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Update samplers.py

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LaVie024 2025-05-02 21:36:29 +00:00 committed by GitHub
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1 changed files with 31 additions and 9 deletions
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comfy/samplers.py
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@ -16,8 +16,6 @@ import comfy.sampler_helpers
import comfy.model_patcher import comfy.model_patcher
import comfy.patcher_extension import comfy.patcher_extension
import comfy.hooks import comfy.hooks
import scipy.stats
import numpy
def add_area_dims(area, num_dims): def add_area_dims(area, num_dims):
@ -442,21 +440,44 @@ def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
return torch.FloatTensor(sigs) return torch.FloatTensor(sigs)
# Implemented based on: https://arxiv.org/abs/2407.12173 # Implemented based on: https://arxiv.org/abs/2407.12173, but with a slightly incorrect
def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): # implementation. See beta_verus_scheduler for the proper implementation.
total_timesteps = (len(model_sampling.sigmas) - 1) def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6, endpoint=False):
ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) # Lazy loader
ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) if not hasattr(beta_scheduler, "_init_done"):
import numpy as np
from scipy.stats import beta as sp_beta
beta_scheduler._np = np
beta_scheduler._sp_beta = sp_beta
beta_scheduler._init_done = True
np = beta_scheduler._np
sp_beta = beta_scheduler._sp_beta
total_timesteps = len(model_sampling.sigmas) - 1
ts = 1 - np.linspace(0, 1, steps, endpoint=endpoint)
ts = np.rint(sp_beta.ppf(ts, alpha, beta) * total_timesteps)
sigs = [] sigs = []
last_t = -1 last_t = -1
for t in ts: for t in ts:
if t != last_t: if t != last_t:
sigs += [float(model_sampling.sigmas[int(t)])] sigs.append(float(model_sampling.sigmas[int(t)]))
last_t = t last_t = t
sigs += [0.0] sigs.append(0.0)
return torch.FloatTensor(sigs) return torch.FloatTensor(sigs)
# Loads beta, but sets the endpoints to true instead of false. This reflects
# the actual interval of the beta distribution, [0, 1].
def beta_verus_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
return beta_scheduler(
model_sampling,
steps,
alpha=alpha,
beta=beta,
endpoint=True
)
# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41 # from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41
def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None): def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None):
if steps == 1: if steps == 1:
@ -1040,6 +1061,7 @@ SCHEDULER_HANDLERS = {
"simple": SchedulerHandler(simple_scheduler), "simple": SchedulerHandler(simple_scheduler),
"ddim_uniform": SchedulerHandler(ddim_scheduler), "ddim_uniform": SchedulerHandler(ddim_scheduler),
"beta": SchedulerHandler(beta_scheduler), "beta": SchedulerHandler(beta_scheduler),
"beta_verus": SchedulerHandler(beta_verus_scheduler),
"linear_quadratic": SchedulerHandler(linear_quadratic_schedule), "linear_quadratic": SchedulerHandler(linear_quadratic_schedule),
"kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False), "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False),
} }