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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Fast Diffusion Models with Transformers
| Date | Stars |
|---|---|
| 2026-07-31 | 954 |
| 2026-08-02 | 954 |
| 2026-08-06 | 954 |
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## Scalable Diffusion Models with Transformers (DiT)<br><sub>Improved PyTorch Implementation</sub> ### [Paper](http://arxiv.org/abs/2212.09748) | [Project Page](https://www.wpeebles.com/DiT) | Run DiT-XL/2 [](https://huggingface.co/spaces/wpeebles/DiT) [](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb) <a href="https://replicate.com/arielreplicate/scalable_diffusion_with_transformers"><img src="https://replicate.com/arielreplicate/scalable_diffusion_with_transformers/badge"></a>  This repo features an improved PyTorch implementation for the paper [**Scalable Diffusion Models with Transformers**](https://www.wpeebles.com/DiT). It contains: * 🪐 An improved PyTorch [implementation](models.py) and the original [implementation](train_options/models_original.py) of DiT * ⚡️ Pre-trained class-conditional DiT models trained on ImageNet (512x512 and 256x256) * 💥 A self-contained [Hugging Face Space](https://huggingface.co/spaces/wpeebles/DiT) and [Colab notebook](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb) for running pre-trained DiT-XL/2 models * 🛸 An improved DiT [training script](train.py) and several [training options](train_options) ## Setup First, download and set up the repo: ```bash git clone https://github.com/chuanyangjin/fast-DiT.git cd DiT ``` We provide an [`environment.yml`](environment.yml) file that can be used to create a Conda environment. If you only want to run pre-trained models locally on CPU, you can remove the `cudatoolkit` and `pytorch-cuda` requirements from the file. ```bash conda env create -f environment.yml conda activate DiT ``` ## Sampling [](https://huggingface.co/spaces/wpeebles/DiT) [](http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb)  **Pre-trained DiT checkpoints.** You can sample from our pre-trained DiT models with [`sample.py`](sample.py). Weights for our pre-trained DiT model will be automatically downloaded depending on the model you use. The script has various arguments to switch between the 256x256 and 512x512 models, adjust sampling steps, change the classifier-free guidance scale, etc. For example, to sample from our 512x512 DiT-XL/2 model, you can use: ```bash python sample.py --image-size 512 --seed 1 ``` For convenience, our pre-trained DiT models can be downloaded directly here as well: | DiT Model | Image Resolution | FID-50K | Inception Score | Gflops | |---------------|------------------|---------|-----------------|--------| | [XL/2](https://dl.fbaipublicfiles.com/DiT/models/DiT-XL-2-256x256.pt) | 256x256 | 2.27 | 278.24 | 119 | | [XL/2](https://dl.fbaipublicfiles.com/DiT/models/DiT-XL-2-512x512.pt) | 512x512 | 3.04 | 240.82 | 525 | **Custom DiT checkpoints.** If you've trained a new DiT model with [`train.py`](train.py) (see [below](#training-dit)), you can add the `--ckpt` argument to use your own checkpoint instead. For example, to sample from the EMA weights of a custom 256x256 DiT-L/4 model, run: ```bash python sample.py --model DiT-L/4 --image-size 256 --ckpt /path/to/model.pt ``` ## Training ### Preparation Before Training To extract ImageNet features with `N` GPUs on one node: ```bash torchrun --nnodes=1 --nproc_per_node=N extract_features.py --model DiT-XL/2 --data-path /path/to/imagenet/train --features-path /path/to/store/features --global-batch-size=256 ``` ### Training DiT We provide a training script for DiT in [`train.py`](train.py). This script can be used to
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Jason Ren · Ai2/UW · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e1b87b23afc7b6dd, llm:Repository description: "Fast Diffusion Models with Transformers" (implies diffusion models and transformer architectures for image generation/acceleration).
matched fp:e1b87b23afc7b6dd, llm:Repository description: "Fast Diffusion Models with Transformers" (implies diffusion models and transformer architectures for image generation/acceleration).
matched fp:e1b87b23afc7b6dd, llm:Repository description: "Fast Diffusion Models with Transformers" (implies diffusion models and transformer architectures for image generation/acceleration).