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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 | |