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https://github.com/invoke-ai/InvokeAI
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added customized patches and updated the README
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@ -255,7 +255,7 @@ class DDPM(pl.LightningModule):
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b = shape[0]
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img = torch.randn(shape, device=device)
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intermediates = [img]
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for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
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for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps, dynamic_ncols=True):
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img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
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clip_denoised=self.clip_denoised)
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if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
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