Merge pull request #11850 from lambertae/restart_sampling

Restart sampling

1 files changed, 71 insertions, 2 deletions

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 5552a8dc..a54673eb 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -30,12 +30,81 @@ 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}),

 ]

 

+

+@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)

+    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')

 ]

 

 sampler_extra_params = {

@@ -270,7 +339,7 @@ class KDiffusionSampler:
 

         self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)

         self.funcname = funcname

-        self.func = getattr(k_diffusion.sampling, self.funcname)

+        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler

         self.extra_params = sampler_extra_params.get(funcname, [])

         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

         self.sampler_noises = None