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repair DDIM/PLMS/UniPC batches

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AUTOMATIC1111 2023-08-14 08:49:02 +03:00
parent 007ecfbb29
commit aeb76ef174
2 changed files with 12 additions and 11 deletions
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5
modules/sd_samplers_timesteps.py
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@ -51,10 +51,9 @@ class CFGDenoiserTimesteps(CFGDenoiser):
self.alphas = shared.sd_model.alphas_cumprod
def get_pred_x0(self, x_in, x_out, sigma):
ts = int(sigma.item())
ts = sigma.to(dtype=int)
s_in = x_in.new_ones([x_in.shape[0]])
a_t = self.alphas[ts].item() * s_in
a_t = self.alphas[ts][:, None, None, None]
sqrt_one_minus_at = (1 - a_t).sqrt()
pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()

18
modules/sd_samplers_timesteps_impl.py
View File

@ -16,16 +16,17 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
s_in = x.new_ones((x.shape[0]))
s_x = x.new_ones((x.shape[0], 1, 1, 1))
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
index = len(timesteps) - 1 - i
e_t = model(x, timesteps[index].item() * s_in, **extra_args)
a_t = alphas[index].item() * s_in
a_prev = alphas_prev[index].item() * s_in
sigma_t = sigmas[index].item() * s_in
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
a_t = alphas[index].item() * s_x
a_prev = alphas_prev[index].item() * s_x
sigma_t = sigmas[index].item() * s_x
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
@ -47,13 +48,14 @@ def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
s_x = x.new_ones((x.shape[0], 1, 1, 1))
old_eps = []
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
a_t = alphas[index].item() * s_in
a_prev = alphas_prev[index].item() * s_in
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
a_t = alphas[index].item() * s_x
a_prev = alphas_prev[index].item() * s_x
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()