| |
|
|
| 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 |
|
|
