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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 | |