README.md

# 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)

**Schedulers**: Algorithm to compute previous image according to alpha, beta schedule and to sample noise. Should be used for both *training* and *inference*.
*Example: Gaussian 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)

## Quickstart

```
git clone https://github.com/huggingface/diffusers.git
cd diffusers && pip install -e .
```

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

`diffusers` is more modularized than `transformers`. The idea is that researchers and engineers can use only parts of the library easily for the own use cases.
It could become a central place for all kinds of models, schedulers, training utils and processors that one can mix and match for one's own use case.
Both models and schedulers should be load- and saveable from the Hub.

#### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**

```python
import torch
from diffusers import UNetModel, DDPMScheduler
import PIL
import numpy as np
import tqdm

generator = torch.manual_seed(0)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Load models
noise_scheduler = DDPMScheduler.from_config("fusing/ddpm-lsun-church", tensor_format="pt")
unet = UNetModel.from_pretrained("fusing/ddpm-lsun-church").to(torch_device)

# 2. Sample gaussian noise
image = torch.randn(
	(1, unet.in_channels, unet.resolution, unet.resolution),
	generator=generator,
)
image = image.to(torch_device)

# 3. Denoise
num_prediction_steps = len(noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
	# predict noise residual
	with torch.no_grad():
		residual = unet(image, t)

	# predict previous mean of image x_t-1
	pred_prev_image = noise_scheduler.step(residual, image, t)

	# optionally sample variance
	variance = 0
	if t > 0:
		noise = torch.randn(image.shape, generator=generator).to(image.device)
		variance = noise_scheduler.get_variance(t).sqrt() * noise

	# set current image to prev_image: x_t -> x_t-1
	image = pred_prev_image + variance

# 5. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# 6. save image
image_pil.save("test.png")
```

#### **Example for [DDIM](https://arxiv.org/abs/2010.02502):**

```python
import torch
from diffusers import UNetModel, DDIMScheduler
import PIL
import numpy as np
import tqdm

generator = torch.manual_seed(0)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Load models
noise_scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)

# 2. Sample gaussian noise
image = torch.randn(
	(1, unet.in_channels, unet.resolution, unet.resolution),
	generator=generator,
)
image = image.to(torch_device)

# 3. Denoise                                                                                                                                           
num_inference_steps = 50
eta = 0.0  # <- deterministic sampling

for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
	# 1. predict noise residual
	orig_t = noise_scheduler.get_orig_t(t, num_inference_steps)
	with torch.no_grad():
	    residual = unet(image, orig_t)

	# 2. predict previous mean of image x_t-1
	pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)

	# 3. optionally sample variance
	variance = 0
	if eta > 0:
		noise = torch.randn(image.shape, generator=generator).to(image.device)
		variance = noise_scheduler.get_variance(t).sqrt() * eta * noise

	# 4. set current image to prev_image: x_t -> x_t-1
	image = pred_prev_image + variance

# 5. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# 6. save image
image_pil.save("test.png")
```

### 2. `diffusers` as a collection of most important Diffusion systems (GLIDE, Dalle, ...)
`models` directory in repository hosts the complete code necessary for running a diffusion system as well as to train it. A `DiffusionPipeline` class allows to easily run the diffusion model in inference:

#### **Example image generation with DDPM**

```python
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np

# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom")

# run pipeline in inference (sample random noise and denoise)
image = ddpm()

# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# save image
image_pil.save("test.png")
```

**Text to Image generation with Latent Diffusion**

```python
from diffusers import DiffusionPipeline

ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")

generator = torch.Generator()
generator = generator.manual_seed(6694729458485568)

prompt = "A painting of a squirrel eating a burger"
image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)

image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = image_processed  * 255.
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# save image
image_pil.save("test.png")
```

## Library structure:

```
├── models
│   ├── audio
│   │   └── fastdiff
│   │       ├── modeling_fastdiff.py
│   │       ├── README.md
│   │       └── run_fastdiff.py
│   ├── __init__.py
│   └── vision
│       ├── dalle2
│       │   ├── modeling_dalle2.py
│       │   ├── README.md
│       │   └── run_dalle2.py
│       ├── ddpm
│       │   ├── example.py
│       │   ├── modeling_ddpm.py
│       │   ├── README.md
│       │   └── run_ddpm.py
│       ├── glide
│       │   ├── modeling_glide.py
│       │   ├── modeling_vqvae.py.py
│       │   ├── README.md
│       │   └── run_glide.py
│       ├── imagen
│       │   ├── modeling_dalle2.py
│       │   ├── README.md
│       │   └── run_dalle2.py
│       ├── __init__.py
│       └── latent_diffusion
│           ├── modeling_latent_diffusion.py
│           ├── README.md
│           └── run_latent_diffusion.py
├── pyproject.toml
├── README.md
├── setup.cfg
├── setup.py
├── src
│   └── diffusers
│       ├── configuration_utils.py
│       ├── __init__.py
│       ├── modeling_utils.py
│       ├── models
│       │   ├── __init__.py
│       │   ├── unet_glide.py
│       │   └── unet.py
│       ├── pipeline_utils.py
│       └── schedulers
│           ├── gaussian_ddpm.py
│           ├── __init__.py
├── tests
│   └── test_modeling_utils.py
```