59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
from comfy.samplers import KSAMPLER
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import torch
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from comfy.k_diffusion.sampling import default_noise_sampler, to_d
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from tqdm.auto import trange
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from ... import ROOT_NAME
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@torch.no_grad()
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def sampler_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, gamma=None):
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extra_args = {} if extra_args is None else extra_args
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noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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d = to_d(x, sigmas[i], denoised)
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sigma_from = sigmas[i]
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sigma_to = sigmas[i + 1]
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t = model.inner_model.inner_model.model_sampling.timestep(sigma_from)
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down_t = (1 - gamma) * t
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sigma_down = model.inner_model.inner_model.model_sampling.sigma(down_t)
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if sigma_down > sigma_to:
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sigma_down = sigma_to
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sigma_up = (sigma_to ** 2 - sigma_down ** 2) ** 0.5
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# same as euler ancestral
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d = to_d(x, sigma_from, denoised)
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dt = sigma_down - sigma_from
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x = x + d * dt
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if sigma_to > 0:
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x = x + noise_sampler(sigma_from, sigma_to) * sigma_up
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return x
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class TCDSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required":{
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"gamma": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step":0.01}),
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},
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = ROOT_NAME + "custom_samplers"
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FUNCTION = "get_sampler"
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def get_sampler(self, gamma):
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sampler = KSAMPLER(sampler_tcd, {"gamma": gamma})
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return (sampler, )
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NODE_CLASS_MAPPINGS = {
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"TCDSampler": TCDSampler,
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} |