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add laplace scheduler [2407.03297]

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yoinked 2024-09-19 17:41:26 -07:00 committed by GitHub
parent 3326bdfd4e
commit 72009e7696
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2 changed files with 14 additions and 1 deletions
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11
comfy/k_diffusion/sampling.py
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@ -44,6 +44,17 @@ def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
return append_zero(sigmas) return append_zero(sigmas)
def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
"""Constructs the noise schedule proposed by Tiankai et al. (2024). """
epsilon = 1e-5 # avoid log(0)
x = torch.linspace(0, 1, n, device=device)
clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
sigmas = clamp(torch.exp(lmb))
return sigmas
def to_d(x, sigma, denoised): def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative.""" """Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / utils.append_dims(sigma, x.ndim) return (x - denoised) / utils.append_dims(sigma, x.ndim)

4
comfy/samplers.py
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@ -729,7 +729,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"] SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "laplace"]
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
def calculate_sigmas(model_sampling, scheduler_name, steps): def calculate_sigmas(model_sampling, scheduler_name, steps):
@ -747,6 +747,8 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
sigmas = normal_scheduler(model_sampling, steps, sgm=True) sigmas = normal_scheduler(model_sampling, steps, sgm=True)
elif scheduler_name == "beta": elif scheduler_name == "beta":
sigmas = beta_scheduler(model_sampling, steps) sigmas = beta_scheduler(model_sampling, steps)
elif scheduler_name == "laplace":
sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
else: else:
logging.error("error invalid scheduler {}".format(scheduler_name)) logging.error("error invalid scheduler {}".format(scheduler_name))
return sigmas return sigmas