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pcuenca
commented
Nov 21, 2022
pcuenca
commented
Nov 21, 2022
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| i = 0 | ||
| t = self.scheduler.timesteps[0] | ||
| while t > 0: |
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We need to keep i and t, which is very ugly. i is only required for the callback.
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| if not return_dict: | ||
| return (prev_sample, self.timesteps[step_index]) | ||
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| return SchedulerOutput(prev_sample=prev_sample, timestep=self.timesteps[step_index]) |
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Similar implementation as in #1336 except that we return the next timestep.
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. |
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| # compute the previous noisy sample x_t -> x_t-1 | ||
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | ||
| latents, t = self.scheduler.step(noise_pred, t, latents, return_dict=False, **extra_step_kwargs) |
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Hmm this makes the step function a bit more black-box - not suuuper happy about it at this point of the library, but maybe at some point we have to let the scheduler adapt t anyways. However would still be happier with exposing the whole timestamp array @patil-suraj @anton-l
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Without duplicating the sigmas and timesteps (reference: #1336)
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No duplication of sigmas and timesteps.
Does not require ugly function
index_for_timestep(or confusing oneliner repeated twice in the code)➖
Requires modification of all pipelines.
The pipeline loop is uglier.
All in all I don't think we gain any more clarity than in #1336, so I recommend we adopt #1336 unless it's important that the timesteps and sigmas are not duplicated.
This implementation does work. I'll push a fix for #1336 so it works too.