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https://huggingface.co/docs/diffusers/api/models/unet2d | Diffusers documentation
UNet2DModel
UNet2DModel
The UNet model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system ... |
https://huggingface.co/docs/diffusers/using-diffusers/controlnet | ControlNet
ControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affo... |
https://huggingface.co/docs/diffusers/api/pipelines/latent_diffusion | Latent Diffusion
Latent Diffusion was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
The abstract from the paper is:
By decomposing the image formation process into a sequential application of denoising autoencod... |
https://huggingface.co/docs/diffusers/using-diffusers/sdxl | Stable Diffusion XL
Stable Diffusion XL (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:
the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number o... |
https://huggingface.co/docs/diffusers/using-diffusers/shap-e | Shap-E
Shap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained ... |
https://huggingface.co/docs/diffusers/using-diffusers/diffedit | DiffEdit
Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:
the diffusion model denoises an image cond... |
https://huggingface.co/docs/diffusers/api/pipelines/kandinsky_v22 | Kandinsky 2.2
The Kandinsky 2.2 release includes robust new text-to-image models that support text-to-image generation, image-to-image generation, image interpolation, and text-guided image inpainting. The general workflow to perform these tasks using Kandinsky 2.2 is the same as in Kandinsky 2.1. First, you will need... |
https://huggingface.co/docs/diffusers/using-diffusers/distilled_sd | Distilled Stable Diffusion inference
Stable Diffusion inference can be a computationally intensive process because it must iteratively denoise the latents to generate an image. To reduce the computational burden, you can use a distilled version of the Stable Diffusion model from Nota AI. The distilled version of their... |
https://huggingface.co/docs/diffusers/api/pipelines/paint_by_example | PaintByExample
Paint by Example: Exemplar-based Image Editing with Diffusion Models is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.
The abstract from the paper is:
Language-guided image editing has achieved great success recently. In this paper, for the first time, w... |
https://huggingface.co/docs/diffusers/using-diffusers/reproducibility | Create reproducible pipelines
Reproducibility is important for testing, replicating results, and can even be used to improve image quality. However, the randomness in diffusion models is a desired property because it allows the pipeline to generate different images every time it is run. While you can’t expect to get t... |
https://huggingface.co/docs/diffusers/api/pipelines/kandinsky | Kandinsky
Overview
Kandinsky inherits best practices from DALL-E 2 and Latent Diffusion, while introducing some new ideas.
It uses CLIP for encoding images and text, and a diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach enhances the visual performance of the model and unveils n... |
https://huggingface.co/docs/diffusers/api/pipelines/musicldm | MusicLDM
MusicLDM was proposed in MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov. MusicLDM takes a text prompt as input and predicts the corresponding music sample.
Inspired by... |
https://huggingface.co/docs/diffusers/api/pipelines/panorama | MultiDiffusion
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.
The abstract from the paper is:
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user contr... |
https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_examples | Community pipelines
For more information about community pipelines, please have a look at this issue.
Community examples consist of both inference and training examples that have been added by the community. Please have a look at the following table to get an overview of all community examples. Click on the Code Examp... |
https://huggingface.co/docs/diffusers/training/overview | 🧨 Diffusers Training Examples
Diffusers training examples are a collection of scripts to demonstrate how to effectively use the diffusers library for a variety of use cases.
Note: If you are looking for official examples on how to use diffusers for inference, please have a look at src/diffusers/pipelines
Our examples... |
https://huggingface.co/docs/diffusers/using-diffusers/contribute_pipeline | How to contribute a community pipeline
💡 Take a look at GitHub Issue #841 for more context about why we’re adding community pipelines to help everyone easily share their work without being slowed down.
Community pipelines allow you to add any additional features you’d like on top of the DiffusionPipeline. The main be... |
https://huggingface.co/docs/diffusers/api/pipelines/pndm | PNDM
Pseudo Numerical methods for Diffusion Models on manifolds (PNDM) is by Luping Liu, Yi Ren, Zhijie Lin and Zhou Zhao.
The abstract from the paper is:
Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of ... |
https://huggingface.co/docs/diffusers/training/create_dataset | Create a dataset for training
There are many datasets on the Hub to train a model on, but if you can’t find one you’re interested in or want to use your own, you can create a dataset with the 🤗 Datasets library. The dataset structure depends on the task you want to train your model on. The most basic dataset structur... |
https://huggingface.co/docs/diffusers/training/adapt_a_model | Adapt a model to a new task
Many diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task.
This guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained UNet2DC... |
https://huggingface.co/docs/diffusers/api/pipelines/repaint | RePaint
RePaint: Inpainting using Denoising Diffusion Probabilistic Models is by Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool.
The abstract from the paper is:
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary ma... |
https://huggingface.co/docs/diffusers/api/pipelines/paradigms | Parallel Sampling of Diffusion Models
Parallel Sampling of Diffusion Models is by Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari.
The abstract from the paper is:
Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one samp... |
https://huggingface.co/docs/diffusers/training/unconditional_training | Unconditional image generation
Unconditional image generation is not conditioned on any text or images, unlike text- or image-to-image models. It only generates images that resemble its training data distribution.
This guide will show you how to train an unconditional image generation model on existing datasets as wel... |
https://huggingface.co/docs/diffusers/api/pipelines/pix2pix_zero | Pix2Pix Zero
Zero-shot Image-to-Image Translation is by Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu.
The abstract from the paper is:
Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is... |
https://huggingface.co/docs/diffusers/training/text_inversion | Textual Inversion
Textual Inversion is a technique for capturing novel concepts from a small number of example images. While the technique was originally demonstrated with a latent diffusion model, it has since been applied to other model variants like Stable Diffusion. The learned concepts can be used to better contr... |
https://huggingface.co/docs/diffusers/api/pipelines/self_attention_guidance | Self-Attention Guidance
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance is by Susung Hong et al.
The abstract from the paper is:
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of... |
https://huggingface.co/docs/diffusers/api/pipelines/spectrogram_diffusion | Spectrogram Diffusion
Spectrogram Diffusion is by Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, and Jesse Engel.
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes... |
https://huggingface.co/docs/diffusers/api/pipelines/score_sde_ve | Score SDE VE
Score-Based Generative Modeling through Stochastic Differential Equations (Score SDE) is by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole. This pipeline implements the variance expanding (VE) variant of the stochastic differential equation method.
The ab... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview | Stable Diffusion pipelines
Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. This specific type of diffus... |
https://huggingface.co/docs/diffusers/api/pipelines/semantic_stable_diffusion | class diffusers.SemanticStableDiffusionPipeline
< source >
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )
... |
https://huggingface.co/docs/diffusers/training/dreambooth | DreamBooth
DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. It allows the model to generate contextualized images of the subject in different scenes, poses, and views.
Dreambooth examples from the project's blog.
This guide will show you how t... |
https://huggingface.co/docs/diffusers/training/lora | Low-Rank Adaptation of Large Language Models (LoRA)
This is an experimental feature. Its APIs can change in future.
Low-Rank Adaptation of Large Language Models (LoRA) is a training method that accelerates the training of large models while consuming less memory. It adds pairs of rank-decomposition weight matrices (ca... |
https://huggingface.co/docs/diffusers/training/text2image | Text-to-image
The text-to-image fine-tuning script is experimental. It’s easy to overfit and run into issues like catastrophic forgetting. We recommend you explore different hyperparameters to get the best results on your dataset.
Text-to-image models like Stable Diffusion generate an image from a text prompt. This gu... |
https://huggingface.co/docs/diffusers/api/pipelines/shap_e | Shap-E
The Shap-E model was proposed in Shap-E: Generating Conditional 3D Implicit Functions by Alex Nichol and Heewon Jun from OpenAI.
The abstract from the paper is:
We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representatio... |
https://huggingface.co/docs/diffusers/training/instructpix2pix | InstructPix2Pix
InstructPix2Pix is a method to fine-tune text-conditioned diffusion models such that they can follow an edit instruction for an input image. Models fine-tuned using this method take the following as inputs:
The output is an “edited” image that reflects the edit instruction applied on the input image:
T... |
https://huggingface.co/docs/diffusers/training/custom_diffusion | Custom Diffusion training example
Custom Diffusion is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject. The train_custom_diffusion.py script shows how to implement the training procedure and adapt it for stable diffusion.
This training example was contributed ... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint | Inpainting
The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.
Tips
It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such as runwayml/stab... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img | Image-to-image
The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images.
The StableDiffusionImg2ImgPipeline uses the diffusion-denoising mechanism proposed in SDEdit: Guided Image Synthesis and Editing with Stoch... |
https://huggingface.co/docs/diffusers/training/controlnet | ControlNet
Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet) by Lvmin Zhang and Maneesh Agrawala.
This example is based on the training example in the original ControlNet repository. It trains a ControlNet to fill circles using a small synthetic dataset.
Installing the dependencies
Before runn... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img | Text-to-image
The Stable Diffusion model was created by researchers and engineers from CompVis, Stability AI, Runway, and LAION. The StableDiffusionPipeline is capable of generating photorealistic images given any text input. It’s trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a froze... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_2 | Stable Diffusion 2
Stable Diffusion 2 is a text-to-image latent diffusion model built upon the work of the original Stable Diffusion, and it was led by Robin Rombach and Katherine Crowson from Stability AI and LAION.
The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text e... |
https://huggingface.co/docs/diffusers/using-diffusers/other-modalities | Diffusers documentation
Using Diffusers with other modalities
Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Using Diffusers with other modalities
Dif... |
https://huggingface.co/docs/diffusers/optimization/fp16 | Memory and speed
We present some techniques and ideas to optimize 🤗 Diffusers inference for memory or speed. As a general rule, we recommend the use of xFormers for memory efficient attention, please see the recommended installation instructions.
We’ll discuss how the following settings impact performance and memory.... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/depth2img | The Stable Diffusion model can also infer depth based on an image using MiDas. This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a depth_map to preserve the image structure.
Make sure to check out the Stable Diffusion Tips section to learn how to explore th... |
https://huggingface.co/docs/diffusers/training/t2i_adapters | T2I-Adapters for Stable Diffusion XL (SDXL)
The train_t2i_adapter_sdxl.py script (as shown below) shows how to implement the T2I-Adapter training procedure for Stable Diffusion XL.
Running locally with PyTorch
Installing the dependencies
Before running the scripts, make sure to install the library’s training depende... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation | The Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by Justin Pinkney from Lambda.
Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipelin... |
https://huggingface.co/docs/diffusers/optimization/torch2.0 | Accelerated PyTorch 2.0 support in Diffusers
Starting from version 0.13.0, Diffusers supports the latest optimization from PyTorch 2.0. These include:
Support for accelerated transformers implementation with memory-efficient attention – no extra dependencies (such as xformers) required.
torch.compile support for extra... |
https://huggingface.co/docs/diffusers/api/models/autoencoder_tiny | Tiny AutoEncoder
Tiny AutoEncoder for Stable Diffusion (TAESD) was introduced in madebyollin/taesd by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion’s VAE that can quickly decode the latents in a StableDiffusionPipeline or StableDiffusionXLPipeline almost instantly.
To use with Stable Diffusion ... |
https://huggingface.co/docs/diffusers/api/models/unet3d-cond | Diffusers documentation
UNet3DConditionModel
UNet3DConditionModel
The UNet model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a... |
https://huggingface.co/docs/diffusers/api/models/asymmetricautoencoderkl | AsymmetricAutoencoderKL
Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: Designing a Better Asymmetric VQGAN for StableDiffusion by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
The abstract from the paper is:
StableDiffusion is a revo... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_safe | Safe Stable Diffusion
Safe Stable Diffusion was proposed in Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models and mitigates inappropriate degeneration from Stable Diffusion models because they’re trained on unfiltered web-crawled datasets. For instance Stable Diffusion may unexpectedly g... |
https://huggingface.co/docs/diffusers/api/models/vq | VQModel
The VQ-VAE model was introduced in Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. The model is used in 🤗 Diffusers to decode latent representations into images. Unlike AutoencoderKL, the VQModel works in a quantized latent space.
The abstract from the paper... |
https://huggingface.co/docs/diffusers/api/models/autoencoderkl | AutoencoderKL
The variational autoencoder (VAE) model with KL loss was introduced in Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The abstract from the paper is:
How can we perfor... |
https://huggingface.co/docs/diffusers/optimization/opt_overview | Overview
Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🧨 Diffuser’s goal is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized ha... |
https://huggingface.co/docs/diffusers/api/models/prior_transformer | Prior Transformer
The Prior Transformer was originally introduced in Hierarchical Text-Conditional Image Generation with CLIP Latents by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.
The abstract from the paper... |
https://huggingface.co/docs/diffusers/api/models/transformer_temporal | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Transformer Temporal
A Transformer model for video-like data.
TransformerTemporalModel
class diffusers.... |
https://huggingface.co/docs/diffusers/api/models/transformer2d | Transformer2D
A Transformer model for image-like data from CompVis that is based on the Vision Transformer introduced by Dosovitskiy et al. The Transformer2DModel accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.
When the input is continuous:
Project the input and reshape it to ... |
https://huggingface.co/docs/diffusers/api/pipelines/audioldm | AudioLDM
AudioLDM was proposed in AudioLDM: Text-to-Audio Generation with Latent Diffusion Models by Haohe Liu et al. Inspired by Stable Diffusion, AudioLDM is a text-to-audio latent diffusion model (LDM) that learns continuous audio representations from CLAP latents. AudioLDM takes a text prompt as input and predicts... |
https://huggingface.co/docs/diffusers/api/pipelines/overview | 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 com... |
https://huggingface.co/docs/diffusers/api/pipelines/attend_and_excite | Attend-and-Excite
Attend-and-Excite for Stable Diffusion was proposed in Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models and provides textual attention control over image generation.
The abstract from the paper is:
Text-to-image diffusion models have recently received a lot of i... |
https://huggingface.co/docs/diffusers/api/models/controlnet | ControlNet
The ControlNet model was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and ... |
https://huggingface.co/docs/diffusers/api/pipelines/alt_diffusion | AltDiffusion
AltDiffusion was proposed in AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
The abstract from the paper is:
In this work, we present a conceptually simple and effective method to train a stro... |
https://huggingface.co/docs/diffusers/api/pipelines/audio_diffusion | Audio Diffusion
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.
The original codebase, training scripts and example notebooks can be found at teticio/audio-diffusion.
Make sure to... |
https://huggingface.co/docs/diffusers/api/pipelines/ddpm | DDPM
Denoising Diffusion Probabilistic Models (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.
The abstract from the paper is:
We present high qualit... |
https://huggingface.co/docs/diffusers/api/pipelines/cycle_diffusion | Cycle Diffusion
Cycle Diffusion is a text guided image-to-image generation model proposed in Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance by Chen Henry Wu, Fernando De la Torre.
The abstract from the paper is:
Diffusion models have achieved unprecedented performance in gene... |
https://huggingface.co/docs/diffusers/api/pipelines/controlnet_sdxl | ControlNet with Stable Diffusion XL
ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a dep... |
https://huggingface.co/docs/diffusers/api/pipelines/consistency_models | Consistency Models
Consistency Models were proposed in Consistency Models by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.
The abstract from the paper is:
Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that ... |
https://huggingface.co/docs/diffusers/api/pipelines/audioldm2 | AudioLDM 2
AudioLDM 2 was proposed in AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.
Inspired by Stable Diffusion, A... |
https://huggingface.co/docs/diffusers/api/pipelines/controlnet | ControlNet
ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet mo... |
https://huggingface.co/docs/diffusers/api/pipelines/dance_diffusion | Dance Diffusion
Dance Diffusion is by Zach Evans.
Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by Harmonai.
The original codebase of this implementation can be found at Harmonai-org.
Make sure to check out the Schedulers guide to learn how to explore the tradeo... |
https://huggingface.co/docs/diffusers/api/pipelines/ddim | DDIM
Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The abstract from the paper is:
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produc... |
https://huggingface.co/docs/diffusers/api/schedulers/multistep_dpm_solver | DPMSolverMultistepScheduler
DPMSolverMultistep is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and ... |
https://huggingface.co/docs/diffusers/api/schedulers/heun | class diffusers.HeunDiscreteScheduler
< source >
( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' use_karras_sigmas: typing.Optional[bool] =... |
https://huggingface.co/docs/diffusers/api/schedulers/euler_ancestral | EulerAncestralDiscreteScheduler
A scheduler that uses ancestral sampling with Euler method steps. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.
EulerAncestralDiscreteScheduler
class diffusers.Euler... |
https://huggingface.co/docs/diffusers/api/schedulers/euler | EulerDiscreteScheduler
The Euler scheduler (Algorithm 2) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Cro... |
https://huggingface.co/docs/diffusers/api/pipelines/diffedit | DiffEdit
DiffEdit: Diffusion-based semantic image editing with mask guidance is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.
The abstract from the paper is:
Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large var... |
https://huggingface.co/docs/diffusers/api/pipelines/auto_pipeline | AutoPipeline
AutoPipeline is designed to:
make it easy for you to load a checkpoint for a task without knowing the specific pipeline class to use
use multiple pipelines in your workflow
Based on the task, the AutoPipeline class automatically retrieves the relevant pipeline given the name or path to the pretrained weig... |
https://huggingface.co/docs/diffusers/api/schedulers/stochastic_karras_ve | Returns
~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput or tuple
If return_dict is True, ~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor. |
https://huggingface.co/docs/diffusers/api/schedulers/dpm_discrete | class diffusers.KDPM2DiscreteScheduler
< source >
( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_... |
https://huggingface.co/docs/diffusers/api/schedulers/pndm | PNDMScheduler, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at crowsonkb/k-diffusion.
class diffusers.PNDMScheduler
< source >
( num_train_timesteps: int = 1000 beta_start: floa... |
https://huggingface.co/docs/diffusers/api/schedulers/ipndm | IPNDMScheduler is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at crowsonkb/v-diffusion-pytorch.
class diffusers.IPNDMScheduler
< source >
( num_train_timesteps: int = 1000 trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None )
Parameter... |
https://huggingface.co/docs/diffusers/api/schedulers/repaint | RePaintScheduler
RePaintScheduler is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the RePaintPipeline, and it is based on the paper RePaint: Inpainting using Denoising Diffusion Probabilistic Models by Andreas Lugmayr et al.
The abstract from the pape... |
https://huggingface.co/docs/diffusers/api/schedulers/dpm_discrete_ancestral | KDPM2AncestralDiscreteScheduler
The KDPM2DiscreteScheduler with ancestral sampling is inspired by the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson.
The original codebase can be found at crowsonkb/k-diffusion.
KDPM2AncestralDi... |
https://huggingface.co/docs/diffusers/api/schedulers/dpm_sde | class diffusers.DPMSolverSDEScheduler
< source >
( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' use_karras_sigmas: typing.Optional[bool] =... |
https://huggingface.co/docs/diffusers/api/schedulers/lms_discrete | LMSDiscreteScheduler is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by Katherine Crowson, and the original implementation can be found at crowsonkb/k-diffusion.
class diffusers.LMSDiscreteScheduler
< source >
( num_train_timesteps: int = 1000 beta_start: float = 0.... |
https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if | DeepFloyd IF
Overview
DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. The model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules:
Stage 1: a base model that generates 64x64 px image based ... |
https://huggingface.co/docs/diffusers/api/schedulers/score_sde_ve | ScoreSdeVeScheduler
ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the Score-Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
T... |
https://huggingface.co/docs/diffusers/api/schedulers/singlestep_dpm_solver | DPMSolverSinglestepScheduler
DPMSolverSinglestepScheduler is a single step scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chong... |
https://huggingface.co/docs/diffusers/api/schedulers/score_sde_vp | ScoreSdeVpScheduler
ScoreSdeVpScheduler is a variance preserving stochastic differential equation (SDE) scheduler. It was introduced in the Score-Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
... |
https://huggingface.co/docs/diffusers/api/schedulers/unipc | UniPCMultistepScheduler
UniPCMultistepScheduler is a training-free framework designed for fast sampling of diffusion models. It was introduced in UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu.
It consists of a correcto... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/pix2pix_zero | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/pix2pix | undefined |
https://huggingface.co/docs/diffusers/api/models | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/panorama | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/if | undefined |
https://huggingface.co/docs/diffusers/api/schedulers/vq_diffusion | Returns
torch.FloatTensor of shape (batch size, num classes, num latent pixels)
The log probabilities for the predicted classes of the image at timestep t-1. |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale | The Stable Diffusion latent upscaler model was created by Katherine Crowson in collaboration with Stability AI. It is used to enhance the output image resolution by a factor of 2 (see this demo notebook for a demonstration of the original implementation).
Make sure to check out the Stable Diffusion Tips section to lear... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale | The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from CompVis, Stability AI, and LAION. It is used to enhance the resolution of input images by a factor of 4.
Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed a... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/attend_and_excite | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/score_sde_vp | undefined |
https://huggingface.co/docs/diffusers/if | 404
Language if not found in diffusers v0.21.0 |
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