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# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
import os
import safetensors.torch as sf
import torch
import torch.nn as nn
import ldm_patched.modules.model_management
from ldm_patched.modules.model_patcher import ModelPatcher
from modules.config import path_vae_approx
class ResBlock(nn.Module):
"""Block with residuals"""
def __init__(self, ch):
super().__init__()
self.join = nn.ReLU()
self.norm = nn.BatchNorm2d(ch)
self.long = nn.Sequential(
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.Dropout(0.1)
)
def forward(self, x):
x = self.norm(x)
return self.join(self.long(x) + x)
class ExtractBlock(nn.Module):
"""Increase no. of channels by [out/in]"""
def __init__(self, ch_in, ch_out):
super().__init__()
self.join = nn.ReLU()
self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
self.long = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
nn.Dropout(0.1)
)
def forward(self, x):
return self.join(self.long(x) + self.short(x))
class InterposerModel(nn.Module):
"""Main neural network"""
def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):
super().__init__()
self.ch_in = ch_in
self.ch_out = ch_out
self.ch_mid = ch_mid
self.blocks = blocks
self.scale = scale
self.head = ExtractBlock(self.ch_in, self.ch_mid)
self.core = nn.Sequential(
nn.Upsample(scale_factor=self.scale, mode="nearest"),
*[ResBlock(self.ch_mid) for _ in range(blocks)],
nn.BatchNorm2d(self.ch_mid),
nn.SiLU(),
)
self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
def forward(self, x):
y = self.head(x)
z = self.core(y)
return self.tail(z)
vae_approx_model = None
vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors')
def parse(x):
global vae_approx_model
x_origin = x.clone()
if vae_approx_model is None:
model = InterposerModel()
model.eval()
sd = sf.load_file(vae_approx_filename)
model.load_state_dict(sd)
fp16 = ldm_patched.modules.model_management.should_use_fp16()
if fp16:
model = model.half()
vae_approx_model = ModelPatcher(
model=model,
load_device=ldm_patched.modules.model_management.get_torch_device(),
offload_device=torch.device('cpu')
)
vae_approx_model.dtype = torch.float16 if fp16 else torch.float32
ldm_patched.modules.model_management.load_model_gpu(vae_approx_model)
x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype)
x = vae_approx_model.model(x).to(x_origin)
return x
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