VictorMylle/Kohya-ss-sd-scripts
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Merge branch 'dev' of https://github.com/kohya-ss/sd-scripts into dev

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ykume
2023-05-03 11:17:43 +09:00
parent 2fcbfec178 2e688b7cd3
commit 69579668bb
7 changed files with 42 additions and 5 deletions
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4
fine_tune.py
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@@ -21,7 +21,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like
def train(args):
@@ -307,6 +307,8 @@ def train(args):
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
elif args.multires_noise_iterations:
noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)

13
library/custom_train_functions.py
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@@ -1,5 +1,6 @@
import torch
import argparse
import random
import re
from typing import List, Optional, Union
@@ -342,3 +343,15 @@ def get_weighted_text_embeddings(
text_embeddings = text_embeddings * (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
return text_embeddings
# https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
def pyramid_noise_like(noise, device, iterations=6, discount=0.3):
b, c, w, h = noise.shape
u = torch.nn.Upsample(size=(w, h), mode='bilinear').to(device)
for i in range(iterations):
r = random.random()*2+2 # Rather than always going 2x,
w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i)))
noise += u(torch.randn(b, c, w, h).to(device)) * discount**i
if w==1 or h==1: break # Lowest resolution is 1x1
return noise/noise.std() # Scaled back to roughly unit variance

12
library/train_util.py
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@@ -2121,6 +2121,18 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
default=None,
help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）",
)
parser.add_argument(
"--multires_noise_iterations",
type=int,
default=None,
help="enable multires noise with this number of iterations (if enabled, around 6-10 is recommended)"
)
parser.add_argument(
"--multires_noise_discount",
type=float,
default=0.3,
help="set discount value for multires noise (has no effect without --multires_noise_iterations)"
)
parser.add_argument(
"--lowram",
action="store_true",

4
train_db.py
View File

@@ -23,7 +23,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like
def train(args):
@@ -273,6 +273,8 @@ def train(args):
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
elif args.multires_noise_iterations:
noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
# Get the text embedding for conditioning
with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):

6
train_network.py
View File

@@ -25,7 +25,7 @@ from library.config_util import (
)
import library.huggingface_util as huggingface_util
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings
from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like
# TODO 他のスクリプトと共通化する
@@ -342,6 +342,8 @@ def train(args):
"ss_seed": args.seed,
"ss_lowram": args.lowram,
"ss_noise_offset": args.noise_offset,
"ss_multires_noise_iterations": args.multires_noise_iterations,
"ss_multires_noise_discount": args.multires_noise_discount,
"ss_training_comment": args.training_comment, # will not be updated after training
"ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
"ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
@@ -588,6 +590,8 @@ def train(args):
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
elif args.multires_noise_iterations:
noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)

4
train_textual_inversion.py
View File

@@ -20,7 +20,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight
from library.custom_train_functions import apply_snr_weight, pyramid_noise_like
imagenet_templates_small = [
"a photo of a {}",
@@ -389,6 +389,8 @@ def train(args):
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
elif args.multires_noise_iterations:
noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)

4
train_textual_inversion_XTI.py
View File

@@ -20,7 +20,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight
from library.custom_train_functions import apply_snr_weight, pyramid_noise_like
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
imagenet_templates_small = [
@@ -428,6 +428,8 @@ def train(args):
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
elif args.multires_noise_iterations:
noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)