diff options
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 32 |
1 files changed, 12 insertions, 20 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index ed5e6c79..c72d01c8 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -35,14 +35,15 @@ samplers_k_diffusion = [ @torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
+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
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
+ from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras
def heun_step(x, old_sigma, new_sigma, second_order = True):
nonlocal step_id
denoised = model(x, old_sigma * s_in, **extra_args)
@@ -62,24 +63,15 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No x = x + d_prime * dt
step_id += 1
return x
- 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))
- max_inv_rho = (sigma_max ** (1 / rho))
- if isinstance(min_inv_rho, torch.Tensor):
- min_inv_rho = min_inv_rho.to(device)
- if isinstance(max_inv_rho, torch.Tensor):
- 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()
+ if restart_list is None:
+ 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].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
@@ -91,7 +83,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No min_idx = i + 1
max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
if max_idx < min_idx:
- sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
+ sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end
while restart_times > 0:
restart_times -= 1
x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
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