VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-09 06:45:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix for TypeError from bf16 precision: Thanks to mgz-dev

Browse Source
This commit is contained in:
AI-Casanova
2023-03-26 00:01:29 +00:00
parent 518a18aeff
commit 4c06bfad60
1 changed files with 5 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
library/custom_train_functions.py
View File

@@ -1,21 +1,18 @@
import torch
import argparse
import numpy as np
def apply_snr_weight(loss, timesteps, noise_scheduler, gamma):
alphas_cumprod = noise_scheduler.alphas_cumprod.cpu()
sqrt_alphas_cumprod = np.sqrt(alphas_cumprod)
sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - alphas_cumprod)
alphas_cumprod = noise_scheduler.alphas_cumprod
sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
alpha = sqrt_alphas_cumprod
sigma = sqrt_one_minus_alphas_cumprod
all_snr = (alpha / sigma) ** 2
all_snr.to(loss.device)
snr = torch.stack([all_snr[t] for t in timesteps])
gamma_over_snr = torch.div(torch.ones_like(snr)*gamma,snr)
snr_weight = torch.minimum(gamma_over_snr,torch.ones_like(gamma_over_snr)).float().to(loss.device) #from paper
snr_weight = torch.minimum(gamma_over_snr,torch.ones_like(gamma_over_snr)).float() #from paper
loss = loss * snr_weight
return loss
def add_custom_train_arguments(parser: argparse.ArgumentParser):
parser.add_argument("--min_snr_gamma", type=float, default=0, help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper.")
parser.add_argument("--min_snr_gamma", type=float, default=None, help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper.")