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Patrick von Platen
2022-06-15 12:25:48 +02:00
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@@ -58,12 +58,14 @@ git clone https://github.com/huggingface/diffusers.git
cd diffusers && pip install -e .
```
### 1. `diffusers` as a central modular diffusion and sampler library
### 1. `diffusers` as a toolbox for schedulers and models.
`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.
For more examples see [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) and [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)
#### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**
```python
@@ -171,25 +173,35 @@ image_pil = PIL.Image.fromarray(image_processed[0])
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:
### 2. `diffusers` as a collection of popula Diffusion systems (GLIDE, Dalle, ...)
#### **Example image generation with DDPM**
For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
#### **Example image generation with PNDM**
```python
from diffusers import DiffusionPipeline
from diffusers import PNDM, UNetModel, PNDMScheduler
import PIL.Image
import numpy as np
import torch
model_id = "fusing/ddim-celeba-hq"
model = UNetModel.from_pretrained(model_id)
scheduler = PNDMScheduler()
# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom")
ddpm = PNDM(unet=model, noise_scheduler=scheduler)
# run pipeline in inference (sample random noise and denoise)
image = ddpm()
with torch.no_grad():
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 + 1.0) / 2
image_processed = torch.clamp(image_processed, 0.0, 1.0)
image_processed = image_processed * 255
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])