Loading modules/sd_samplers_timesteps.py +2 −3 Original line number Diff line number Diff line Loading @@ -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() Loading modules/sd_samplers_timesteps_impl.py +10 −8 Original line number Diff line number Diff line Loading @@ -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 Loading @@ -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() Loading Loading
modules/sd_samplers_timesteps.py +2 −3 Original line number Diff line number Diff line Loading @@ -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() Loading
modules/sd_samplers_timesteps_impl.py +10 −8 Original line number Diff line number Diff line Loading @@ -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 Loading @@ -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() Loading
