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Fix missing punctuation in PHILOSOPHY.md #5530

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Oct 25, 2023
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Fix missing punctuation in PHILOSOPHY.md #5530

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4 changes: 2 additions & 2 deletions PHILOSOPHY.md
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Expand Up @@ -70,7 +70,7 @@ The following design principles are followed:
- Pipelines should be used **only** for inference.
- Pipelines should be very readable, self-explanatory, and easy to tweak.
- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner)
- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
- Pipelines should be named after the task they are intended to solve.
- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
Expand Down Expand Up @@ -104,7 +104,7 @@ The following design principles are followed:
- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon
- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
- In almost all cases, novel schedulers shall be implemented in a new scheduling file.
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