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add laplace scheduler [2407.03297] #4990

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Sep 20, 2024
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Merged

add laplace scheduler [2407.03297] #4990

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72009e7
add laplace scheduler [2407.03297]
yoinked-h Sep 20, 2024
ed42dfe
should be here instead lol
yoinked-h Sep 20, 2024
c079b69
better settings
yoinked-h Sep 20, 2024
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should be here instead lol
  • Loading branch information
@yoinked-h
yoinked-h authored Sep 20, 2024
commit ed42dfe45c3bf39b4482b0501836c9a34b02aebc
4 changes: 1 addition & 3 deletions comfy/samplers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)


SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "laplace"]
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]

def calculate_sigmas(model_sampling, scheduler_name, steps):
Expand All @@ -747,8 +747,6 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
sigmas = normal_scheduler(model_sampling, steps, sgm=True)
elif scheduler_name == "beta":
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:
logging.error("error invalid scheduler {}".format(scheduler_name))
return sigmas
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22 changes: 22 additions & 0 deletions comfy_extras/nodes_custom_sampler.py
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Expand Up @@ -90,6 +90,27 @@ def get_sigmas(self, steps, sigma_max, sigma_min, rho):
sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
return (sigmas, )

class LaplaceScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"mu": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"beta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"

FUNCTION = "get_sigmas"

def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta):
sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
return (sigmas, )


class SDTurboScheduler:
@classmethod
def INPUT_TYPES(s):
Expand Down Expand Up @@ -673,6 +694,7 @@ def add_noise(self, model, noise, sigmas, latent_image):
"KarrasScheduler": KarrasScheduler,
"ExponentialScheduler": ExponentialScheduler,
"PolyexponentialScheduler": PolyexponentialScheduler,
"LaplaceScheduler": LaplaceScheduler,
"VPScheduler": VPScheduler,
"BetaSamplingScheduler": BetaSamplingScheduler,
"SDTurboScheduler": SDTurboScheduler,
Expand Down