| | |
| |
|
| | 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 |
| |
|
