# k-diffusion

**Repository Path**: wpjscc/k-diffusion

## Basic Information

- **Project Name**: k-diffusion
- **Description**: 11222222222
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-09-28
- **Last Updated**: 2022-09-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# k-diffusion

An implementation of [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) (Karras et al., 2022) for PyTorch. The patching method in [Improving Diffusion Model Efficiency Through Patching](https://arxiv.org/abs/2207.04316) is implemented as well.

## Training:

To train models:

```sh
$ ./train.py --config CONFIG_FILE --name RUN_NAME
```

For instance, to train a model on MNIST:

```sh
$ ./train.py --config configs/config_mnist.json --name RUN_NAME
```

The configuration file allows you to specify the dataset type. Currently supported types are `"imagefolder"` (a folder with one subfolder per image class, the classes are currently ignored), `"cifar10"` (CIFAR-10), and `"mnist"` (MNIST).

Multi-GPU and multi-node training is supported with [Hugging Face Accelerate](https://huggingface.co/docs/accelerate/index). You can configure Accelerate by running:

```sh
$ accelerate config
```

on all nodes, then running:

```sh
$ accelerate launch train.py --config CONFIG_FILE --name RUN_NAME
```

on all nodes.

## Enhancements/additional features:

- k-diffusion models support progressive growing.

- k-diffusion implements a sampler inspired by [DPM-Solver](https://arxiv.org/abs/2206.00927) and Karras et al. (2022) Algorithm 2 that produces higher quality samples at the same number of function evalutions as Karras Algorithm 2. It also implements a [linear multistep](https://en.wikipedia.org/wiki/Linear_multistep_method#Adams–Bashforth_methods) sampler (comparable to [PLMS](https://arxiv.org/abs/2202.09778)).

- k-diffusion supports [CLIP](https://openai.com/blog/clip/) guided sampling from unconditional diffusion models (see `sample_clip_guided.py`).

- k-diffusion has wrappers for [v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch), [OpenAI diffusion](https://github.com/openai/guided-diffusion), and [CompVis diffusion](https://github.com/CompVis/latent-diffusion) models allowing them to be used with its samplers and ODE/SDE.

- k-diffusion supports log likelihood calculation (not a variational lower bound) for native models and all wrapped models.

## To do:

- Anything except unconditional image diffusion models

- Latent diffusion