Merge branch 'AUTOMATIC1111:master' into master

1 files changed, 137 insertions, 0 deletions

diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
new file mode 100644
index 00000000..a72daafd
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+++ b/modules/sd_samplers_timesteps_impl.py
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+import torch

+import tqdm

+import k_diffusion.sampling

+import numpy as np

+

+from modules import shared

+from modules.models.diffusion.uni_pc import uni_pc

+

+

+@torch.no_grad()

+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):

+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod

+    alphas = alphas_cumprod[timesteps]

+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)

+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)

+    sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))

+

+    extra_args = {} if extra_args is None else extra_args

+    s_in = x.new_ones((x.shape[0]))

+    s_x = x.new_ones((x.shape[0], 1, 1, 1))

+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):

+        index = len(timesteps) - 1 - i

+

+        e_t = model(x, timesteps[index].item() * s_in, **extra_args)

+

+        a_t = alphas[index].item() * s_x

+        a_prev = alphas_prev[index].item() * s_x

+        sigma_t = sigmas[index].item() * s_x

+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x

+

+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()

+        dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t

+        noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)

+        x = a_prev.sqrt() * pred_x0 + dir_xt + noise

+

+        if callback is not None:

+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})

+

+    return x

+

+

+@torch.no_grad()

+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):

+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod

+    alphas = alphas_cumprod[timesteps]

+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)

+    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)

+

+    extra_args = {} if extra_args is None else extra_args

+    s_in = x.new_ones([x.shape[0]])

+    s_x = x.new_ones((x.shape[0], 1, 1, 1))

+    old_eps = []

+

+    def get_x_prev_and_pred_x0(e_t, index):

+        # select parameters corresponding to the currently considered timestep

+        a_t = alphas[index].item() * s_x

+        a_prev = alphas_prev[index].item() * s_x

+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x

+

+        # current prediction for x_0

+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()

+

+        # direction pointing to x_t

+        dir_xt = (1. - a_prev).sqrt() * e_t

+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt

+        return x_prev, pred_x0

+

+    for i in tqdm.trange(len(timesteps) - 1, disable=disable):

+        index = len(timesteps) - 1 - i

+        ts = timesteps[index].item() * s_in

+        t_next = timesteps[max(index - 1, 0)].item() * s_in

+

+        e_t = model(x, ts, **extra_args)

+

+        if len(old_eps) == 0:

+            # Pseudo Improved Euler (2nd order)

+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)

+            e_t_next = model(x_prev, t_next, **extra_args)

+            e_t_prime = (e_t + e_t_next) / 2

+        elif len(old_eps) == 1:

+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)

+            e_t_prime = (3 * e_t - old_eps[-1]) / 2

+        elif len(old_eps) == 2:

+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)

+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12

+        else:

+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)

+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24

+

+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

+

+        old_eps.append(e_t)

+        if len(old_eps) >= 4:

+            old_eps.pop(0)

+

+        x = x_prev

+

+        if callback is not None:

+            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})

+

+    return x

+

+

+class UniPCCFG(uni_pc.UniPC):

+    def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):

+        super().__init__(None, *args, **kwargs)

+

+        def after_update(x, model_x):

+            callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})

+            self.index += 1

+

+        self.cfg_model = cfg_model

+        self.extra_args = extra_args

+        self.callback = callback

+        self.index = 0

+        self.after_update = after_update

+

+    def get_model_input_time(self, t_continuous):

+        return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.

+

+    def model(self, x, t):

+        t_input = self.get_model_input_time(t)

+

+        res = self.cfg_model(x, t_input, **self.extra_args)

+

+        return res

+

+

+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):

+    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod

+

+    ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)

+    t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None  # this is likely off by a bit - if someone wants to fix it please by all means

+    unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)

+    x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)

+

+    return x