| import torch as t, torch.nn as nn, torch.nn.functional as F |
| def C(n_in, n_out, **kwargs): |
| return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) |
| class Clamp(nn.Module): |
| def forward(self, x): |
| return t.tanh(x / 3) * 3 |
| class B(nn.Module): |
| def __init__(self, n_in, n_out): |
| super().__init__() |
| self.conv = nn.Sequential(C(n_in, n_out), nn.ReLU(), C(n_out, n_out), nn.ReLU(), C(n_out, n_out)) |
| self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() |
| self.fuse = nn.ReLU() |
| def forward(self, x): |
| return self.fuse(self.conv(x) + self.skip(x)) |
| def E(latent_channels=4): |
| return nn.Sequential( |
| C(3, 64), B(64, 64), |
| C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64), |
| C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64), |
| C(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64), |
| C(64, latent_channels), |
| ) |
| def D(latent_channels=16): |
| return nn.Sequential( |
| Clamp(), |
| C(latent_channels, 48),nn.ReLU(),B(48, 48), B(48, 48), |
| nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48), B(48, 48), |
| nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48), |
| nn.Upsample(scale_factor=2), C(48, 48, bias=False),B(48, 48), |
| C(48, 3), |
| ) |
| class M(nn.Module): |
| lm, ls = 3, 0.5 |
| def __init__(s, ep="encoder.pth", dp="decoder.pth", lc=None): |
| super().__init__() |
| if lc is None: lc = s.glc(str(ep)) |
| s.e, s.d = E(lc), D(lc) |
| def f(sd, mod, pfx): |
| f_sd = {k.strip(pfx): v for k, v in sd.items() if k.strip(pfx) in mod.state_dict() and v.size() == mod.state_dict()[k.strip(pfx)].size()} |
| mod.load_state_dict(f_sd, strict=False) |
| if ep: f(t.load(ep, map_location="cpu", weights_only=True), s.e, "encoder.") |
| if dp: f(t.load(dp, map_location="cpu", weights_only=True), s.d, "decoder.") |
| s.e.requires_grad_(False) |
| s.d.requires_grad_(False) |
| def glc(s, ep): return 16 if "taef1" in ep or "taesd3" in ep else 4 |
| @staticmethod |
| def sl(x): return x.div(2 * M.lm).add(M.ls).clamp(0, 1) |
| @staticmethod |
| def ul(x): return x.sub(M.ls).mul(2 * M.lm) |
| def forward(s, x, rl=False): |
| l, o = s.e(x), s.d(s.e(x)) |
| return (o.clamp(0, 1), l) if rl else o.clamp(0, 1) |
