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import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def inverse_sigmoid(y):
return np.log(y / (1 - y))
# 逆シグモイド関数の微分
================================================================================================================================================================
Layer (type (var_name)) Input Shape Output Shape Param # Kernel Shape
================================================================================================================================================================
SD3Transformer2DModel (SD3Transformer2DModel) -- [1, 16, 128, 128] -- --
├─PatchEmbed (pos_embed) [1, 16, 128, 128] [1, 4096, 1536] -- --
│ └─Conv2d (proj) [1, 16, 128, 128] [1, 1536, 64, 64] 99,840 [2, 2]
├─CombinedTimestepTextProjEmbeddings (time_text_embed) [1] [1, 1536] --
import comfy
from comfy.samplers import KSAMPLER
import torch
from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler
from tqdm.auto import trange
@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, upscale_ratio=2.0, start_step=5, end_step=15, upscale_n_step=3):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args