split the new sampler into a different file

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