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res_multistep: Fix cfgpp and add ancestral samplers (#6731)

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Pam 2025-02-09 05:39:58 +05:00 committed by GitHub
parent 3d06e1c555
commit caeb27c3a5
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2 changed files with 36 additions and 28 deletions
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59
comfy/k_diffusion/sampling.py
View File

@ -1267,7 +1267,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
return x return x
@torch.no_grad() @torch.no_grad()
def res_multistep(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, cfg_pp=False): def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
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
@ -1289,53 +1289,60 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
for i in trange(len(sigmas) - 1, disable=disable): for i in trange(len(sigmas) - 1, disable=disable):
if s_churn > 0: denoised = model(x, sigmas[i] * s_in, **extra_args)
gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0 sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
sigma_hat = sigmas[i] * (gamma + 1)
else:
gamma = 0
sigma_hat = sigmas[i]
if gamma > 0:
eps = torch.randn_like(x) * s_noise
x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
if callback is not None: if callback is not None:
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised}) callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
if sigmas[i + 1] == 0 or old_denoised is None: if sigma_down == 0 or old_denoised is None:
# Euler method # Euler method
if cfg_pp: if cfg_pp:
d = to_d(x, sigma_hat, uncond_denoised) d = to_d(x, sigmas[i], uncond_denoised)
x = denoised + d * sigmas[i + 1] x = denoised + d * sigma_down
else: else:
d = to_d(x, sigma_hat, denoised) d = to_d(x, sigmas[i], denoised)
dt = sigmas[i + 1] - sigma_hat dt = sigma_down - sigmas[i]
x = x + d * dt x = x + d * dt
else: else:
# Second order multistep method in https://arxiv.org/pdf/2308.02157 # Second order multistep method in https://arxiv.org/pdf/2308.02157
t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigmas[i + 1]), t_fn(sigmas[i - 1]) t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
h = t_next - t h = t_next - t
c2 = (t_prev - t) / h c2 = (t_prev - t) / h
phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0) b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0) b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
if cfg_pp: if cfg_pp:
x = x + (denoised - uncond_denoised) x = x + (denoised - uncond_denoised)
x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
else:
x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
x = (sigma_fn(t_next) / sigma_fn(t)) * x + h * (b1 * denoised + b2 * old_denoised) # Noise addition
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
if cfg_pp:
old_denoised = uncond_denoised
else:
old_denoised = denoised old_denoised = denoised
return x return x
@torch.no_grad() @torch.no_grad()
def sample_res_multistep(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(model, x, sigmas, extra_args=None, callback=None, disable=None, 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=False) return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
@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_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_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
@torch.no_grad()
def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
@torch.no_grad()
def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
@torch.no_grad() @torch.no_grad()
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):

3
comfy/samplers.py
View File

@ -686,7 +686,8 @@ 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", "gradient_estimation"] "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_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={}):