Repository: Ttl/ComfyUi_NNLatentUpscale
Branch: master
Commit: 08105da31dbd
Files: 9
Total size: 24.2 MB
Directory structure:
gitextract_lidqyb8t/
├── LICENSE
├── README.md
├── __init__.py
├── evaluation.py
├── latent_resizer.py
├── latent_resizer_train.py
├── nn_upscale.py
├── sd15_resizer.pt
└── sdxl_resizer.pt
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# ComfyUI Neural network latent upscale custom node
This repository includes a custom node for
[ComfyUI](https://github.com/comfyanonymous/ComfyUI) for upscaling the latents
quickly using a small neural network without needing to decode and encode with
VAE. The node can be found in "Add Node -> latent -> NNLatentUpscale".
This node is meant to be used in a workflow where the initial image is
generated in lower resolution, the latent is upscaled and the upscaled latent is
fed back into the stable diffusion u-net for low noise diffusion pass (high-res
fix).
Compared to VAE decode -> upscale -> encode, the neural net latent upscale is
about 20 - 50 times faster depending on the image resolution with minimal
quality loss. Compared to direct linear interpolation of the latent the neural
net upscale is slower but has much better quality. Direct latent interpolation
usually has very large artifacts.
## Installation
Clone this repository in ComfyUI `custom_nodes` directory with: `git clone https://github.com/Ttl/ComfyUi_NNLatentUpscale.git`.
## Evaluation
Dataset: [COCO 2017](https://cocodataset.org.org) validation images center
cropped to 256x256 resolution. The comparison image is linear upscale of the
input image. All tests are done with fp32 precision and batch size 4.
VAE Upscale: VAE decode -> Linear interpolation -> Encode.
NN Upscale: Neural network upscale (This repository).
Latent Upscale: Linear interpolation of latent.
SDXL, 2x upscale:
| | MSE ↓ | LPIPS ↓ | PSNR ↑ | Time (ms) ↓ |
|----------------------|--------|---------|--------|-------------|
| VAE Upscale | 0.009 | 0.22 | 26.9 | 832 |
| NN Upscale | 0.010 | 0.28 | 26.3 | 36 |
| Latent Upscale | 0.047 | 0.65 | 19.5 | 0.1 |
SDXL, 1.5x upscale:
| | MSE ↓ | LPIPS ↓ | PSNR ↑ | Time (ms) ↓ |
|----------------------|--------|---------|--------|-------------|
| VAE Upscale | 0.009 | 0.20 | 26.9 | 583 |
| NN Upscale | 0.010 | 0.26 | 26.3 | 19 |
| Latent Upscale | 0.038 | 0.58 | 20.4 | 0.1 |
SD 1.5, 2x upscale:
| | MSE ↓ | LPIPS ↓ | PSNR ↑ | Time (ms) ↓ |
|----------------------|-------|---------|--------|-------------|
| VAE Upscale | 0.009 | 0.21 | 26.7 | 822 |
| NN Upscale | 0.008 | 0.24 | 27.0 | 36 |
| Latent Upscale | 0.033 | 0.61 | 20.9 | 0.1 |
SD 1.5, 1.5x upscale:
| | MSE ↓ | LPIPS ↓ | PSNR ↑ | Time (ms) ↓ |
|----------------------|-------|---------|--------|-------------|
| VAE Upscale | 0.010 | 0.18 | 26.5 | 594 |
| NN Upscale | 0.009 | 0.21 | 26.9 | 20 |
| Latent Upscale | 0.031 | 0.52 | 21.3 | 0.1 |
================================================
FILE: __init__.py
================================================
from .nn_upscale import NNLatentUpscale
NODE_CLASS_MAPPINGS = {
"NNLatentUpscale": NNLatentUpscale
}
NODE_DISPLAY_NAME_MAPPINGS = {
"NNlLatentUpscale": "NN Latent Upscale"
}
================================================
FILE: evaluation.py
================================================
#!/usr/bin/env python
from latent_resizer import LatentResizer
import argparse
from diffusers import AutoencoderKL
import lpips
import torch
from torchvision import transforms
from pathlib import Path
from PIL import Image
import numpy as np
from tqdm import tqdm
from pytorch_msssim import ssim
def psnr(x, ref, maxg=2):
mse = torch.mean(torch.square(x - ref))
return 20 * torch.log10(maxg / torch.sqrt(mse))
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, path, size):
self.path = Path(path)
if not self.path.exists():
raise ValueError("Dataset path does not exist")
self.images = list(self.path.iterdir())
self.num_images = len(self.images)
self.image_transforms = transforms.Compose(
[
transforms.Resize(
size, interpolation=transforms.InterpolationMode.BILINEAR
),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self.num_images
def __getitem__(self, index):
example = {}
image = Image.open(self.images[index % self.num_images]).convert("RGB")
image = self.image_transforms(image)
return image
def collate_fn(images):
images = torch.stack(images)
images = images.to(memory_format=torch.contiguous_format).float()
return images
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Latent resizer evaluation")
parser.add_argument(
"--test_path",
required=True,
type=str,
help="Test images",
)
parser.add_argument(
"--vae_path",
required=True,
type=str,
help="VAE path",
)
parser.add_argument(
"--resizer_path",
required=True,
type=str,
help="Resizer weight path",
)
parser.add_argument(
"--device",
required=False,
type=str,
default="cuda",
help="Torch device",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Use fp16 precision",
)
parser.add_argument(
"--num_workers",
default=4,
type=int,
help="Dataloader workers",
)
parser.add_argument(
"--batch_size",
default=8,
type=int,
help="Batch size",
)
parser.add_argument(
"--resolution",
default=256,
type=int,
help="Image resolution",
)
parser.add_argument(
"--scale",
default=2.0,
type=float,
required=True,
help="Resize scale",
)
parser.add_argument(
"--resizer_only",
action="store_true",
help="Only evaluate resizer",
)
args = parser.parse_args()
device = torch.device(args.device)
scale_factor = 0.13025
if args.fp16:
dtype = torch.float16
else:
dtype = torch.float32
vae = AutoencoderKL.from_single_file(args.vae_path).to(device, dtype=dtype)
vae.eval()
resizer = LatentResizer.load_model(args.resizer_path, device, dtype)
# LPIPS is always in float32 because of nans in float16
lpips_fn = lpips.LPIPS(net="vgg").to(device=device, dtype=torch.float32)
dataset = ImageDataset(args.test_path, args.resolution)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=args.batch_size, num_workers=args.num_workers
)
elapsed_vae_list = []
elapsed_resizer_list = []
elapsed_latent_list = []
mse_vae_list = []
mse_resizer_list = []
mse_latent_list = []
lpips_vae_list = []
lpips_resizer_list = []
lpips_latent_list = []
psnr_vae_list = []
psnr_resizer_list = []
psnr_latent_list = []
ssim_vae_list = []
ssim_resizer_list = []
ssim_latent_list = []
try:
with torch.inference_mode():
for images in tqdm(dataloader):
images = images.to(device=device, dtype=dtype)
images_upscaled = torch.nn.functional.interpolate(
images, scale_factor=args.scale, mode="bilinear"
)
latents = vae.encode(images).latent_dist.sample()
del images
# Resizer
start_resizer = torch.cuda.Event(enable_timing=True)
end_resizer = torch.cuda.Event(enable_timing=True)
start_resizer.record()
resized = (
resizer(scale_factor * latents, scale=args.scale) / scale_factor
)
end_resizer.record()
torch.cuda.synchronize()
resizer_elapsed = start_resizer.elapsed_time(end_resizer)
decoded_resized = vae.decode(resized)[0]
del resized
elapsed_resizer_list.append(resizer_elapsed)
mse_resizer = torch.nn.functional.mse_loss(
decoded_resized, images_upscaled
)
mse_resizer_list.extend(mse_resizer.cpu().numpy().flatten())
lpips_resizer = lpips_fn(
decoded_resized.float(), images_upscaled.float()
)
lpips_resizer_list.extend(lpips_resizer.cpu().numpy().flatten())
psnr_resized = psnr(decoded_resized, images_upscaled)
psnr_resizer_list.extend(psnr_resized.cpu().numpy().flatten())
ssim_resized = ssim(
0.5 * (decoded_resized + 1),
0.5 * (images_upscaled + 1),
data_range=1,
size_average=True,
)
ssim_resizer_list.append(ssim_resized.cpu())
if not args.resizer_only:
start_vae = torch.cuda.Event(enable_timing=True)
end_vae = torch.cuda.Event(enable_timing=True)
# VAE decode -> upscale -> encode
start_vae.record()
decoded_img = vae.decode(latents)[0]
img_upscaled = torch.nn.functional.interpolate(
decoded_img, scale_factor=args.scale, mode="bilinear"
)
vae_encoded = vae.encode(img_upscaled).latent_dist.sample()
end_vae.record()
torch.cuda.synchronize()
vae_elapsed = start_vae.elapsed_time(end_vae)
# Scale latent
start_latent = torch.cuda.Event(enable_timing=True)
end_latent = torch.cuda.Event(enable_timing=True)
start_latent.record()
resized_latent = torch.nn.functional.interpolate(
latents, scale_factor=args.scale, mode="bilinear"
)
end_latent.record()
torch.cuda.synchronize()
latent_elapsed = start_latent.elapsed_time(end_latent)
elapsed_vae_list.append(vae_elapsed)
elapsed_latent_list.append(latent_elapsed)
# Decode latents and calculate LPIPS and MSE
decoded_vae = vae.decode(vae_encoded)[0]
decoded_latent = vae.decode(resized_latent)[0]
mse_vae = torch.nn.functional.mse_loss(decoded_vae, images_upscaled)
mse_latent = torch.nn.functional.mse_loss(
decoded_latent, images_upscaled
)
mse_vae_list.extend(mse_vae.cpu().numpy().flatten())
mse_latent_list.extend(mse_latent.cpu().numpy().flatten())
lpips_vae = lpips_fn(decoded_vae.float(), images_upscaled.float())
lpips_latent = lpips_fn(
decoded_latent.float(), images_upscaled.float()
)
lpips_vae_list.extend(lpips_vae.cpu().numpy().flatten())
lpips_latent_list.extend(lpips_latent.cpu().numpy().flatten())
psnr_vae = psnr(decoded_vae, images_upscaled)
psnr_latent = psnr(decoded_latent, images_upscaled)
psnr_vae_list.extend(psnr_vae.cpu().numpy().flatten())
psnr_latent_list.extend(psnr_latent.cpu().numpy().flatten())
ssim_vae = ssim(
0.5 * (decoded_vae + 1),
0.5 * (images_upscaled + 1),
data_range=1,
size_average=True,
)
ssim_latent = ssim(
0.5 * (decoded_latent + 1),
0.5 * (images_upscaled + 1),
data_range=1,
size_average=True,
)
ssim_vae_list.append(ssim_vae.cpu())
ssim_latent_list.append(ssim_latent.cpu())
finally:
print("Batch size", args.batch_size)
if not args.resizer_only:
print("Elapsed VAE", np.mean(elapsed_vae_list), "ms")
print("Elapsed latent upscale", np.mean(elapsed_latent_list), "ms")
print("Elapsed resizer", np.mean(elapsed_resizer_list), "ms")
if not args.resizer_only:
print("MSE VAE", np.mean(mse_vae_list))
print("MSE latent upscale", np.mean(mse_latent_list))
print("MSE resizer", np.mean(mse_resizer_list))
if not args.resizer_only:
print("LPIPS VAE", np.mean(lpips_vae_list))
print("LPIPS latent upscale", np.mean(lpips_latent_list))
print("LPIPS resizer", np.mean(lpips_resizer_list))
if not args.resizer_only:
print("PSNR VAE", np.mean(psnr_vae_list))
print("PSNR latent upscale", np.mean(psnr_latent_list))
print("PSNR resizer", np.mean(psnr_resizer_list))
if not args.resizer_only:
print("SSIM VAE", np.mean(ssim_vae_list))
print("SSIM latent upscale", np.mean(ssim_latent_list))
print("SSIM resizer", np.mean(ssim_resizer_list))
================================================
FILE: latent_resizer.py
================================================
#!/usr/bin/env python
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
def normalization(channels):
return nn.GroupNorm(32, channels)
def zero_module(module):
for p in module.parameters():
p.detach().zero_()
return module
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = normalization(in_channels)
self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.proj_out = nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
def attention(self, h_: torch.Tensor) -> torch.Tensor:
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
q, k, v = map(
lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v)
)
h_ = nn.functional.scaled_dot_product_attention(
q, k, v
) # scale is dim ** -0.5 per default
return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
def forward(self, x, **kwargs):
h_ = x
h_ = self.attention(h_)
h_ = self.proj_out(h_)
return x + h_
def make_attn(in_channels, attn_kwargs=None):
return AttnBlock(in_channels)
class ResBlockEmb(nn.Module):
def __init__(
self,
channels,
emb_channels,
dropout=0,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
kernel_size=3,
exchange_temb_dims=False,
skip_t_emb=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
self.exchange_temb_dims = exchange_temb_dims
padding = kernel_size // 2
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
nn.Conv2d(channels, self.out_channels, kernel_size, padding=padding),
)
self.skip_t_emb = skip_t_emb
self.emb_out_channels = (
2 * self.out_channels if use_scale_shift_norm else self.out_channels
)
if self.skip_t_emb:
print(f"Skipping timestep embedding in {self.__class__.__name__}")
assert not self.use_scale_shift_norm
self.emb_layers = None
self.exchange_temb_dims = False
else:
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
emb_channels,
self.emb_out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
nn.Conv2d(
self.out_channels,
self.out_channels,
kernel_size,
padding=padding,
)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = nn.Conv2d(
channels, self.out_channels, kernel_size, padding=padding
)
else:
self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
def forward(self, x, emb):
h = self.in_layers(x)
if self.skip_t_emb:
emb_out = torch.zeros_like(h)
else:
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
if self.exchange_temb_dims:
emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class LatentResizer(nn.Module):
def __init__(self, in_blocks=10, out_blocks=10, channels=128, dropout=0, attn=True):
super().__init__()
self.conv_in = nn.Conv2d(4, channels, 3, padding=1)
self.channels = channels
embed_dim = 32
self.embed = nn.Sequential(
nn.Linear(1, embed_dim),
nn.SiLU(),
nn.Linear(embed_dim, embed_dim),
)
self.in_blocks = nn.ModuleList([])
for b in range(in_blocks):
if (b == 1 or b == in_blocks - 1) and attn:
self.in_blocks.append(make_attn(channels))
self.in_blocks.append(ResBlockEmb(channels, embed_dim, dropout))
self.out_blocks = nn.ModuleList([])
for b in range(out_blocks):
if (b == 1 or b == out_blocks - 1) and attn:
self.out_blocks.append(make_attn(channels))
self.out_blocks.append(ResBlockEmb(channels, embed_dim, dropout))
self.norm_out = normalization(channels)
self.conv_out = nn.Conv2d(channels, 4, 3, padding=1)
@classmethod
def load_model(cls, filename, device="cpu", dtype=torch.float32, dropout=0):
if not 'weights_only' in torch.load.__code__.co_varnames:
weights = torch.load(filename, map_location=torch.device("cpu"))
else:
weights = torch.load(filename, map_location=torch.device("cpu"), weights_only=True)
in_blocks = 0
out_blocks = 0
in_tfs = 0
out_tfs = 0
channels = weights["conv_in.bias"].shape[0]
for k in weights.keys():
k = k.split(".")
if k[0] == "in_blocks":
in_blocks = max(in_blocks, int(k[1]))
if k[2] == "q" and k[3] == "weight":
in_tfs += 1
if k[0] == "out_blocks":
out_blocks = max(out_blocks, int(k[1]))
if k[2] == "q" and k[3] == "weight":
out_tfs += 1
in_blocks = in_blocks + 1 - in_tfs
out_blocks = out_blocks + 1 - out_tfs
resizer = cls(
in_blocks=in_blocks,
out_blocks=out_blocks,
channels=channels,
dropout=dropout,
attn=(out_tfs != 0),
)
resizer.load_state_dict(weights)
resizer.eval()
resizer.to(device, dtype=dtype)
return resizer
def forward(self, x, scale=None, size=None):
if scale is None and size is None:
raise ValueError("Either scale or size needs to be not None")
if scale is not None and size is not None:
raise ValueError("Both scale or size can't be not None")
if scale is not None:
size = (x.shape[-2] * scale, x.shape[-1] * scale)
size = tuple([int(round(i)) for i in size])
else:
scale = size[-1] / x.shape[-1]
# Output is the same size as input
if size == x.shape[-2:]:
return x
scale = torch.tensor([scale - 1], dtype=x.dtype).to(x.device).unsqueeze(0)
emb = self.embed(scale)
x = self.conv_in(x)
for b in self.in_blocks:
if isinstance(b, ResBlockEmb):
x = b(x, emb)
else:
x = b(x)
x = F.interpolate(x, size=size, mode="bilinear")
for b in self.out_blocks:
if isinstance(b, ResBlockEmb):
x = b(x, emb)
else:
x = b(x)
x = self.norm_out(x)
x = F.silu(x)
x = self.conv_out(x)
return x
================================================
FILE: latent_resizer_train.py
================================================
#!/usr/bin/env python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torch.utils.tensorboard import SummaryWriter
from diffusers import AutoencoderKL
import argparse
import os
import random
import webdataset as wds
import io
from tqdm.auto import tqdm
from latent_resizer import LatentResizer
import lpips
from collections import defaultdict
from PIL import Image
from torchvision import transforms
def init_dataset(dataset_path, size=512):
shards = []
if type(dataset_path) not in (list, tuple):
dataset_path = [dataset_path]
for path in dataset_path:
for filename in os.listdir(path):
full_path = os.path.join(path, filename)
if full_path.endswith(".tar"):
shards.append(full_path)
print(f"{len(shards)} shards")
def preprocess(sample):
k = [k for k in sample.keys() if k in ["jpg", "png"]]
if len(k) == 0:
raise ValueError("Dataset images should be in jpg or png format")
k = k[0]
img = sample[k]
img = Image.open(io.BytesIO(img))
if not img.mode == "RGB":
img = img.convert("RGB")
pil_image = img
image_transforms = transforms.Compose(
[
transforms.RandomCrop(size, pad_if_needed=True, padding_mode="reflect"),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
img = image_transforms(img)
examples = {}
examples["img"] = img
return examples
dataset = (
wds.WebDataset(shards, handler=wds.warn_and_continue, shardshuffle=True)
.repeat()
.shuffle(256)
.map(preprocess)
)
return dataset
def collate_fn(examples):
imgs = [example["img"] for example in examples]
imgs = torch.stack(imgs)
scale = random.uniform(1.0, 2.1)
size = [int(round(imgs.shape[-2] * scale)), int(round(imgs.shape[-1] * scale))]
size[0] -= size[0] % 8
size[1] -= size[1] % 8
imgs_scaled = F.interpolate(imgs, size=size, mode="bilinear")
if scale < 1:
batch = {
"img_input": imgs_scaled,
"img_target": imgs,
}
else:
batch = {
"img_input": imgs,
"img_target": imgs_scaled,
}
return batch
def calculate_loss(
model,
batch,
vae,
lpips,
dtype=torch.float16,
mse_weight=1,
lpips_weight=0.1,
mse_latent_weight=0.01,
):
img_input = batch["img_input"].to(args.device, dtype=dtype)
img_target = batch["img_target"].to(args.device, dtype=dtype)
latent_input = (
vae.config.scaling_factor * vae.encode(img_input).latent_dist.sample()
)
latent_target = (
vae.config.scaling_factor * vae.encode(img_target).latent_dist.sample()
)
size = latent_target.shape[-2:]
with torch.autocast(args.device, dtype=dtype, enabled=dtype != torch.float32):
resized = model(latent_input, size=size)
mse_latent = F.mse_loss(resized, latent_target)
logs = {"mse_latent": mse_latent.detach().cpu().item()}
decoded = vae.decode(resized / vae.config.scaling_factor)[0]
mse = F.mse_loss(decoded, img_target)
logs["mse"] = mse
loss = mse_weight * mse + mse_latent_weight * mse_latent
if lpips_weight > 0:
ploss = lpips(decoded, img_target).mean()
logs["lpips"] = ploss.detach().cpu().item()
loss = loss + lpips_weight * ploss
logs["loss"] = loss.detach().cpu().item()
return loss, logs
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Latent interpolate trainer")
parser.add_argument(
"--train_path",
required=True,
action="append",
help="Training data path for VAE latents. Webdataset format.",
)
parser.add_argument(
"--test_path",
default=None,
required=False,
action="append",
help="Test data path for VAE latents. Webdataset format.",
)
parser.add_argument(
"--vae_path",
type=str,
required=True,
help="Path to VAE",
)
parser.add_argument(
"--test_steps",
type=int,
default=1000,
required=False,
help="Test interval",
)
parser.add_argument(
"--test_batches",
type=int,
default=10,
required=False,
help="Number of test batches",
)
parser.add_argument(
"--output_filename",
type=str,
default="sdxl_resizer.pt",
required=False,
help="Output filename",
)
parser.add_argument(
"--steps",
type=int,
default=100000,
help="Number of steps to train",
)
parser.add_argument(
"--save_steps",
type=int,
default=5000,
help="Save model every this step",
)
parser.add_argument(
"--batch_size",
type=int,
default=4,
help="Batch size",
)
parser.add_argument(
"--num_workers",
type=int,
default=4,
help="CPU workers",
)
parser.add_argument(
"--lr",
type=float,
default=2e-4,
help="Learning rate",
)
parser.add_argument(
"--dropout",
type=float,
default=0.0,
help="Droput rate",
)
parser.add_argument(
"--grad_clip",
type=float,
default=5.0,
help="Gradient clipping",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
help="Device to use",
)
parser.add_argument(
"--resolution",
type=int,
default=256,
help="Image resolution",
)
parser.add_argument(
"--init_weights",
type=str,
default=None,
help="Resume training from weights file",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Use fp16 precision",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Enable gradient checkpointing for VAE",
)
args = parser.parse_args()
device = torch.device(args.device)
vae_dtype = torch.float32
if args.fp16:
vae_dtype = torch.float16
vae = AutoencoderKL.from_single_file(args.vae_path).to(device, dtype=vae_dtype)
# Use this scale even with SD 1.5
vae.config.scaling_factor = 0.13025
vae.train()
if args.gradient_checkpointing:
vae.enable_gradient_checkpointing()
lpips_fn = lpips.LPIPS(net="vgg").to(device=device, dtype=vae_dtype)
if args.init_weights:
model = LatentResizer.load_model(
args.init_weights,
device=args.device,
dropout=args.dropout,
dtype=torch.float32,
)
else:
model = LatentResizer(dropout=args.dropout).to(args.device)
train_dataset = init_dataset(args.train_path, size=args.resolution)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
collate_fn=collate_fn,
num_workers=args.num_workers,
)
if args.test_path:
test_dataset = init_dataset(args.test_path, size=args.resolution)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
collate_fn=collate_fn,
num_workers=args.num_workers,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)
scaler = torch.cuda.amp.GradScaler()
scheduler1 = torch.optim.lr_scheduler.LinearLR(
optimizer, start_factor=0.001, total_iters=200
)
scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.steps)
scheduler = torch.optim.lr_scheduler.SequentialLR(
optimizer, schedulers=[scheduler1, scheduler2], milestones=[20]
)
params = 0
for p in model.parameters():
params += p.numel()
print(params, "Parameters")
writer = SummaryWriter(comment="resizer")
model.train()
epoch = 0
step = 0
progress_bar = tqdm(range(args.steps))
progress_bar.set_description("Steps")
train_fn = lambda batch: calculate_loss(model, batch, vae, lpips_fn, vae_dtype)
while step < args.steps:
epoch += 1
for batch in train_dataloader:
if batch["img_input"].shape == batch["img_target"].shape:
continue
step += 1
loss, logs = train_fn(batch)
l = loss.detach().cpu().item()
for k in logs.keys():
writer.add_scalar("{}/train".format(k), logs[k], step)
progress_bar.set_postfix(loss=round(l, 2), lr=scheduler.get_last_lr()[0])
scaler.scale(loss).backward()
if 0:
total_norm = 0
for p in model.parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm ** (1.0 / 2)
print("norm", total_norm)
if args.grad_clip > 0:
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
scaler.step(optimizer)
optimizer.zero_grad()
scaler.update()
progress_bar.update(1)
scheduler.step()
if step >= args.steps:
break
if (step % args.save_steps) == 0:
base, ext = os.path.splitext(args.output_filename)
save_filename = f"{base}-{step}{ext}"
torch.save(model.state_dict(), save_filename)
if args.test_path and (step % args.test_steps) == 0:
test_batches = 0
test_logs = defaultdict(float)
test_loss = 0
model.eval()
for batch in test_dataloader:
with torch.inference_mode():
_, logs = loss, logs = train_fn(batch)
test_batches += 1
for k in logs.keys():
test_logs[k] += logs[k]
if test_batches >= args.test_batches:
break
model.train()
for k in test_logs.keys():
writer.add_scalar(
"{}/test".format(k), test_logs[k] / test_batches, step
)
torch.save(model.state_dict(), args.output_filename)
print("Model saved")
================================================
FILE: nn_upscale.py
================================================
import torch
from .latent_resizer import LatentResizer
from comfy import model_management
import os
class NNLatentUpscale:
"""
Upscales SDXL latent using neural network
"""
def __init__(self):
self.local_dir = os.path.dirname(os.path.realpath(__file__))
self.scale_factor = 0.13025
self.dtype = torch.float32
if model_management.should_use_fp16():
self.dtype = torch.float16
self.weight_path = {
"SDXL": os.path.join(self.local_dir, "sdxl_resizer.pt"),
"SD 1.x": os.path.join(self.local_dir, "sd15_resizer.pt"),
}
self.version = "none"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"version": (["SDXL", "SD 1.x"],),
"upscale": (
"FLOAT",
{
"default": 1.5,
"min": 1.0,
"max": 2.0,
"step": 0.01,
"display": "number",
},
),
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
def upscale(self, latent, version, upscale):
device = model_management.get_torch_device()
samples = latent["samples"].to(device=device, dtype=self.dtype)
if version != self.version:
self.model = LatentResizer.load_model(
self.weight_path[version], device, self.dtype
)
self.version = version
self.model.to(device=device)
latent_out = (
self.model(self.scale_factor * samples, scale=upscale) / self.scale_factor
)
if self.dtype != torch.float32:
latent_out = latent_out.to(dtype=torch.float32)
latent_out = latent_out.to(device="cpu")
self.model.to(device=model_management.vae_offload_device())
return ({"samples": latent_out},)
================================================
FILE: sd15_resizer.pt
================================================
[File too large to display: 12.0 MB]
================================================
FILE: sdxl_resizer.pt
================================================
[File too large to display: 12.0 MB]