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@ -31,7 +31,7 @@ cd diffusers && pip install -e .
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. 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. Both models and schedulers should be load- and saveable from the Hub.
**Example for [DDPM](https://arxiv.org/abs/2006.11239):** #### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**
```python ```python
import torch import torch
@ -79,7 +79,7 @@ image_pil = PIL.Image.fromarray(image_processed[0])
image_pil.save("test.png") image_pil.save("test.png")
``` ```
**Example for [DDIM](https://arxiv.org/abs/2010.02502):** #### **Example for [DDIM](https://arxiv.org/abs/2010.02502):**
```python ```python
import torch import torch
@ -133,7 +133,7 @@ image_pil.save("test.png")
### 2. `diffusers` as a collection of most important Diffusion systems (GLIDE, Dalle, ...) ### 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: `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** #### **Example image generation with DDPM**
```python ```python
from diffusers import DiffusionPipeline from diffusers import DiffusionPipeline