453 lines
17 KiB
Python
453 lines
17 KiB
Python
import torch.nn as nn
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import torch
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import cv2
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import numpy as np
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import safetensors.torch as sf
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from accelerate.logging import get_logger
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logger = get_logger(__name__, log_level="INFO")
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from tqdm import tqdm
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from typing import Optional, Tuple
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.models.modeling_utils import ModelMixin
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from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
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from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
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import torchvision
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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class LatentTransparencyOffsetEncoder(torch.nn.Module):
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def __init__(self, latent_c=4, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.blocks = torch.nn.Sequential(
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torch.nn.Conv2d(4, 32, kernel_size=3, padding=1, stride=1),
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nn.SiLU(),
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torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
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nn.SiLU(),
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torch.nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2),
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nn.SiLU(),
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torch.nn.Conv2d(64, 64, kernel_size=3, padding=1, stride=1),
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nn.SiLU(),
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torch.nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=2),
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nn.SiLU(),
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torch.nn.Conv2d(128, 128, kernel_size=3, padding=1, stride=1),
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nn.SiLU(),
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torch.nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),
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nn.SiLU(),
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torch.nn.Conv2d(256, 256, kernel_size=3, padding=1, stride=1),
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nn.SiLU(),
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zero_module(torch.nn.Conv2d(256, latent_c, kernel_size=3, padding=1, stride=1)),
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)
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def __call__(self, x):
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return self.blocks(x)
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# 1024 * 1024 * 3 -> 16 * 16 * 512 -> 1024 * 1024 * 3
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class UNet1024(ModelMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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down_block_types: Tuple[str] = (
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"DownBlock2D", "DownBlock2D", "DownBlock2D", "DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D",
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"AttnDownBlock2D"),
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up_block_types: Tuple[str] = (
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"AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D"),
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block_out_channels: Tuple[int] = (32, 32, 64, 128, 256, 512, 512),
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layers_per_block: int = 2,
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mid_block_scale_factor: float = 1,
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downsample_padding: int = 1,
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downsample_type: str = "conv",
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upsample_type: str = "conv",
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dropout: float = 0.0,
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act_fn: str = "silu",
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attention_head_dim: Optional[int] = 8,
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norm_num_groups: int = 4,
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norm_eps: float = 1e-5,
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latent_c: int = 4,
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):
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super().__init__()
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# input
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self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
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self.latent_conv_in = zero_module(nn.Conv2d(latent_c, block_out_channels[2], kernel_size=1))
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self.down_blocks = nn.ModuleList([])
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self.mid_block = None
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self.up_blocks = nn.ModuleList([])
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# down
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output_channel = block_out_channels[0]
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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down_block = get_down_block(
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down_block_type,
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num_layers=layers_per_block,
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in_channels=input_channel,
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out_channels=output_channel,
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temb_channels=None,
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add_downsample=not is_final_block,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift="default",
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downsample_type=downsample_type,
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dropout=dropout,
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)
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self.down_blocks.append(down_block)
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# mid
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self.mid_block = UNetMidBlock2D(
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in_channels=block_out_channels[-1],
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temb_channels=None,
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dropout=dropout,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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output_scale_factor=mid_block_scale_factor,
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resnet_time_scale_shift="default",
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attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
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resnet_groups=norm_num_groups,
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attn_groups=None,
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add_attention=True,
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)
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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output_channel = reversed_block_out_channels[0]
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for i, up_block_type in enumerate(up_block_types):
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
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is_final_block = i == len(block_out_channels) - 1
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up_block = get_up_block(
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up_block_type,
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num_layers=layers_per_block + 1,
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in_channels=input_channel,
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out_channels=output_channel,
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prev_output_channel=prev_output_channel,
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temb_channels=None,
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add_upsample=not is_final_block,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
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resnet_time_scale_shift="default",
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upsample_type=upsample_type,
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dropout=dropout,
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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# out
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
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self.conv_act = nn.SiLU()
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self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
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def forward(self, x, latent):
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sample_latent = self.latent_conv_in(latent)
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sample = self.conv_in(x)
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emb = None
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down_block_res_samples = (sample,)
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for i, downsample_block in enumerate(self.down_blocks):
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if i == 3:
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sample = sample + sample_latent
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sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
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down_block_res_samples += res_samples
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sample = self.mid_block(sample, emb)
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for upsample_block in self.up_blocks:
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res_samples = down_block_res_samples[-len(upsample_block.resnets):]
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down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
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sample = upsample_block(sample, res_samples, emb)
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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return sample
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def checkerboard(shape):
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return np.indices(shape).sum(axis=0) % 2
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def build_alpha_pyramid(color, alpha, dk=1.2):
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# Written by lvmin at Stanford
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# Massive iterative Gaussian filters are mathematically consistent to pyramid.
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pyramid = []
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current_premultiplied_color = color * alpha
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current_alpha = alpha
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while True:
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pyramid.append((current_premultiplied_color, current_alpha))
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H, W, C = current_alpha.shape
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if min(H, W) == 1:
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break
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current_premultiplied_color = cv2.resize(current_premultiplied_color, (int(W / dk), int(H / dk)),
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interpolation=cv2.INTER_AREA)
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current_alpha = cv2.resize(current_alpha, (int(W / dk), int(H / dk)), interpolation=cv2.INTER_AREA)[:, :, None]
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return pyramid[::-1]
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def pad_rgb(np_rgba_hwc_uint8):
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# Written by lvmin at Stanford
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# Massive iterative Gaussian filters are mathematically consistent to pyramid.
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np_rgba_hwc = np_rgba_hwc_uint8.astype(np.float32) # / 255.0
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pyramid = build_alpha_pyramid(color=np_rgba_hwc[..., :3], alpha=np_rgba_hwc[..., 3:])
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top_c, top_a = pyramid[0]
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fg = np.sum(top_c, axis=(0, 1), keepdims=True) / np.sum(top_a, axis=(0, 1), keepdims=True).clip(1e-8, 1e32)
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for layer_c, layer_a in pyramid:
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layer_h, layer_w, _ = layer_c.shape
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fg = cv2.resize(fg, (layer_w, layer_h), interpolation=cv2.INTER_LINEAR)
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fg = layer_c + fg * (1.0 - layer_a)
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return fg
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def dist_sample_deterministic(dist: DiagonalGaussianDistribution, perturbation: torch.Tensor):
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# Modified from diffusers.models.autoencoders.vae.DiagonalGaussianDistribution.sample()
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x = dist.mean + dist.std * perturbation.to(dist.std)
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return x
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class TransparentVAE(torch.nn.Module):
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def __init__(self, sd_vae, dtype=torch.float16, encoder_file=None, decoder_file=None, alpha=300.0, latent_c=16,
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*args, **kwargs):
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super().__init__(*args, **kwargs)
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self.dtype = dtype
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self.sd_vae = sd_vae
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self.sd_vae.to(dtype=self.dtype)
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self.sd_vae.requires_grad_(False)
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self.encoder = LatentTransparencyOffsetEncoder(latent_c=latent_c)
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if encoder_file is not None:
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temp = sf.load_file(encoder_file)
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# del temp['blocks.16.weight']
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# del temp['blocks.16.bias']
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self.encoder.load_state_dict(temp, strict=True)
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del temp
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self.encoder.to(dtype=self.dtype)
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self.alpha = alpha
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self.decoder = UNet1024(in_channels=3, out_channels=4, latent_c=latent_c)
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if decoder_file is not None:
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temp = sf.load_file(decoder_file)
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# del temp['latent_conv_in.weight']
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# del temp['latent_conv_in.bias']
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self.decoder.load_state_dict(temp, strict=True)
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del temp
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self.decoder.to(dtype=self.dtype)
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self.latent_c = latent_c
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def sd_decode(self, latent):
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return self.sd_vae.decode(latent)
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def decode(self, latent, aug=True):
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origin_pixel = self.sd_vae.decode(latent).sample
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origin_pixel = (origin_pixel * 0.5 + 0.5)
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if not aug:
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y = self.decoder(origin_pixel.to(self.dtype), latent.to(self.dtype))
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return origin_pixel, y
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list_y = []
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for i in range(int(latent.shape[0])):
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y = self.estimate_augmented(origin_pixel[i:i + 1].to(self.dtype), latent[i:i + 1].to(self.dtype))
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list_y.append(y)
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y = torch.concat(list_y, dim=0)
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return origin_pixel, y
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def encode(self, img_rgba, img_rgb, padded_img_rgb, use_offset=True):
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a_bchw_01 = img_rgba[:, 3:, :, :]
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vae_feed = img_rgb.to(device=self.sd_vae.device, dtype=self.sd_vae.dtype)
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latent_dist = self.sd_vae.encode(vae_feed).latent_dist
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offset_feed = torch.cat([padded_img_rgb, a_bchw_01], dim=1).to(device=self.sd_vae.device, dtype=self.dtype)
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offset = self.encoder(offset_feed) * self.alpha
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if use_offset:
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latent = dist_sample_deterministic(dist=latent_dist, perturbation=offset)
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latent = self.sd_vae.config.scaling_factor * (latent - self.sd_vae.config.shift_factor)
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else:
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latent = latent_dist.sample()
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latent = self.sd_vae.config.scaling_factor * (latent - self.sd_vae.config.shift_factor)
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return latent
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def forward(self, img_rgba, img_rgb, padded_img_rgb, use_offset=True):
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return self.decode(self.encode(img_rgba, img_rgb, padded_img_rgb, use_offset))
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@property
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def device(self):
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return next(self.parameters()).device
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@torch.no_grad()
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def estimate_augmented(self, pixel, latent):
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args = [
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[False, 0], [False, 1], [False, 2], [False, 3], [True, 0], [True, 1], [True, 2], [True, 3],
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]
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result = []
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for flip, rok in tqdm(args):
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feed_pixel = pixel.clone()
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feed_latent = latent.clone()
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if flip:
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feed_pixel = torch.flip(feed_pixel, dims=(3,))
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feed_latent = torch.flip(feed_latent, dims=(3,))
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feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
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feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
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eps = self.decoder(feed_pixel, feed_latent).clip(0, 1)
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eps = torch.rot90(eps, k=-rok, dims=(2, 3))
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if flip:
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eps = torch.flip(eps, dims=(3,))
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result += [eps]
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result = torch.stack(result, dim=0)
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median = torch.median(result, dim=0).values
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return median
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class TransparentVAEDecoder(torch.nn.Module):
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def __init__(self, filename, dtype=torch.float16, *args, **kwargs):
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super().__init__(*args, **kwargs)
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sd = sf.load_file(filename)
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model = UNet1024(in_channels=3, out_channels=4)
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model.load_state_dict(sd, strict=True)
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model.to(dtype=dtype)
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model.eval()
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self.model = model
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self.dtype = dtype
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return
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@torch.no_grad()
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def estimate_single_pass(self, pixel, latent):
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y = self.model(pixel, latent)
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return y
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@torch.no_grad()
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def estimate_augmented(self, pixel, latent):
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args = [
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[False, 0], [False, 1], [False, 2], [False, 3], [True, 0], [True, 1], [True, 2], [True, 3],
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]
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result = []
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for flip, rok in tqdm(args):
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feed_pixel = pixel.clone()
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feed_latent = latent.clone()
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if flip:
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feed_pixel = torch.flip(feed_pixel, dims=(3,))
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feed_latent = torch.flip(feed_latent, dims=(3,))
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feed_pixel = torch.rot90(feed_pixel, k=rok, dims=(2, 3))
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feed_latent = torch.rot90(feed_latent, k=rok, dims=(2, 3))
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eps = self.estimate_single_pass(feed_pixel, feed_latent).clip(0, 1)
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eps = torch.rot90(eps, k=-rok, dims=(2, 3))
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if flip:
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eps = torch.flip(eps, dims=(3,))
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result += [eps]
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result = torch.stack(result, dim=0)
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median = torch.median(result, dim=0).values
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return median
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@torch.no_grad()
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def forward(self, sd_vae, latent):
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pixel = sd_vae.decode(latent).sample
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pixel = (pixel * 0.5 + 0.5).clip(0, 1).to(self.dtype)
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latent = latent.to(self.dtype)
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result_list = []
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vis_list = []
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for i in range(int(latent.shape[0])):
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y = self.estimate_augmented(pixel[i:i + 1], latent[i:i + 1])
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y = y.clip(0, 1).movedim(1, -1)
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alpha = y[..., :1]
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fg = y[..., 1:]
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B, H, W, C = fg.shape
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cb = checkerboard(shape=(H // 64, W // 64))
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cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_NEAREST)
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cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None]
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cb = torch.from_numpy(cb).to(fg)
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vis = (fg * alpha + cb * (1 - alpha))[0]
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vis = (vis * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
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vis_list.append(vis)
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png = torch.cat([fg, alpha], dim=3)[0]
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png = (png * 255.0).detach().cpu().float().numpy().clip(0, 255).astype(np.uint8)
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result_list.append(png)
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return result_list, vis_list
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class TransparentVAEEncoder(torch.nn.Module):
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def __init__(self, filename, dtype=torch.float16, alpha=300.0, *args, **kwargs):
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super().__init__(*args, **kwargs)
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sd = sf.load_file(filename)
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self.dtype = dtype
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model = LatentTransparencyOffsetEncoder()
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model.load_state_dict(sd, strict=True)
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model.to(dtype=self.dtype)
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model.eval()
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self.model = model
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# similar to LoRA's alpha to avoid initial zero-initialized outputs being too small
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self.alpha = alpha
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return
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@torch.no_grad()
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def forward(self, sd_vae, list_of_np_rgba_hwc_uint8, use_offset=True):
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list_of_np_rgb_padded = [pad_rgb(x) for x in list_of_np_rgba_hwc_uint8]
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rgb_padded_bchw_01 = torch.from_numpy(np.stack(list_of_np_rgb_padded, axis=0)).float().movedim(-1, 1)
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rgba_bchw_01 = torch.from_numpy(np.stack(list_of_np_rgba_hwc_uint8, axis=0)).float().movedim(-1, 1) / 255.0
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rgb_bchw_01 = rgba_bchw_01[:, :3, :, :]
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a_bchw_01 = rgba_bchw_01[:, 3:, :, :]
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vae_feed = (rgb_bchw_01 * 2.0 - 1.0) * a_bchw_01
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vae_feed = vae_feed.to(device=sd_vae.device, dtype=sd_vae.dtype)
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latent_dist = sd_vae.encode(vae_feed).latent_dist
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offset_feed = torch.cat([a_bchw_01, rgb_padded_bchw_01], dim=1).to(device=sd_vae.device, dtype=self.dtype)
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offset = self.model(offset_feed) * self.alpha
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if use_offset:
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latent = dist_sample_deterministic(dist=latent_dist, perturbation=offset)
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else:
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latent = latent_dist.sample()
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return latent
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