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wip: update ldm_patched

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currently issues with calculate_sigmas call
This commit is contained in:
Manuel Schmid
2024-06-02 20:52:52 +02:00
parent ab01104d42
commit 3881fa0806
84 changed files with 5380 additions and 1923 deletions
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ldm_patched/ldm/modules/diffusionmodules/util.py
+14 -12
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@@ -46,23 +46,25 @@ class AlphaBlender(nn.Module):
else:
raise ValueError(f"unknown merge strategy {self.merge_strategy}")
def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:
def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor:
# skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
if self.merge_strategy == "fixed":
# make shape compatible
# alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs)
alpha = self.mix_factor.to(image_only_indicator.device)
alpha = self.mix_factor.to(device)
elif self.merge_strategy == "learned":
alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
alpha = torch.sigmoid(self.mix_factor.to(device))
# make shape compatible
# alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
elif self.merge_strategy == "learned_with_images":
assert image_only_indicator is not None, "need image_only_indicator ..."
alpha = torch.where(
image_only_indicator.bool(),
torch.ones(1, 1, device=image_only_indicator.device),
rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
)
if image_only_indicator is None:
alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1")
else:
alpha = torch.where(
image_only_indicator.bool(),
torch.ones(1, 1, device=image_only_indicator.device),
rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
)
alpha = rearrange(alpha, self.rearrange_pattern)
# make shape compatible
# alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
@@ -76,7 +78,7 @@ class AlphaBlender(nn.Module):
x_temporal,
image_only_indicator=None,
) -> torch.Tensor:
alpha = self.get_alpha(image_only_indicator)
alpha = self.get_alpha(image_only_indicator, x_spatial.device)
x = (
alpha.to(x_spatial.dtype) * x_spatial
+ (1.0 - alpha).to(x_spatial.dtype) * x_temporal
@@ -98,7 +100,7 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
betas = torch.clamp(betas, min=0, max=0.999)
elif schedule == "squaredcos_cap_v2": # used for karlo prior
# return early
@@ -113,7 +115,7 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
return betas
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):