# Diffusers

## Definitions

**Models**: Single neural network that models p_θ(x_t-1|x_t) and is trained to “denoise” to image
*Examples: UNet, Conditioned UNet, 3D UNet, Transformer UNet*

![model_diff_1_50](https://user-images.githubusercontent.com/23423619/171610307-dab0cd8b-75da-4d4e-9f5a-5922072e2bb5.png)

**Samplers**: Algorithm to *train* and *sample* from **Model**. Defines alpha and beta schedule, timesteps, etc..
*Example: Vanilla DDPM, DDIM, PMLS, DEIN*

![sampling](https://user-images.githubusercontent.com/23423619/171608981-3ad05953-a684-4c82-89f8-62a459147a07.png)
![training](https://user-images.githubusercontent.com/23423619/171608964-b3260cce-e6b4-4841-959d-7d8ba4b8d1b2.png)

**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, CLIP
*Example: GLIDE,CompVis/Latent-Diffusion, Imagen, DALL-E*

![imagen](https://user-images.githubusercontent.com/23423619/171609001-c3f2c1c9-f597-4a16-9843-749bf3f9431c.png)

## 1. `diffusers` as a central modular diffusion and sampler library

`diffusers` should be more modularized than `transformers` so that parts of it can be easily used in other libraries.
It could become a central place for all kinds of models, samplers, training utils and processors required when using diffusion models in audio, vision, ... 
One should be able to save both models and samplers as well as load them from the Hub.

Example:

```python
from diffusers import UNetModel, GaussianDDPMScheduler
import torch

# 1. Load model
unet = UNetModel.from_pretrained("fusing/ddpm_dummy")

# 2. Do one denoising step with model
batch_size, num_channels, height, width = 1, 3, 32, 32
dummy_noise = torch.ones((batch_size, num_channels, height, width))
time_step = torch.tensor([10])
image = unet(dummy_noise, time_step)

# 3. Load sampler
sampler = GaussianDDPMScheduler.from_config("fusing/ddpm_dummy")

# 4. Sample image from sampler passing the model
image = sampler.sample(model, batch_size=1)

print(image)
```

## 2. `diffusers` as a collection of most import Diffusion models (GLIDE, Dalle, ...)
`models` directory in repository hosts complete diffusion training code & pipelines. Easily load & saveable from the Hub. Will be possible to use just from pip `diffusers` version:

Example:

```python
from diffusers import UNetModel, GaussianDDPMScheduler
from modeling_ddpm import DDPM
import tempfile

unet = UNetModel.from_pretrained("fusing/ddpm_dummy")
sampler = GaussianDDPMScheduler.from_config("fusing/ddpm_dummy")

# compose Diffusion Pipeline
ddpm = DDPM(unet, sampler)
# generate / sample
image = ddpm()
print(image)


# save and load with 0 extra code (handled by general `DiffusionPipeline` class)
# will also be possible to do so from the Hub
with tempfile.TemporaryDirectory() as tmpdirname:
    ddpm.save_pretrained(tmpdirname)
    print("Model saved")
    ddpm_new = DDPM.from_pretrained(tmpdirname)
    print("Model loaded")
    print(ddpm_new)
```

## Library structure:

```
├── models
│   ├── audio
│   │   └── fastdiff
│   │       ├── modeling_fastdiff.py
│   │       ├── README.md
│   │       └── run_fastdiff.py
│   └── vision
│       ├── dalle2
│       │   ├── modeling_dalle2.py
│       │   ├── README.md
│       │   └── run_dalle2.py
│       ├── ddpm
│       │   ├── modeling_ddpm.py
│       │   ├── README.md
│       │   └── run_ddpm.py
│       ├── glide
│       │   ├── modeling_glide.py
│       │   ├── README.md
│       │   └── run_dalle2.py
│       ├── imagen
│       │   ├── modeling_dalle2.py
│       │   ├── README.md
│       │   └── run_dalle2.py
│       └── latent_diffusion
│           ├── modeling_latent_diffusion.py
│           ├── README.md
│           └── run_latent_diffusion.py

├── src
│   └── diffusers
│       ├── configuration_utils.py
│       ├── __init__.py
│       ├── modeling_utils.py
│       ├── models
│       │   └── unet.py
│       ├── processors
│       └── samplers
│           ├── gaussian.py
├── tests
│   └── test_modeling_utils.py
```