
| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |
| [stable_diffusion_adapter](./api/pipelines/stable_diffusion/adapter) | [**T2I-Adapter**](https://arxiv.org/abs/2302.08453) | Image-to-Image Text-Guided Generation | -
| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |
| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |
| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |
| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) | Text-Guided Image Editing|
| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |
| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |
| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |
| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |
| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
| [stable_diffusion_ldm3d](./api/pipelines/stable_diffusion/ldm3d_diffusion) | [LDM3D: Latent Diffusion Model for 3D](https://arxiv.org/abs/2305.10853) | Text to Image and Depth Generation |
docs/source/en/installation.md
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...@@ -12,12 +12,10 @@ specific language governing permissions and limitations under the License. ...@@ -12,12 +12,10 @@ specific language governing permissions and limitations under the License.
# Installation # Installation
Install 🤗 Diffusers for whichever deep learning library you're working with. 🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:
🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+ and Flax. Follow the installation instructions below for the deep learning library you are using: - [PyTorch](https://pytorch.org/get-started/locally/) installation instructions
- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions
- [PyTorch](https://pytorch.org/get-started/locally/) installation instructions.
- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions.
## Install with pip ## Install with pip
...@@ -37,7 +35,7 @@ Activate the virtual environment: ...@@ -37,7 +35,7 @@ Activate the virtual environment:
source .env/bin/activate source .env/bin/activate
``` ```
🤗 Diffusers also relies on the 🤗 Transformers library, and you can install both with the following command: You should also install 🤗 Transformers because 🤗 Diffusers relies on its models:
<frameworkcontent> <frameworkcontent>
<pt> <pt>
...@@ -54,9 +52,7 @@ pip install diffusers["flax"] transformers ...@@ -54,9 +52,7 @@ pip install diffusers["flax"] transformers
## Install from source ## Install from source
Before installing 🤗 Diffusers from source, make sure you have `torch` and 🤗 Accelerate installed. Before installing 🤗 Diffusers from source, make sure you have PyTorch and 🤗 Accelerate installed.
For `torch` installation, refer to the `torch` [installation](https://pytorch.org/get-started/locally/#start-locally) guide.
To install 🤗 Accelerate: To install 🤗 Accelerate:
...@@ -64,7 +60,7 @@ To install 🤗 Accelerate: ...@@ -64,7 +60,7 @@ To install 🤗 Accelerate:
pip install accelerate pip install accelerate
``` ```
Install 🤗 Diffusers from source with the following command: Then install 🤗 Diffusers from source:
```bash ```bash
pip install git+https://github.com/huggingface/diffusers pip install git+https://github.com/huggingface/diffusers
...@@ -75,7 +71,7 @@ The `main` version is useful for staying up-to-date with the latest developments ...@@ -75,7 +71,7 @@ The `main` version is useful for staying up-to-date with the latest developments
For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet.
However, this means the `main` version may not always be stable. However, this means the `main` version may not always be stable.
We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day. We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day.
If you run into a problem, please open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose), so we can fix it even sooner! If you run into a problem, please open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) so we can fix it even sooner!
## Editable install ## Editable install
...@@ -123,17 +119,29 @@ git pull ...@@ -123,17 +119,29 @@ git pull
Your Python environment will find the `main` version of 🤗 Diffusers on the next run. Your Python environment will find the `main` version of 🤗 Diffusers on the next run.
## Notice on telemetry logging ## Cache
Model weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the `HF_HOME` or `HUGGINFACE_HUB_CACHE` environment variables or configuring the `cache_dir` parameter in methods like [`~DiffusionPipeline.from_pretrained`].
Cached files allow you to run 🤗 Diffusers offline. To prevent 🤗 Diffusers from connecting to the internet, set the `HF_HUB_OFFLINE` environment variable to `True` and 🤗 Diffusers will only load previously downloaded files in the cache.
```shell
export HF_HUB_OFFLINE=True
```
For more details about managing and cleaning the cache, take a look at the [caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache) guide.
## Telemetry logging
Our library gathers telemetry information during `from_pretrained()` requests. Our library gathers telemetry information during [`~DiffusionPipeline.from_pretrained`] requests.
This data includes the version of Diffusers and PyTorch/Flax, the requested model or pipeline class, The data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class,
and the path to a pre-trained checkpoint if it is hosted on the Hub. and the path to a pretrained checkpoint if it is hosted on the Hugging Face Hub.
This usage data helps us debug issues and prioritize new features. This usage data helps us debug issues and prioritize new features.
Telemetry is only sent when loading models and pipelines from the HuggingFace Hub, Telemetry is only sent when loading models and pipelines from the Hub,
and is not collected during local usage. and it is not collected if you're loading local files.
We understand that not everyone wants to share additional information, and we respect your privacy, We understand that not everyone wants to share additional information,and we respect your privacy.
so you can disable telemetry collection by setting the `DISABLE_TELEMETRY` environment variable from your terminal: You can disable telemetry collection by setting the `DISABLE_TELEMETRY` environment variable from your terminal:
On Linux/MacOS: On Linux/MacOS:
```bash ```bash
......
docs/source/en/using-diffusers/contribute_pipeline.md
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...@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o ...@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License. specific language governing permissions and limitations under the License.
--> -->
# How to contribute a community pipeline # Contribute a community pipeline
<Tip> <Tip>
......
docs/source/en/using-diffusers/pipeline_overview.md
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...@@ -14,4 +14,4 @@ specific language governing permissions and limitations under the License. ...@@ -14,4 +14,4 @@ specific language governing permissions and limitations under the License.
A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionXLPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components. A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionXLPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components.
This section introduces you to some of the more complex pipelines like Stable Diffusion XL, ControlNet, and DiffEdit, which require additional inputs. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to control randomness on your hardware when generating images, and how to create a community pipeline for a custom task like generating images from speech. This section demonstrates how to use specific pipelines such as Stable Diffusion XL, ControlNet, and DiffEdit. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to create reproducible pipelines, and how to use and contribute community pipelines.
\ No newline at end of file \ No newline at end of file
docs/source/en/using-diffusers/textual_inversion_inference.md
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The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer). The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).
This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](./training/text_inversion) training guide. This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](../training/text_inversion) training guide.
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