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# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py
import os
import torch
import safetensors.torch as sf
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 Block(nn.Module):
def __init__(self, size):
super().__init__()
self.join = nn.ReLU()
self.long = nn.Sequential(
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.1),
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.1),
nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1),
)
def forward(self, x):
y = self.long(x)
z = self.join(y + x)
return z
class Interposer(nn.Module):
def __init__(self):
super().__init__()
self.chan = 4
self.hid = 128
self.head_join = nn.ReLU()
self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1)
self.head_long = nn.Sequential(
nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.1),
nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.1),
nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1),
)
self.core = nn.Sequential(
Block(self.hid),
Block(self.hid),
Block(self.hid),
)
self.tail = nn.Sequential(
nn.ReLU(),
nn.Conv2d(self.hid, self.chan, kernel_size=3, stride=1, padding=1)
)
def forward(self, x):
y = self.head_join(
self.head_long(x) +
self.head_short(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-v3.1.safetensors')
def parse(x):
global vae_approx_model
x_origin = x.clone()
if vae_approx_model is None:
model = Interposer()
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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