From 36078b25801787f0a0f145143637f46d33d8c389 Mon Sep 17 00:00:00 2001 From: Ashen Date: Fri, 7 Apr 2023 22:04:35 -0700 Subject: [PATCH] karras v2 experimental --- k_diffusion/sampling.py | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/k_diffusion/sampling.py b/k_diffusion/sampling.py index f050f88..4d5df2a 100644 --- a/k_diffusion/sampling.py +++ b/k_diffusion/sampling.py @@ -605,3 +605,39 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d old_denoised = denoised return x + + +@torch.no_grad() +def sample_dpmpp_2m_test(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + + t_min = min(sigma_fn(t_next), sigma_fn(t)) + t_max = max(sigma_fn(t_next), sigma_fn(t)) + + if old_denoised is None or sigmas[i + 1] == 0: + x = (t_min / t_max) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + + h_min = min(h_last, h) + h_max = max(h_last, h) + r = h_max / h_min + + h_d = (h_max + h_min) / 2 + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (t_min / t_max) * x - (-h_d).expm1() * denoised_d + + old_denoised = denoised + return x \ No newline at end of file -- 2.40.0