| import torch |
| import tqdm |
| import k_diffusion.sampling |
| from modules import sd_samplers_common, sd_samplers_kdiffusion, sd_samplers |
| from tqdm.auto import trange, tqdm |
| from k_diffusion import utils |
| from k_diffusion.sampling import to_d |
| import math |
|
|
|
|
| NAME = 'Euler_Dy' |
| ALIAS = 'euler_dy' |
|
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|
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|
|
| @torch.no_grad() |
| def dy_sampling_step(x, model, dt, sigma_hat, **extra_args): |
|
|
| original_shape = x.shape |
| batch_size, m, n = original_shape[0], original_shape[2] // 2, original_shape[3] // 2 |
| extra_row = x.shape[2] % 2 == 1 |
| extra_col = x.shape[3] % 2 == 1 |
|
|
| if extra_row: |
| extra_row_content = x[:, :, -1:, :] |
| x = x[:, :, :-1, :] |
| if extra_col: |
| extra_col_content = x[:, :, :, -1:] |
| x = x[:, :, :, :-1] |
|
|
| a_list = x.unfold(2, 2, 2).unfold(3, 2, 2).contiguous().view(batch_size, 4, m * n, 2, 2) |
| c = a_list[:, :, :, 1, 1].view(batch_size, 4, m, n) |
|
|
| denoised = model(c, sigma_hat * c.new_ones([c.shape[0]]), **extra_args) |
| d = to_d(c, sigma_hat, denoised) |
| c = c + d * dt |
|
|
| d_list = c.view(batch_size, 4, m * n, 1, 1) |
| a_list[:, :, :, 1, 1] = d_list[:, :, :, 0, 0] |
| x = a_list.view(batch_size, 4, m, n, 2, 2).permute(0, 1, 2, 4, 3, 5).reshape(batch_size, 4, 2 * m, 2 * n) |
|
|
| if extra_row or extra_col: |
| x_expanded = torch.zeros(original_shape, dtype=x.dtype, device=x.device) |
| x_expanded[:, :, :2 * m, :2 * n] = x |
| if extra_row: |
| x_expanded[:, :, -1:, :2 * n + 1] = extra_row_content |
| if extra_col: |
| x_expanded[:, :, :2 * m, -1:] = extra_col_content |
| if extra_row and extra_col: |
| x_expanded[:, :, -1:, -1:] = extra_col_content[:, :, -1:, :] |
| x = x_expanded |
|
|
| return x |
|
|
|
|
| @torch.no_grad() |
| def sample_euler_dy(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., |
| s_tmax=float('inf'), s_noise=1.): |
| extra_args = {} if extra_args is None else extra_args |
| s_in = x.new_ones([x.shape[0]]) |
| for i in trange(len(sigmas) - 1, disable=disable): |
| |
| |
| gamma = max(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. |
| eps = torch.randn_like(x) * s_noise |
| sigma_hat = sigmas[i] * (gamma + 1) |
| |
| dt = sigmas[i + 1] - sigma_hat |
| if gamma > 0: |
| x = x - eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 |
| denoised = model(x, sigma_hat * s_in, **extra_args) |
| d = to_d(x, sigma_hat, denoised) |
| if sigmas[i + 1] > 0: |
| if i // 2 == 1: |
| x = dy_sampling_step(x, model, dt, sigma_hat, **extra_args) |
| if callback is not None: |
| callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) |
| |
| x = x + d * dt |
| return x |
|
|
|
|
|
|
| if not NAME in [x.name for x in sd_samplers.all_samplers]: |
| euler_max_samplers = [(NAME, sample_euler_dy, [ALIAS], {})] |
| samplers_data_euler_max_samplers = [ |
| sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: sd_samplers_kdiffusion.KDiffusionSampler(funcname, model), aliases, options) |
| for label, funcname, aliases, options in euler_max_samplers |
| if callable(funcname) or hasattr(k_diffusion.sampling, funcname) |
| ] |
| sd_samplers.all_samplers += samplers_data_euler_max_samplers |
| sd_samplers.all_samplers_map = {x.name: x for x in sd_samplers.all_samplers} |
|
|
