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feat: add tcd sampler and discrete distilled tcd scheduler based on sgm_uniform (same as lcm) (#2907)

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Manuel Schmid
2024-05-19 13:08:33 +02:00
committed by GitHub
parent 3bae73e23e
commit 2e2e8f851a
8 changed files with 65 additions and 13 deletions
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ldm_patched/modules/model_sampling.py
+4 -4
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@@ -50,17 +50,17 @@ class ModelSamplingDiscrete(torch.nn.Module):
self.linear_start = linear_start
self.linear_end = linear_end
# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
self.set_sigmas(sigmas)
self.set_alphas_cumprod(alphas_cumprod.float())
def set_sigmas(self, sigmas):
self.register_buffer('sigmas', sigmas)
self.register_buffer('log_sigmas', sigmas.log())
def set_alphas_cumprod(self, alphas_cumprod):
self.register_buffer("alphas_cumprod", alphas_cumprod.float())
@property
def sigma_min(self):
return self.sigmas[0]