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# EulerDiscreteScheduler
The Euler scheduler (Algorithm 2) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) 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](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/).
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# 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](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72) implementation by [Katherine Crowson](https://github.com/crowsonkb/).
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# HeunDiscreteScheduler
The Heun scheduler (Algorithm 1) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. The scheduler is ported from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library and created by [Katherine Crowson](https://github.com/crowsonkb/).
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# IPNDMScheduler
`IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at [crowsonkb/v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296).
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# Latent Consistency Model Multistep Scheduler
## Overview
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps.
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# LMSDiscreteScheduler
`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
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# DPMSolverMultistepScheduler
`DPMSolverMultistep` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in 10 steps.
## Tips
It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stable Diffusion.
The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order `sde-dpmsolver++`.
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# DPMSolverMultistepInverse
`DPMSolverMultistepInverse` is the inverted scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794) and notebook implementation of the [`DiffEdit`] latent inversion from [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb).
## Tips
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
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# Schedulers
🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`.
Depending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:
- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model
- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output
Many schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below:
[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.
The different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32).
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# PNDMScheduler
`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](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
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# 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](https://huggingface.co/papers/2201.09865) by Andreas Lugmayr et al.
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 mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. GitHub Repository: [this http URL](http://git.io/RePaint).*
The original implementation can be found at [andreas128/RePaint](https://github.com/andreas128/).
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# ScoreSdeVeScheduler
`ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
The abstract from the paper is:
*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.*
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# ScoreSdeVpScheduler
`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
The abstract from the paper is:
*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.*
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# DPMSolverSinglestepScheduler
`DPMSolverSinglestepScheduler` is a single step scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in 10 steps.
The original implementation can be found at [LuChengTHU/dpm-solver](https://github.com/LuChengTHU/dpm-solver).
## Tips
It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
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# KarrasVeScheduler
`KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) and [Score-based generative modeling through stochastic differential equations](https://huggingface.co/papers/2011.13456) papers.
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# TCDScheduler
[Trajectory Consistency Distillation](https://huggingface.co/papers/2402.19159) by Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao and Tat-Jen Cham introduced a Strategic Stochastic Sampling (Algorithm 4) that is capable of generating good samples in a small number of steps. Distinguishing it as an advanced iteration of the multistep scheduler (Algorithm 1) in the [Consistency Models](https://huggingface.co/papers/2303.01469), Strategic Stochastic Sampling specifically tailored for the trajectory consistency function.
The abstract from the paper is:
*Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the distillation errors by broadening the scope of the self-consistency boundary condition and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE. Additionally, strategic stochastic sampling is specifically designed to circumvent the accumulated errors inherent in multi-step consistency sampling, which is meticulously tailored to complement the TCD model. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs.*
The original codebase can be found at [jabir-zheng/TCD](https://github.com/jabir-zheng/TCD).
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# 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](https://huggingface.co/papers/2302.04867) by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu.
It consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
UniPC is by design model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction and data prediction models. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy.
The abstract from the paper is:
*Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., <10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256×256 (conditional) with only 10 function evaluations. Code is available at [this https URL](https://github.com/wl-zhao/UniPC).*
## Tips
It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use dynamic thresholding. This thresholding method is unsuitable for latent-space diffusion models such as Stable Diffusion.
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# VQDiffusionScheduler
`VQDiffusionScheduler` converts the transformer model's output into a sample for the unnoised image at the previous diffusion timestep. It was introduced in [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://huggingface.co/papers/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo.
The abstract from the paper is:
*We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.*
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# How to contribute to Diffusers 🧨
We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!
Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. <ahref="https://Discord.gg/G7tWnz98XR"><imgalt="Join us on Discord"src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>
Whichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility.
We enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered.
## Overview
You can contribute in many ways ranging from answering questions on issues to adding new diffusion models to
the core library.
In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.
* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).
* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose).
* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues).
* 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).
* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples).
* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples).
* 8. Fix a more difficult issue, marked by the "Good second issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22).
* 9. Add a new pipeline, model, or scheduler, see ["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and ["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md).
As said before, **all contributions are valuable to the community**.
In the following, we will explain each contribution a bit more in detail.
For all contributions 4 - 9, you will need to open a PR. It is explained in detail how to do so in [Opening a pull request](#how-to-open-a-pr).
### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord
Any question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to):
- Reports of training or inference experiments in an attempt to share knowledge
- Presentation of personal projects
- Questions to non-official training examples
- Project proposals
- General feedback
- Paper summaries
- Asking for help on personal projects that build on top of the Diffusers library
- General questions
- Ethical questions regarding diffusion models
- ...
Every question that is asked on the forum or on Discord actively encourages the community to publicly
share knowledge and might very well help a beginner in the future who has the same question you're
having. Please do pose any questions you might have.
In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
**Please** keep in mind that the more effort you put into asking or answering a question, the higher
the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
**NOTE about channels**:
[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
In addition, questions and answers posted in the forum can easily be linked to.
In contrast, *Discord* has a chat-like format that invites fast back-and-forth communication.
While it will most likely take less time for you to get an answer to your question on Discord, your
question won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers.
### 2. Opening new issues on the GitHub issues tab
The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of
the problems they encounter. So thank you for reporting an issue.
Remember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design.
In a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).
**Please consider the following guidelines when opening a new issue**:
- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues).
- Please never report a new issue on another (related) issue. If another issue is highly related, please
open a new issue nevertheless and link to the related issue.
- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English.
- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c "import diffusers; print(diffusers.__version__)"` is higher or matches the latest Diffusers version.
- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues.
New issues usually include the following.
#### 2.1. Reproducible, minimal bug reports
A bug report should always have a reproducible code snippet and be as minimal and concise as possible.
This means in more detail:
- Narrow the bug down as much as you can, **do not just dump your whole code file**.
- Format your code.
- Do not include any external libraries except for Diffusers depending on them.
-**Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.
- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.
-**Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell.
- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible.
For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml).
#### 2.2. Feature requests
A world-class feature request addresses the following points:
1. Motivation first:
* Is it related to a problem/frustration with the library? If so, please explain
why. Providing a code snippet that demonstrates the problem is best.
* Is it related to something you would need for a project? We'd love to hear
about it!
* Is it something you worked on and think could benefit the community?
Awesome! Tell us what problem it solved for you.
2. Write a *full paragraph* describing the feature;
3. Provide a **code snippet** that demonstrates its future use;
4. In case this is related to a paper, please attach a link;
5. Attach any additional information (drawings, screenshots, etc.) you think may help.
You can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=).
#### 2.3 Feedback
Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed.
If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.
You can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
#### 2.4 Technical questions
Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide details on
why this part of the code is difficult to understand.
You can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml).
#### 2.5 Proposal to add a new model, scheduler, or pipeline
If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:
* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release.
* Link to any of its open-source implementation(s).
* Link to the model weights if they are available.
If you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget
to tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it.
You can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml).
### 3. Answering issues on the GitHub issues tab
Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct.
Some tips to give a high-quality answer to an issue:
- Be as concise and minimal as possible.
- Stay on topic. An answer to the issue should concern the issue and only the issue.
- Provide links to code, papers, or other sources that prove or encourage your point.
- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet.
Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great
help to the maintainers if you can answer such issues, encouraging the author of the issue to be
more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).
If you have verified that the issued bug report is correct and requires a correction in the source code,
please have a look at the next sections.
For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull request](#how-to-open-a-pr) section.
### 4. Fixing a "Good first issue"
*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already
explains how a potential solution should look so that it is easier to fix.
If the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message "I would like to try this issue.". There are usually three scenarios:
- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it.
- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR.
- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR.
### 5. Contribute to the documentation
A good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly
valuable contribution**.
Contributing to the library can have many forms:
- Correcting spelling or grammatical errors.
- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we would be very happy if you take some time to correct it.
- Correct the shape or dimensions of a docstring input or output tensor.
- Clarify documentation that is hard to understand or incorrect.
- Update outdated code examples.
- Translating the documentation to another language.
Anything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source).
Please have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally.
### 6. Contribute a community pipeline
[Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user.
Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models/overview) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).
We support two types of pipelines:
- Official Pipelines
- Community Pipelines
Both official and community pipelines follow the same design and consist of the same type of components.
Official pipelines are tested and maintained by the core maintainers of Diffusers. Their code
resides in [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
In contrast, community pipelines are contributed and maintained purely by the **community** and are **not** tested.
They reside in [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and while they can be accessed via the [PyPI diffusers package](https://pypi.org/project/diffusers/), their code is not part of the PyPI distribution.
The reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all
possible ways diffusion models can be used for inference, but some of them may be of interest to the community.
Officially released diffusion pipelines,
such as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures
high quality of maintenance, no backward-breaking code changes, and testing.
More bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library.
To add a community pipeline, one should add a <name-of-the-community>.py file to [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and adapt the [examples/community/README.md](https://github.com/huggingface/diffusers/tree/main/examples/community/README.md) to include an example of the new pipeline.
An example can be seen [here](https://github.com/huggingface/diffusers/pull/2400).
Community pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors.
Contributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the
core package.
### 7. Contribute to training examples
Diffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples).
We support two types of training examples:
- Official training examples
- Research training examples
Research training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders.
The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.
This is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.
Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
training examples, it is required to clone the repository:
Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).
Training examples of the Diffusers library should adhere to the following philosophy:
- All the code necessary to run the examples should be found in a single Python file.
- One should be able to run the example from the command line with `python <your-example>.py --args`.
- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials.
To contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like.
We strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated
with Diffusers.
Once an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include:
- An example command on how to run the example script as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch).
- A link to some training results (logs, models, etc.) that show what the user can expect as shown [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).
- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations).
If you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples.
### 8. Fixing a "Good second issue"
*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are
usually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
The issue description usually gives less guidance on how to fix the issue and requires
a decent understanding of the library by the interested contributor.
If you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.
Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.
### 9. Adding pipelines, models, schedulers
Pipelines, models, and schedulers are the most important pieces of the Diffusers library.
They provide easy access to state-of-the-art diffusion technologies and thus allow the community to
build powerful generative AI applications.
By adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem.
Diffusers has a couple of open feature requests for all three components - feel free to gloss over them
if you don't know yet what specific component you would like to add:
-[Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)
Before adding any of the three components, it is strongly recommended that you give the [Philosophy guide](philosophy) a read to better understand the design of any of the three components. Please be aware that we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy
as it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please open a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us.
Please make sure to add links to the original codebase/paper to the PR and ideally also ping the original author directly on the PR so that they can follow the progress and potentially help with questions.
If you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help.
#### Copied from mechanism
A unique and important feature to understand when adding any pipeline, model or scheduler code is the `# Copied from` mechanism. You'll see this all over the Diffusers codebase, and the reason we use it is to keep the codebase easy to understand and maintain. Marking code with the `# Copied from` mechanism forces the marked code to be identical to the code it was copied from. This makes it easy to update and propagate changes across many files whenever you run `make fix-copies`.
For example, in the code example below, [`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is the original code and `AltDiffusionPipelineOutput` uses the `# Copied from` mechanism to copy it. The only difference is changing the class prefix from `Stable` to `Alt`.
```py
# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt
classAltDiffusionPipelineOutput(BaseOutput):
"""
Output class for Alt Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
```
To learn more, read this section of the [~Don't~ Repeat Yourself*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static) blog post.
## How to write a good issue
**The better your issue is written, the higher the chances that it will be quickly resolved.**
1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose).
2.**Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write "Error in diffusers".
3.**Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data.
4.**Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets.
5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better.
6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information.
7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library.
## How to write a good PR
1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged.
2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of "also fixing another problem while we're adding it". It is much more difficult to review pull requests that solve multiple, unrelated problems at once.
3. If helpful, try to add a code snippet that displays an example of how your addition can be used.
4. The title of your pull request should be a summary of its contribution.
5. If your pull request addresses an issue, please mention the issue number in
the pull request description to make sure they are linked (and people
consulting the issue know you are working on it);
6. To indicate a work in progress please prefix the title with `[WIP]`. These
are useful to avoid duplicated work, and to differentiate it from PRs ready
to be merged;
7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue).
8. Make sure existing tests pass;
9. Add high-coverage tests. No quality testing = no merge.
- If you are adding new `@slow` tests, make sure they pass using
CircleCI does not run the slow tests, but GitHub Actions does every night!
10. All public methods must have informative docstrings that work nicely with markdown. See [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) for an example.
11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files.
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
## How to open a PR
Before writing code, we strongly advise you to search through the existing PRs or
issues to make sure that nobody is already working on the same thing. If you are
unsure, it is always a good idea to open an issue to get some feedback.
You will need basic `git` proficiency to be able to contribute to
🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest
manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
Git](https://git-scm.com/book/en/v2) is a very good reference.
Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):
1. Fork the [repository](https://github.com/huggingface/diffusers) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
### Syncing forked main with upstream (HuggingFace) main
To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
when syncing the main branch of a forked repository, please, follow these steps:
1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
2. If a PR is absolutely necessary, use the following steps after checking out your branch:
```bash
$ git checkout -b your-branch-for-syncing
$ git pull --squash--no-commit upstream main
$ git commit -m'<your message without GitHub references>'
