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Make euler_ancestral work on flow models (credit: Ashen).

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comfyanonymous 2024-10-25 19:53:44 -04:00
parent ce759b7db6
commit d605677b33
1 changed files with 25 additions and 0 deletions
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25
comfy/k_diffusion/sampling.py
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@ -164,6 +164,8 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
@torch.no_grad() @torch.no_grad()
def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
"""Ancestral sampling with Euler method steps.""" """Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@ -181,6 +183,29 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
return x return x
@torch.no_grad()
def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
# sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
sigma_down = sigmas[i+1] * downstep_ratio
alpha_ip1 = 1 - sigmas[i+1]
alpha_down = 1 - sigma_down
renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
# Euler method
sigma_down_i_ratio = sigma_down / sigmas[i]
x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised
if sigmas[i + 1] > 0 and eta > 0:
x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
return x
@torch.no_grad() @torch.no_grad()
def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):