diffsynth/models/qwen_image_vae.py

import torch
from typing import List, Optional, Tuple, Union
from torch import nn


CACHE_T = 2

class QwenImageCausalConv3d(torch.nn.Conv3d):
    r"""
    A custom 3D causal convolution layer with feature caching support.

    This layer extends the standard Conv3D layer by ensuring causality in the time dimension and handling feature
    caching for efficient inference.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int, int]],
        stride: Union[int, Tuple[int, int, int]] = 1,
        padding: Union[int, Tuple[int, int, int]] = 0,
    ) -> None:
        super().__init__(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
        )

        # Set up causal padding
        self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0)
        self.padding = (0, 0, 0)

    def forward(self, x, cache_x=None):
        padding = list(self._padding)
        if cache_x is not None and self._padding[4] > 0:
            cache_x = cache_x.to(x.device)
            x = torch.cat([cache_x, x], dim=2)
            padding[4] -= cache_x.shape[2]
        x = torch.nn.functional.pad(x, padding)
        return super().forward(x)



class QwenImageRMS_norm(nn.Module):
    r"""
    A custom RMS normalization layer.

    Args:
        dim (int): The number of dimensions to normalize over.
        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
            Default is True.
        images (bool, optional): Whether the input represents image data. Default is True.
        bias (bool, optional): Whether to include a learnable bias term. Default is False.
    """

    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
        super().__init__()
        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
        shape = (dim, *broadcastable_dims) if channel_first else (dim,)

        self.channel_first = channel_first
        self.scale = dim**0.5
        self.gamma = nn.Parameter(torch.ones(shape))
        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0

    def forward(self, x):
        return torch.nn.functional.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias



class QwenImageResidualBlock(nn.Module):
    r"""
    A custom residual block module.

    Args:
        in_dim (int): Number of input channels.
        out_dim (int): Number of output channels.
        dropout (float, optional): Dropout rate for the dropout layer. Default is 0.0.
        non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: int,
        dropout: float = 0.0,
        non_linearity: str = "silu",
    ) -> None:
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.nonlinearity = torch.nn.SiLU()

        # layers
        self.norm1 = QwenImageRMS_norm(in_dim, images=False)
        self.conv1 = QwenImageCausalConv3d(in_dim, out_dim, 3, padding=1)
        self.norm2 = QwenImageRMS_norm(out_dim, images=False)
        self.dropout = nn.Dropout(dropout)
        self.conv2 = QwenImageCausalConv3d(out_dim, out_dim, 3, padding=1)
        self.conv_shortcut = QwenImageCausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        # Apply shortcut connection
        h = self.conv_shortcut(x)

        # First normalization and activation
        x = self.norm1(x)
        x = self.nonlinearity(x)

        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)

            x = self.conv1(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv1(x)

        # Second normalization and activation
        x = self.norm2(x)
        x = self.nonlinearity(x)

        # Dropout
        x = self.dropout(x)

        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)

            x = self.conv2(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv2(x)

        # Add residual connection
        return x + h



class QwenImageAttentionBlock(nn.Module):
    r"""
    Causal self-attention with a single head.

    Args:
        dim (int): The number of channels in the input tensor.
    """

    def __init__(self, dim):
        super().__init__()
        self.dim = dim

        # layers
        self.norm = QwenImageRMS_norm(dim)
        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
        self.proj = nn.Conv2d(dim, dim, 1)

    def forward(self, x):
        identity = x
        batch_size, channels, time, height, width = x.size()

        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * time, channels, height, width)
        x = self.norm(x)

        # compute query, key, value
        qkv = self.to_qkv(x)
        qkv = qkv.reshape(batch_size * time, 1, channels * 3, -1)
        qkv = qkv.permute(0, 1, 3, 2).contiguous()
        q, k, v = qkv.chunk(3, dim=-1)

        # apply attention
        x = torch.nn.functional.scaled_dot_product_attention(q, k, v)

        x = x.squeeze(1).permute(0, 2, 1).reshape(batch_size * time, channels, height, width)

        # output projection
        x = self.proj(x)

        # Reshape back: [(b*t), c, h, w] -> [b, c, t, h, w]
        x = x.view(batch_size, time, channels, height, width)
        x = x.permute(0, 2, 1, 3, 4)

        return x + identity



class QwenImageUpsample(nn.Upsample):
    r"""
    Perform upsampling while ensuring the output tensor has the same data type as the input.

    Args:
        x (torch.Tensor): Input tensor to be upsampled.

    Returns:
        torch.Tensor: Upsampled tensor with the same data type as the input.
    """

    def forward(self, x):
        return super().forward(x.float()).type_as(x)



class QwenImageResample(nn.Module):
    r"""
    A custom resampling module for 2D and 3D data.

    Args:
        dim (int): The number of input/output channels.
        mode (str): The resampling mode. Must be one of:
            - 'none': No resampling (identity operation).
            - 'upsample2d': 2D upsampling with nearest-exact interpolation and convolution.
            - 'upsample3d': 3D upsampling with nearest-exact interpolation, convolution, and causal 3D convolution.
            - 'downsample2d': 2D downsampling with zero-padding and convolution.
            - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
    """

    def __init__(self, dim: int, mode: str) -> None:
        super().__init__()
        self.dim = dim
        self.mode = mode

        # layers
        if mode == "upsample2d":
            self.resample = nn.Sequential(
                QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
            )
        elif mode == "upsample3d":
            self.resample = nn.Sequential(
                QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
            )
            self.time_conv = QwenImageCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))

        elif mode == "downsample2d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
        elif mode == "downsample3d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
            self.time_conv = QwenImageCausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))

        else:
            self.resample = nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        b, c, t, h, w = x.size()
        if self.mode == "upsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = "Rep"
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
                        # cache last frame of last two chunk
                        cache_x = torch.cat(
                            [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2
                        )
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
                        cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
                    if feat_cache[idx] == "Rep":
                        x = self.time_conv(x)
                    else:
                        x = self.time_conv(x, feat_cache[idx])
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1

                    x = x.reshape(b, 2, c, t, h, w)
                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
                    x = x.reshape(b, c, t * 2, h, w)
        t = x.shape[2]
        x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
        x = self.resample(x)
        x = x.view(b, t, x.size(1), x.size(2), x.size(3)).permute(0, 2, 1, 3, 4)

        if self.mode == "downsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = x.clone()
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -1:, :, :].clone()
                    x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1
        return x



class QwenImageMidBlock(nn.Module):
    """
    Middle block for WanVAE encoder and decoder.

    Args:
        dim (int): Number of input/output channels.
        dropout (float): Dropout rate.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(self, dim: int, dropout: float = 0.0, non_linearity: str = "silu", num_layers: int = 1):
        super().__init__()
        self.dim = dim

        # Create the components
        resnets = [QwenImageResidualBlock(dim, dim, dropout, non_linearity)]
        attentions = []
        for _ in range(num_layers):
            attentions.append(QwenImageAttentionBlock(dim))
            resnets.append(QwenImageResidualBlock(dim, dim, dropout, non_linearity))
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        # First residual block
        x = self.resnets[0](x, feat_cache, feat_idx)

        # Process through attention and residual blocks
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if attn is not None:
                x = attn(x)

            x = resnet(x, feat_cache, feat_idx)

        return x



class QwenImageEncoder3d(nn.Module):
    r"""
    A 3D encoder module.

    Args:
        dim (int): The base number of channels in the first layer.
        z_dim (int): The dimensionality of the latent space.
        dim_mult (list of int): Multipliers for the number of channels in each block.
        num_res_blocks (int): Number of residual blocks in each block.
        attn_scales (list of float): Scales at which to apply attention mechanisms.
        temperal_downsample (list of bool): Whether to downsample temporally in each block.
        dropout (float): Dropout rate for the dropout layers.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(
        self,
        dim=128,
        z_dim=4,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_downsample=[True, True, False],
        dropout=0.0,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_downsample = temperal_downsample
        self.nonlinearity = torch.nn.SiLU()

        # dimensions
        dims = [dim * u for u in [1] + dim_mult]
        scale = 1.0

        # init block
        self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1)

        # downsample blocks
        self.down_blocks = torch.nn.ModuleList([])
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            for _ in range(num_res_blocks):
                self.down_blocks.append(QwenImageResidualBlock(in_dim, out_dim, dropout))
                if scale in attn_scales:
                    self.down_blocks.append(QwenImageAttentionBlock(out_dim))
                in_dim = out_dim

            # downsample block
            if i != len(dim_mult) - 1:
                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
                self.down_blocks.append(QwenImageResample(out_dim, mode=mode))
                scale /= 2.0

        # middle blocks
        self.mid_block = QwenImageMidBlock(out_dim, dropout, non_linearity, num_layers=1)

        # output blocks
        self.norm_out = QwenImageRMS_norm(out_dim, images=False)
        self.conv_out = QwenImageCausalConv3d(out_dim, z_dim, 3, padding=1)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_in(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_in(x)

        ## downsamples
        for layer in self.down_blocks:
            if feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## middle
        x = self.mid_block(x, feat_cache, feat_idx)

        ## head
        x = self.norm_out(x)
        x = self.nonlinearity(x)
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_out(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_out(x)
        return x



class QwenImageUpBlock(nn.Module):
    """
    A block that handles upsampling for the WanVAE decoder.

    Args:
        in_dim (int): Input dimension
        out_dim (int): Output dimension
        num_res_blocks (int): Number of residual blocks
        dropout (float): Dropout rate
        upsample_mode (str, optional): Mode for upsampling ('upsample2d' or 'upsample3d')
        non_linearity (str): Type of non-linearity to use
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: int,
        num_res_blocks: int,
        dropout: float = 0.0,
        upsample_mode: Optional[str] = None,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim

        # Create layers list
        resnets = []
        # Add residual blocks and attention if needed
        current_dim = in_dim
        for _ in range(num_res_blocks + 1):
            resnets.append(QwenImageResidualBlock(current_dim, out_dim, dropout, non_linearity))
            current_dim = out_dim

        self.resnets = nn.ModuleList(resnets)

        # Add upsampling layer if needed
        self.upsamplers = None
        if upsample_mode is not None:
            self.upsamplers = nn.ModuleList([QwenImageResample(out_dim, mode=upsample_mode)])

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        """
        Forward pass through the upsampling block.

        Args:
            x (torch.Tensor): Input tensor
            feat_cache (list, optional): Feature cache for causal convolutions
            feat_idx (list, optional): Feature index for cache management

        Returns:
            torch.Tensor: Output tensor
        """
        for resnet in self.resnets:
            if feat_cache is not None:
                x = resnet(x, feat_cache, feat_idx)
            else:
                x = resnet(x)

        if self.upsamplers is not None:
            if feat_cache is not None:
                x = self.upsamplers[0](x, feat_cache, feat_idx)
            else:
                x = self.upsamplers[0](x)
        return x



class QwenImageDecoder3d(nn.Module):
    r"""
    A 3D decoder module.

    Args:
        dim (int): The base number of channels in the first layer.
        z_dim (int): The dimensionality of the latent space.
        dim_mult (list of int): Multipliers for the number of channels in each block.
        num_res_blocks (int): Number of residual blocks in each block.
        attn_scales (list of float): Scales at which to apply attention mechanisms.
        temperal_upsample (list of bool): Whether to upsample temporally in each block.
        dropout (float): Dropout rate for the dropout layers.
        non_linearity (str): Type of non-linearity to use.
    """

    def __init__(
        self,
        dim=128,
        z_dim=4,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_upsample=[False, True, True],
        dropout=0.0,
        non_linearity: str = "silu",
    ):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_upsample = temperal_upsample

        self.nonlinearity = torch.nn.SiLU()

        # dimensions
        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
        scale = 1.0 / 2 ** (len(dim_mult) - 2)

        # init block
        self.conv_in = QwenImageCausalConv3d(z_dim, dims[0], 3, padding=1)

        # middle blocks
        self.mid_block = QwenImageMidBlock(dims[0], dropout, non_linearity, num_layers=1)

        # upsample blocks
        self.up_blocks = nn.ModuleList([])
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            if i > 0:
                in_dim = in_dim // 2

            # Determine if we need upsampling
            upsample_mode = None
            if i != len(dim_mult) - 1:
                upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"

            # Create and add the upsampling block
            up_block = QwenImageUpBlock(
                in_dim=in_dim,
                out_dim=out_dim,
                num_res_blocks=num_res_blocks,
                dropout=dropout,
                upsample_mode=upsample_mode,
                non_linearity=non_linearity,
            )
            self.up_blocks.append(up_block)

            # Update scale for next iteration
            if upsample_mode is not None:
                scale *= 2.0

        # output blocks
        self.norm_out = QwenImageRMS_norm(out_dim, images=False)
        self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1)

        self.gradient_checkpointing = False

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        ## conv1
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_in(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_in(x)

        ## middle
        x = self.mid_block(x, feat_cache, feat_idx)

        ## upsamples
        for up_block in self.up_blocks:
            x = up_block(x, feat_cache, feat_idx)

        ## head
        x = self.norm_out(x)
        x = self.nonlinearity(x)
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
            x = self.conv_out(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv_out(x)
        return x



class QwenImageVAE(torch.nn.Module):
    def __init__(
        self,
        base_dim: int = 96,
        z_dim: int = 16,
        dim_mult: Tuple[int] = [1, 2, 4, 4],
        num_res_blocks: int = 2,
        attn_scales: List[float] = [],
        temperal_downsample: List[bool] = [False, True, True],
        dropout: float = 0.0,
    ) -> None:
        super().__init__()

        self.z_dim = z_dim
        self.temperal_downsample = temperal_downsample
        self.temperal_upsample = temperal_downsample[::-1]

        self.encoder = QwenImageEncoder3d(
            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
        )
        self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1)
        self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1)

        self.decoder = QwenImageDecoder3d(
            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
        )

        mean = [
            -0.7571,
            -0.7089,
            -0.9113,
            0.1075,
            -0.1745,
            0.9653,
            -0.1517,
            1.5508,
            0.4134,
            -0.0715,
            0.5517,
            -0.3632,
            -0.1922,
            -0.9497,
            0.2503,
            -0.2921,
        ]
        std = [
            2.8184,
            1.4541,
            2.3275,
            2.6558,
            1.2196,
            1.7708,
            2.6052,
            2.0743,
            3.2687,
            2.1526,
            2.8652,
            1.5579,
            1.6382,
            1.1253,
            2.8251,
            1.9160,
        ]
        self.mean = torch.tensor(mean).view(1, 16, 1, 1, 1)
        self.std = 1 / torch.tensor(std).view(1, 16, 1, 1, 1)

    def encode(self, x, **kwargs):
        x = x.unsqueeze(2)
        x = self.encoder(x)
        x = self.quant_conv(x)
        x = x[:, :16]
        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
        x = (x - mean) * std
        x = x.squeeze(2)
        return x
    
    def decode(self, x, **kwargs):
        x = x.unsqueeze(2)
        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
        x = x / std + mean
        x = self.post_quant_conv(x)
        x = self.decoder(x)
        x = x.squeeze(2)
        return x


# import torch
# import numpy as np
# from PIL import Image
# from einops import repeat
# from safetensors.torch import load_file
# import os



# # ================= 配置参数 =================
# # VAE 权重路径
# VAE_CHECKPOINT = "/root/workspace/data/lzh/models/Qwen-Image-Edit-2509/vae/diffusion_pytorch_model.safetensors"
# # 【请修改此处】真实的输入图像路径
# IMAGE_PATH = "/root/workspace/lzh/my-DiffSynth-Studio/test_lora_image1.jpg"  # <--- 请替换为真实的图片路径

# # 设备配置
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# DTYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
# # ===========================================

# def preprocess_image(image, torch_dtype=torch.float32, device="cuda", pattern="B C H W", min_value=-1, max_value=1):
#     """
#     复用你提供的预处理逻辑
#     """
#     image = torch.Tensor(np.array(image, dtype=np.float32))
#     image = image.to(dtype=torch_dtype, device=device)
#     image = image * ((max_value - min_value) / 255) + min_value
#     # 处理 H W C -> B C H W
#     if image.ndim == 3: 
#         image = repeat(image, f"H W C -> {pattern}", **({"B": 1} if "B" in pattern else {}))
#     return image

# def denormalize_and_save(tensor, filename):
#     """
#     将 [-1, 1] 的 Tensor 还原为图片并保存
#     """
#     tensor = tensor.float().cpu().detach()
#     tensor = (tensor.clamp(-1, 1) + 1) / 2 * 255
#     tensor = tensor.to(torch.uint8)
#     if tensor.dim() == 4:
#         tensor = tensor[0] # remove batch
#     # C H W -> H W C
#     array = tensor.permute(1, 2, 0).numpy()
#     Image.fromarray(array).save(filename)
#     print(f"Saved: {filename}")

# def main():
#     print(f"Running verification on device: {DEVICE}")

#     # 1. 初始化并加载模型
#     print(f"Loading VAE from {VAE_CHECKPOINT}...")
#     # 使用默认参数初始化，与 qwen_image_vae.py 保持一致
#     vae = QwenImageVAE(
#         base_dim=96,
#         z_dim=16,
#         dim_mult=[1, 2, 4, 4],
#         num_res_blocks=2,
#         temperal_downsample=[False, True, True]
#     ).to(DEVICE, dtype=DTYPE)
    
#     if not os.path.exists(VAE_CHECKPOINT):
#         print(f"Error: 权重文件不存在: {VAE_CHECKPOINT}")
#         return

#     # 加载 Safetensors 权重
#     state_dict = load_file(VAE_CHECKPOINT)
#     missing, unexpected = vae.load_state_dict(state_dict, strict=False)
#     if len(missing) > 0:
#         print(f"Warning: Missing keys (可能是这是正常的): {missing[:5]} ...")
#     vae.eval()

#     # 2. 读取并预处理图像
#     if not os.path.exists(IMAGE_PATH):
#         print(f"Error: 图片文件不存在: {IMAGE_PATH}")
#         return

#     pil_image = Image.open(IMAGE_PATH).convert("RGB")
#     w, h = pil_image.size
#     # 裁剪图像尺寸为 16 的倍数，方便计算 (Latent downsample factor = 8)
#     new_w = (w // 16) * 16
#     new_h = (h // 16) * 16
#     pil_image = pil_image.resize((new_w, new_h))
#     print(f"Image loaded and resized to: {new_w}x{new_h}")

#     input_tensor = preprocess_image(pil_image, torch_dtype=DTYPE, device=DEVICE)

#     # 3. 编码完整图像
#     with torch.no_grad():
#         # encode 返回的是 (x - mean) * std，已经是 Latent 分布的采样结果
#         latent_full = vae.encode(input_tensor)
    
#     print(f"Full Latent Shape: {latent_full.shape}") # Expect: [1, 16, H/8, W/8]

#     # 4. 实验：切取 Latent 子块 (Center Crop)
#     # 目标：在 Latent 空间切取中间 16x16 的区域
#     # 这对应原图空间 128x128 的区域
#     latent_crop_size = 16
#     _, _, lh, lw = latent_full.shape
    
#     y_start = lh // 2 - latent_crop_size // 2
#     x_start = lw // 2 - latent_crop_size // 2
    
#     # 【关键步骤】直接对 Latent 进行切片
#     latent_crop = latent_full[:, :, y_start : y_start + 2*latent_crop_size, x_start : x_start + 2*latent_crop_size]
#     print(f"Cropped Latent Shape: {latent_crop.shape}")

#     # 5. 解码 Latent 子块
#     with torch.no_grad():
#         recon_crop = vae.decode(latent_crop)
    
#     print(f"Reconstructed Crop Shape: {recon_crop.shape}") # Expect: [1, 3, 128, 128]

#     # 6. 获取原图对应的真实切片 (Ground Truth)
#     stride = 8 # VAE 下采样倍率
#     pixel_crop_size = latent_crop_size * stride*2 # 16 * 8 = 128
    
#     pixel_y_start = y_start * stride
#     pixel_x_start = x_start * stride
    
#     orig_crop = input_tensor[:, :, pixel_y_start : pixel_y_start + pixel_crop_size, pixel_x_start : pixel_x_start + pixel_crop_size]

#     # 7. 计算差异并保存
#     # 计算 L1 误差
#     l1_diff = torch.nn.functional.l1_loss(recon_crop, orig_crop).item()
#     print(f"\n====== 验证结果 ======")
#     print(f"L1 Pixel Difference: {l1_diff:.5f}")
    
#     # 保存图片以供人工检查
#     denormalize_and_save(recon_crop, "crop_reconstructed.jpg")
#     denormalize_and_save(orig_crop, "crop_original.jpg")

#     # 结论判断
#     if l1_diff < 0.15:
#         print("\n✅ 验证成功：Latent 子块成功重建了原图的对应区域。")
#         print("说明：差异很小，证明 Latent 的空间位置与原图是对应的。")
#     else:
#         print("\n⚠️ 验证存疑：差异较大。")
#         print("可能原因：")
#         print("1. 边界效应：因为切掉了 Latent 的邻域，卷积无法获取边缘信息，导致重建图边缘失真（正常现象）。")
#         print("2. 权重不匹配或预处理逻辑差异。")
    
#     # 计算去除边缘后的中心区域差异 (排除 Padding 带来的边界效应)
#     # 去除周围 4 个像素的边框
#     margin = 4
#     recon_center = recon_crop[:, :, margin:-margin, margin:-margin]
#     orig_center = orig_crop[:, :, margin:-margin, margin:-margin]
#     l1_diff_center = torch.nn.functional.l1_loss(recon_center, orig_center).item()
#     print(f"Center Region L1 Difference (ignoring borders): {l1_diff_center:.5f}")

# if __name__ == "__main__":
#     main()