diff options
Diffstat (limited to 'modules/sd_samplers_kdiffusion.py')
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 75 |
1 files changed, 4 insertions, 71 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index a54673eb..e0da3425 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -2,7 +2,7 @@ from collections import deque import torch
import inspect
import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
from modules.shared import opts, state
import modules.shared as shared
@@ -30,81 +30,14 @@ samplers_k_diffusion = [ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
- ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
+ ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
]
-@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
- 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):
- 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 or not second_order:
- # Euler method
- x = x + d * dt
- else:
- # Heun's method
- x_2 = x + d * dt
- denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
- d_2 = to_d(x_2, new_sigma, denoised_2)
- d_prime = (d + d_2) / 2
- x = x + d_prime * dt
- step_id += 1
- return x
- steps = sigmas.shape[0] - 1
- if restart_list is None:
- if steps >= 20:
- restart_steps = 9
- restart_times = 1
- if steps >= 36:
- restart_steps = steps // 4
- restart_times = 2
- 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
- step_list = []
- for i in range(len(sigmas) - 1):
- step_list.append((sigmas[i], sigmas[i + 1]))
- if i + 1 in restart_list:
- restart_steps, restart_times, restart_max = restart_list[i + 1]
- 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].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
- 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):
- 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
-
samplers_data_k_diffusion = [
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
for label, funcname, aliases, options in samplers_k_diffusion
- if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
+ if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
]
sampler_extra_params = {
@@ -339,7 +272,7 @@ class KDiffusionSampler: self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
- self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
+ self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
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