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## 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, schedulers, 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.
`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 scredulers should be load- and saveable from the Hub.
Example:
@ -78,8 +78,8 @@ image_pil = PIL.Image.fromarray(image_processed[0])
image_pil.save("test.png")
```
## 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:
## 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: