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Add gradient estimation sampler (#6554)

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chaObserv 2025-01-22 18:29:40 +08:00 committed by GitHub
parent d303cb5341
commit e857dd48b8
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2 changed files with 24 additions and 1 deletions
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23
comfy/k_diffusion/sampling.py
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@ -1336,3 +1336,26 @@ def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disab
@torch.no_grad() @torch.no_grad()
def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None): def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True) return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
@torch.no_grad()
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
"""Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
old_d = None
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
d = to_d(x, sigmas[i], denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
dt = sigmas[i + 1] - sigmas[i]
if i == 0:
# Euler method
x = x + d * dt
else:
# Gradient estimation
d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
x = x + d_bar * dt
old_d = d
return x

2
comfy/samplers.py
View File

@ -686,7 +686,7 @@ class Sampler:
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", 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_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp"] "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "gradient_estimation"]
class KSAMPLER(Sampler): class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}): def __init__(self, sampler_function, extra_options={}, inpaint_options={}):