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Add DPM-Solver++(2M) SDE and exponential scheduler.

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exponential scheduler is the one recommended with this sampler.
This commit is contained in:
comfyanonymous
2023-05-21 01:35:08 -04:00
parent 516119ad83
commit 069657fbf3
2 changed files with 47 additions and 2 deletions
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6
comfy/samplers.py
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@@ -495,10 +495,10 @@ def encode_adm(noise_augmentor, conds, batch_size, device):
class KSampler:
SCHEDULERS = ["normal", "karras", "simple", "ddim_uniform"]
SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde",
"dpmpp_2m", "ddim", "uni_pc", "uni_pc_bh2"]
"dpmpp_2m", "dpmpp_2m_sde", "ddim", "uni_pc", "uni_pc_bh2"]
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
self.model = model
@@ -532,6 +532,8 @@ class KSampler:
if self.scheduler == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
elif self.scheduler == "exponential":
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
elif self.scheduler == "normal":
sigmas = self.model_wrap.get_sigmas(steps)
elif self.scheduler == "simple":