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dpmpp_2s_ancestral_RF for rectified flow (Flux, SD3 and Auraflow).

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Ashen 2024-08-17 17:32:27 -07:00 committed by comfyanonymous
parent 9aa39e743c
commit 1beb348ee2
2 changed files with 50 additions and 1 deletions
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49
comfy/k_diffusion/sampling.py
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@ -541,6 +541,55 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
return x
@torch.no_grad()
def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
# logged_x = x.unsqueeze(0)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
sigma_down = sigmas[i+1] * downstep_ratio
alpha_ip1 = 1 - sigmas[i+1]
alpha_down = 1 - sigma_down
renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
# sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
if sigmas[i + 1] == 0:
# Euler method
d = to_d(x, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
x = x + d * dt
else:
# DPM-Solver++(2S)
if sigmas[i] == 1.0:
sigma_s = 0.9999
else:
t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down)
r = 1 / 2
h = t_down - t_i
s = t_i + r * h
sigma_s = sigma_fn(s)
# sigma_s = sigmas[i+1]
sigma_s_i_ratio = sigma_s / sigmas[i]
u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised
D_i = model(u, sigma_s * s_in, **extra_args)
sigma_down_i_ratio = sigma_down / sigmas[i]
x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i
# print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff)
# Noise addition
if sigmas[i + 1] > 0 and eta > 0:
x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
# logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
return x
@torch.no_grad()
def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
"""DPM-Solver++ (stochastic)."""

2
comfy/samplers.py
View File

@ -570,7 +570,7 @@ class Sampler:
return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_RF", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis"]