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Add phi_2 solver type to seeds_2

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chaObserv 2025-12-13 14:22:51 +08:00
parent 908fd7d749
commit 9b3ef8ec56
1 changed files with 12 additions and 3 deletions
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11
comfy/k_diffusion/sampling.py
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@ -1557,10 +1557,13 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
@torch.no_grad() @torch.no_grad()
def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5): def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"):
"""SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
""" """
if solver_type not in {"phi_1", "phi_2"}:
raise ValueError("solver_type must be 'phi_1' or 'phi_2'")
extra_args = {} if extra_args is None else extra_args extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None) seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@ -1600,8 +1603,14 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
# Step 2 # Step 2
if solver_type == "phi_1":
denoised_d = torch.lerp(denoised, denoised_2, fac) denoised_d = torch.lerp(denoised, denoised_2, fac)
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
elif solver_type == "phi_2":
b2 = ei_h_phi_2(-h_eta) / r
b1 = ei_h_phi_1(-h_eta) - b2
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)
if inject_noise: if inject_noise:
segment_factor = (r - 1) * h * eta segment_factor = (r - 1) * h * eta
sde_noise = sde_noise * segment_factor.exp() sde_noise = sde_noise * segment_factor.exp()