nodes/Clybius-ComfyUI-Extra-Samplers
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Default to 0 momentum when not using a custom sampler node.

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Clybius
2024-02-02 11:09:18 -06:00
parent 72e74b3b77
commit 3be4817026
1 changed files with 3 additions and 3 deletions
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extra_samplers.py
+3 -3
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@@ -317,7 +317,7 @@ def power_noise_sampler(tensor, alpha=2, k=1): # This doesn't work properly righ
# Below this point are extra samplers
@torch.no_grad()
def sample_clyb_4m_sde_momentumized(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None, momentum=0.5):
def sample_clyb_4m_sde_momentumized(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None, momentum=0.0):
"""DPM-Solver++(3M) SDE, modified with an extra SDE, and momentumized in both the SDE and ODE(?). 'its a first' - Clybius 2023
The expression for d1 is derived from the extrapolation formula given in the paper “Diffusion Monte Carlo with stochastic Hamiltonians” by M. Foulkes, L. Mitas, R. Needs, and G. Rajagopal. The formula is given as follows:
d1 = d1_0 + (d1_0 - d1_1) * r2 / (r2 + r1) + ((d1_0 - d1_1) * r2 / (r2 + r1) - (d1_1 - d1_2) * r1 / (r0 + r1)) * r2 / ((r2 + r1) * (r0 + r1))
@@ -447,7 +447,7 @@ def sample_ttm_jvp(model, x, sigmas, extra_args=None, callback=None, disable=Non
# Many thanks to Kat + Birch-San for this wonderful sampler implementation! https://github.com/Birch-san/sdxl-play/commits/res/
from .other_samplers.refined_exp_solver import sample_refined_exp_s
def sample_res_solver(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, denoise_to_zero=True, simple_phi_calc=False, c2=0.5, ita=torch.Tensor((0.25,)), momentum=0.5):
def sample_res_solver(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, denoise_to_zero=True, simple_phi_calc=False, c2=0.5, ita=torch.Tensor((0.25,)), momentum=0.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
@@ -611,7 +611,7 @@ def sample_lcmcustom(model, x, sigmas, extra_args=None, callback=None, disable=N
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
return sample_lcm(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, noise_sampler=noise_sampler)
def sample_clyb_4m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler="brownian", momentum=0.5):
def sample_clyb_4m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler="brownian", momentum=0.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler: