some stylistic changes for the sampler code

1 files changed, 22 insertions, 18 deletions

diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py
index a1b5dab3..1b981ca8 100644
--- a/modules/sd_samplers_extra.py
+++ b/modules/sd_samplers_extra.py
@@ -1,17 +1,20 @@
 import torch

+import tqdm

 import k_diffusion.sampling

 

+

 @torch.no_grad()

-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):

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

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

-    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''

-    from tqdm.auto import trange

+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):

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

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

+    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list

+    """

     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, get_sigmas_karras

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

+

+    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)

@@ -30,6 +33,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt

         step_id += 1

         return x

+

     steps = sigmas.shape[0] - 1

     if restart_list is None:

         if steps >= 20:

@@ -41,11 +45,10 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), 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

+            restart_list = {}

+

+    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}

+

     step_list = []

     for i in range(len(sigmas) - 1):

         step_list.append((sigmas[i], sigmas[i + 1]))

@@ -58,13 +61,14 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                 while restart_times > 0:

                     restart_times -= 1

                     step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])

+

     last_sigma = None

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

+    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):

         if last_sigma is None:

-            last_sigma = step_list[i][0]

-        elif last_sigma < step_list[i][0]:

-            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5

-        x = heun_step(x, step_list[i][0], step_list[i][1])

-        last_sigma = step_list[i][1]

-    return x

+            last_sigma = old_sigma

+        elif last_sigma < old_sigma:

+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5

+        x = heun_step(x, old_sigma, new_sigma)

+        last_sigma = new_sigma

 

+    return x