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import os |
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import safetensors.torch as sf |
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import torch |
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import torch.nn as nn |
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import ldm_patched.modules.model_management |
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from ldm_patched.modules.model_patcher import ModelPatcher |
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from modules.config import path_vae_approx |
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class ResBlock(nn.Module): |
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"""Block with residuals""" |
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def __init__(self, ch): |
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super().__init__() |
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self.join = nn.ReLU() |
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self.norm = nn.BatchNorm2d(ch) |
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self.long = nn.Sequential( |
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nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), |
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nn.SiLU(), |
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nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), |
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nn.SiLU(), |
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nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), |
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nn.Dropout(0.1) |
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) |
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def forward(self, x): |
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x = self.norm(x) |
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return self.join(self.long(x) + x) |
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class ExtractBlock(nn.Module): |
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"""Increase no. of channels by [out/in]""" |
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def __init__(self, ch_in, ch_out): |
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super().__init__() |
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self.join = nn.ReLU() |
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self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1) |
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self.long = nn.Sequential( |
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nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1), |
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nn.SiLU(), |
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nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1), |
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nn.SiLU(), |
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nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1), |
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nn.Dropout(0.1) |
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) |
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def forward(self, x): |
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return self.join(self.long(x) + self.short(x)) |
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class InterposerModel(nn.Module): |
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"""Main neural network""" |
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def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12): |
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super().__init__() |
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self.ch_in = ch_in |
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self.ch_out = ch_out |
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self.ch_mid = ch_mid |
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self.blocks = blocks |
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self.scale = scale |
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self.head = ExtractBlock(self.ch_in, self.ch_mid) |
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self.core = nn.Sequential( |
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nn.Upsample(scale_factor=self.scale, mode="nearest"), |
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*[ResBlock(self.ch_mid) for _ in range(blocks)], |
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nn.BatchNorm2d(self.ch_mid), |
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nn.SiLU(), |
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) |
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self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1) |
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def forward(self, x): |
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y = self.head(x) |
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z = self.core(y) |
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return self.tail(z) |
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vae_approx_model = None |
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vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors') |
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def parse(x): |
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global vae_approx_model |
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x_origin = x.clone() |
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if vae_approx_model is None: |
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model = InterposerModel() |
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model.eval() |
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sd = sf.load_file(vae_approx_filename) |
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model.load_state_dict(sd) |
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fp16 = ldm_patched.modules.model_management.should_use_fp16() |
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if fp16: |
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model = model.half() |
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vae_approx_model = ModelPatcher( |
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model=model, |
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load_device=ldm_patched.modules.model_management.get_torch_device(), |
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offload_device=torch.device('cpu') |
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) |
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vae_approx_model.dtype = torch.float16 if fp16 else torch.float32 |
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ldm_patched.modules.model_management.load_model_gpu(vae_approx_model) |
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x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype) |
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x = vae_approx_model.model(x).to(x_origin) |
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return x |
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