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445 fix controlnet tutorial #448
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added 2 commits
December 13, 2023 16:23
…olNetDiffusionInferer to clean up the code. Ran the notebook.
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ericspod
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Dec 15, 2023
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| # Without using an inferer: | ||
| # progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110) | ||
| # progress_bar_sampling.set_description("sampling...") | ||
| # sample = torch.randn((1, 1, 64, 64)).to(device) | ||
| # for t in progress_bar_sampling: | ||
| # with torch.no_grad(): | ||
| # with autocast(enabled=True): | ||
| # down_block_res_samples, mid_block_res_sample = controlnet( | ||
| # x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...] | ||
| # ) | ||
| # noise_pred = model( | ||
| # sample, | ||
| # timesteps=torch.Tensor((t,)).to(device), | ||
| # down_block_additional_residuals=down_block_res_samples, | ||
| # mid_block_additional_residual=mid_block_res_sample, | ||
| # ) | ||
| # sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample) |
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Do we want to leave this here or just point people to the definition of the inferer's sample method?
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I was just leaving them there in case a user did not want to use the inferer, as it was previously like that
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I think its OK to leave it there, it might help users get a bit more insight into what the inferer is actually doing under the hood
marksgraham
approved these changes
Dec 15, 2023
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I fixed some typos in the Controlnet tutorial, and included new ControlNetDiffusionInferer functionality to make the code cleaner. The notebook's been ran again to account for modifications.
In response to: #443