restart-sampler with correct steps

1 files changed, 14 insertions, 8 deletions

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index db7013f2..ed5e6c79 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -38,20 +38,19 @@ samplers_k_diffusion = [
 def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):

     """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""

     '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''

-    restart_list = {0.1: [10, 2, 2]}

     from tqdm.auto import trange

     extra_args = {} if extra_args is None else extra_args

     s_in = x.new_ones([x.shape[0]])

     step_id = 0

     from k_diffusion.sampling import to_d, append_zero

-    def heun_step(x, old_sigma, new_sigma):

+    def heun_step(x, old_sigma, new_sigma, second_order = True):

         nonlocal step_id

         denoised = model(x, old_sigma * s_in, **extra_args)

         d = to_d(x, old_sigma, denoised)

         if callback is not None:

             callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})

         dt = new_sigma - old_sigma

-        if new_sigma == 0:

+        if new_sigma == 0 or not second_order:

             # Euler method

             x = x + d * dt

         else:

@@ -63,11 +62,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt

         step_id += 1

         return x

-    # print(sigmas)

-    temp_list = dict()

-    for key, value in restart_list.items():

-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value

-    restart_list = temp_list

     def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):

         ramp = torch.linspace(0, 1, n).to(device)

         min_inv_rho = (sigma_min ** (1 / rho))

@@ -78,6 +72,18 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             max_inv_rho = max_inv_rho.to(device)

         sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho

         return append_zero(sigmas).to(device)

+    steps = sigmas.shape[0] - 1

+    if steps >= 20:

+        restart_steps = 9

+        restart_times = 2 if steps >= 36 else 1

+        sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)

+        restart_list = {0.1: [restart_steps + 1, restart_times, 2]}

+    else:

+        restart_list = dict()

+    temp_list = dict()

+    for key, value in restart_list.items():

+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value

+    restart_list = temp_list

     for i in trange(len(sigmas) - 1, disable=disable):

         x = heun_step(x, sigmas[i], sigmas[i+1])

         if i + 1 in restart_list: