Merge pull request #1084 from modelscope/diffsynth-2.0-zimage

z-image
2026-03-18 22:08:13 +00:00 · 2025-11-27 22:44:24 +08:00
parent fb892bd860 0b72c2b3ba
commit 1a6fd69e6b
10 changed files with 1329 additions and 17 deletions
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -429,6 +429,7 @@ flux_series = [
        "extra_kwargs": {"disable_guidance_embedder": True},
    },
 ]
+
 flux2_series = [
    {
        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="text_encoder/*.safetensors")
@@ -451,4 +452,35 @@ flux2_series = [
    },
 ]

-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series
+z_image_series = [
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "fc3a8a1247fe185ce116ccbe0e426c28",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "0f050f62a88876fea6eae0a18dac5a2e",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "1aafa3cc91716fb6b300cc1cd51b85a3",
+        "model_name": "flux_vae_encoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEEncoderStateDictConverterDiffusers",
+        "extra_kwargs": {"use_conv_attention": False},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "1aafa3cc91716fb6b300cc1cd51b85a3",
+        "model_name": "flux_vae_decoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEDecoderStateDictConverterDiffusers",
+        "extra_kwargs": {"use_conv_attention": False},
+    },
+]
+
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series
--- a/diffsynth/models/flux_vae.py
+++ b/diffsynth/models/flux_vae.py
@@ -150,25 +150,75 @@ class ConvAttention(torch.nn.Module):
        return hidden_states


+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+
 class VAEAttentionBlock(torch.nn.Module):

-    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5, use_conv_attention=True):
        super().__init__()
        inner_dim = num_attention_heads * attention_head_dim

        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)

-        self.transformer_blocks = torch.nn.ModuleList([
-            ConvAttention(
-                inner_dim,
-                num_attention_heads,
-                attention_head_dim,
-                bias_q=True,
-                bias_kv=True,
-                bias_out=True
-            )
-            for d in range(num_layers)
-        ])
+        if use_conv_attention:
+            self.transformer_blocks = torch.nn.ModuleList([
+                ConvAttention(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    bias_q=True,
+                    bias_kv=True,
+                    bias_out=True
+                )
+                for d in range(num_layers)
+            ])
+        else:
+            self.transformer_blocks = torch.nn.ModuleList([
+                Attention(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    bias_q=True,
+                    bias_kv=True,
+                    bias_out=True
+                )
+                for d in range(num_layers)
+            ])

    def forward(self, hidden_states, time_emb, text_emb, res_stack):
        batch, _, height, width = hidden_states.shape
@@ -244,7 +294,7 @@ class DownSampler(torch.nn.Module):


 class FluxVAEDecoder(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, use_conv_attention=True):
        super().__init__()
        self.scaling_factor = 0.3611
        self.shift_factor = 0.1159
@@ -253,7 +303,7 @@ class FluxVAEDecoder(torch.nn.Module):
        self.blocks = torch.nn.ModuleList([
            # UNetMidBlock2D
            ResnetBlock(512, 512, eps=1e-6),
-            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6, use_conv_attention=use_conv_attention),
            ResnetBlock(512, 512, eps=1e-6),
            # UpDecoderBlock2D
            ResnetBlock(512, 512, eps=1e-6),
@@ -316,7 +366,7 @@ class FluxVAEDecoder(torch.nn.Module):


 class FluxVAEEncoder(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, use_conv_attention=True):
        super().__init__()
        self.scaling_factor = 0.3611
        self.shift_factor = 0.1159
@@ -340,7 +390,7 @@ class FluxVAEEncoder(torch.nn.Module):
            ResnetBlock(512, 512, eps=1e-6),
            # UNetMidBlock2D
            ResnetBlock(512, 512, eps=1e-6),
-            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6, use_conv_attention=use_conv_attention),
            ResnetBlock(512, 512, eps=1e-6),
        ])

--- a/diffsynth/models/z_image_dit.py
+++ b/diffsynth/models/z_image_dit.py
@@ -0,0 +1,621 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from torch.nn import RMSNorm
+from ..core.attention import attention_forward
+from ..core.gradient import gradient_checkpoint_forward
+
+
+ADALN_EMBED_DIM = 256
+SEQ_MULTI_OF = 32
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, out_size, mid_size=None, frequency_embedding_size=256):
+        super().__init__()
+        if mid_size is None:
+            mid_size = out_size
+        self.mlp = nn.Sequential(
+            nn.Linear(
+                frequency_embedding_size,
+                mid_size,
+                bias=True,
+            ),
+            nn.SiLU(),
+            nn.Linear(
+                mid_size,
+                out_size,
+                bias=True,
+            ),
+        )
+
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        with torch.amp.autocast("cuda", enabled=False):
+            half = dim // 2
+            freqs = torch.exp(
+                -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
+            )
+            args = t[:, None].float() * freqs[None]
+            embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+            if dim % 2:
+                embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+            return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype))
+        return t_emb
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int):
+        super().__init__()
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def _forward_silu_gating(self, x1, x3):
+        return F.silu(x1) * x3
+
+    def forward(self, x):
+        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+
+
+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.ModuleList([torch.nn.Linear(dim_inner, q_dim, bias=bias_out)])
+
+        self.norm_q = RMSNorm(head_dim, eps=1e-5)
+        self.norm_k = RMSNorm(head_dim, eps=1e-5)
+    
+    def forward(self, hidden_states, freqs_cis):
+        query = self.to_q(hidden_states)
+        key = self.to_k(hidden_states)
+        value = self.to_v(hidden_states)
+
+        query = query.unflatten(-1, (self.num_heads, -1))
+        key = key.unflatten(-1, (self.num_heads, -1))
+        value = value.unflatten(-1, (self.num_heads, -1))
+
+        # Apply Norms
+        if self.norm_q is not None:
+            query = self.norm_q(query)
+        if self.norm_k is not None:
+            key = self.norm_k(key)
+
+        # Apply RoPE
+        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+            with torch.amp.autocast("cuda", enabled=False):
+                x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
+                freqs_cis = freqs_cis.unsqueeze(2)
+                x_out = torch.view_as_real(x * freqs_cis).flatten(3)
+                return x_out.type_as(x_in)  # todo
+
+        if freqs_cis is not None:
+            query = apply_rotary_emb(query, freqs_cis)
+            key = apply_rotary_emb(key, freqs_cis)
+
+        # Cast to correct dtype
+        dtype = query.dtype
+        query, key = query.to(dtype), key.to(dtype)
+
+        # Compute joint attention
+        hidden_states = attention_forward(
+            query,
+            key,
+            value,
+            q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s n d",
+        )
+
+        # Reshape back
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(dtype)
+
+        output = self.to_out[0](hidden_states)
+        if len(self.to_out) > 1:  # dropout
+            output = self.to_out[1](output)
+
+        return output
+
+
+class ZImageTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.head_dim = dim // n_heads
+
+        # Refactored to use diffusers Attention with custom processor
+        # Original Z-Image params: dim, n_heads, n_kv_heads, qk_norm
+        self.attention = Attention(
+            q_dim=dim,
+            num_heads=n_heads,
+            head_dim=dim // n_heads,
+        )
+
+        self.feed_forward = FeedForward(dim=dim, hidden_dim=int(dim / 3 * 8))
+        self.layer_id = layer_id
+
+        self.attention_norm1 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps)
+
+        self.attention_norm2 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps)
+
+        self.modulation = modulation
+        if modulation:
+            self.adaLN_modulation = nn.Sequential(
+                nn.Linear(min(dim, ADALN_EMBED_DIM), 4 * dim, bias=True),
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attn_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor] = None,
+    ):
+        if self.modulation:
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
+            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+
+            # Attention block
+            attn_out = self.attention(
+                self.attention_norm1(x) * scale_msa,
+                freqs_cis=freqs_cis,
+            )
+            x = x + gate_msa * self.attention_norm2(attn_out)
+
+            # FFN block
+            x = x + gate_mlp * self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x) * scale_mlp,
+                )
+            )
+        else:
+            # Attention block
+            attn_out = self.attention(
+                self.attention_norm1(x),
+                freqs_cis=freqs_cis,
+            )
+            x = x + self.attention_norm2(attn_out)
+
+            # FFN block
+            x = x + self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x),
+                )
+            )
+
+        return x
+
+
+class FinalLayer(nn.Module):
+    def __init__(self, hidden_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, out_channels, bias=True)
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
+        )
+
+    def forward(self, x, c):
+        scale = 1.0 + self.adaLN_modulation(c)
+        x = self.norm_final(x) * scale.unsqueeze(1)
+        x = self.linear(x)
+        return x
+
+
+class RopeEmbedder:
+    def __init__(
+        self,
+        theta: float = 256.0,
+        axes_dims: List[int] = (16, 56, 56),
+        axes_lens: List[int] = (64, 128, 128),
+    ):
+        self.theta = theta
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        assert len(axes_dims) == len(axes_lens), "axes_dims and axes_lens must have the same length"
+        self.freqs_cis = None
+
+    @staticmethod
+    def precompute_freqs_cis(dim: List[int], end: List[int], theta: float = 256.0):
+        with torch.device("cpu"):
+            freqs_cis = []
+            for i, (d, e) in enumerate(zip(dim, end)):
+                freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+                timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+                freqs = torch.outer(timestep, freqs).float()
+                freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
+                freqs_cis.append(freqs_cis_i)
+
+            return freqs_cis
+
+    def __call__(self, ids: torch.Tensor):
+        assert ids.ndim == 2
+        assert ids.shape[-1] == len(self.axes_dims)
+        device = ids.device
+
+        if self.freqs_cis is None:
+            self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
+            self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+
+        result = []
+        for i in range(len(self.axes_dims)):
+            index = ids[:, i]
+            result.append(self.freqs_cis[i][index])
+        return torch.cat(result, dim=-1)
+
+
+class ZImageDiT(nn.Module):
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["ZImageTransformerBlock"]
+
+    def __init__(
+        self,
+        all_patch_size=(2,),
+        all_f_patch_size=(1,),
+        in_channels=16,
+        dim=3840,
+        n_layers=30,
+        n_refiner_layers=2,
+        n_heads=30,
+        n_kv_heads=30,
+        norm_eps=1e-5,
+        qk_norm=True,
+        cap_feat_dim=2560,
+        rope_theta=256.0,
+        t_scale=1000.0,
+        axes_dims=[32, 48, 48],
+        axes_lens=[1024, 512, 512],
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.all_patch_size = all_patch_size
+        self.all_f_patch_size = all_f_patch_size
+        self.dim = dim
+        self.n_heads = n_heads
+
+        self.rope_theta = rope_theta
+        self.t_scale = t_scale
+        self.gradient_checkpointing = False
+
+        assert len(all_patch_size) == len(all_f_patch_size)
+
+        all_x_embedder = {}
+        all_final_layer = {}
+        for patch_idx, (patch_size, f_patch_size) in enumerate(zip(all_patch_size, all_f_patch_size)):
+            x_embedder = nn.Linear(f_patch_size * patch_size * patch_size * in_channels, dim, bias=True)
+            all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+            final_layer = FinalLayer(dim, patch_size * patch_size * f_patch_size * self.out_channels)
+            all_final_layer[f"{patch_size}-{f_patch_size}"] = final_layer
+
+        self.all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.all_final_layer = nn.ModuleDict(all_final_layer)
+        self.noise_refiner = nn.ModuleList(
+            [
+                ZImageTransformerBlock(
+                    1000 + layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.context_refiner = nn.ModuleList(
+            [
+                ZImageTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    norm_eps,
+                    qk_norm,
+                    modulation=False,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.t_embedder = TimestepEmbedder(min(dim, ADALN_EMBED_DIM), mid_size=1024)
+        self.cap_embedder = nn.Sequential(
+            RMSNorm(cap_feat_dim, eps=norm_eps),
+            nn.Linear(cap_feat_dim, dim, bias=True),
+        )
+
+        self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
+        self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))
+
+        self.layers = nn.ModuleList(
+            [
+                ZImageTransformerBlock(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm)
+                for layer_id in range(n_layers)
+            ]
+        )
+        head_dim = dim // n_heads
+        assert head_dim == sum(axes_dims)
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+
+        self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
+
+    def unpatchify(self, x: List[torch.Tensor], size: List[Tuple], patch_size, f_patch_size) -> List[torch.Tensor]:
+        pH = pW = patch_size
+        pF = f_patch_size
+        bsz = len(x)
+        assert len(size) == bsz
+        for i in range(bsz):
+            F, H, W = size[i]
+            ori_len = (F // pF) * (H // pH) * (W // pW)
+            # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
+            x[i] = (
+                x[i][:ori_len]
+                .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                .permute(6, 0, 3, 1, 4, 2, 5)
+                .reshape(self.out_channels, F, H, W)
+            )
+        return x
+
+    @staticmethod
+    def create_coordinate_grid(size, start=None, device=None):
+        if start is None:
+            start = (0 for _ in size)
+
+        axes = [torch.arange(x0, x0 + span, dtype=torch.int32, device=device) for x0, span in zip(start, size)]
+        grids = torch.meshgrid(axes, indexing="ij")
+        return torch.stack(grids, dim=-1)
+
+    def patchify_and_embed(
+        self,
+        all_image: List[torch.Tensor],
+        all_cap_feats: List[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        device = all_image[0].device
+
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+        all_cap_pos_ids = []
+        all_cap_pad_mask = []
+        all_cap_feats_out = []
+
+        for i, (image, cap_feat) in enumerate(zip(all_image, all_cap_feats)):
+            ### Process Caption
+            cap_ori_len = len(cap_feat)
+            cap_padding_len = (-cap_ori_len) % SEQ_MULTI_OF
+            # padded position ids
+            cap_padded_pos_ids = self.create_coordinate_grid(
+                size=(cap_ori_len + cap_padding_len, 1, 1),
+                start=(1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            all_cap_pos_ids.append(cap_padded_pos_ids)
+            # pad mask
+            all_cap_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            cap_padded_feat = torch.cat(
+                [cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)],
+                dim=0,
+            )
+            all_cap_feats_out.append(cap_padded_feat)
+
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
+
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_ori_len + cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padding_pos_ids = (
+                self.create_coordinate_grid(
+                    size=(1, 1, 1),
+                    start=(0, 0, 0),
+                    device=device,
+                )
+                .flatten(0, 2)
+                .repeat(image_padding_len, 1)
+            )
+            image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
+            all_image_pos_ids.append(image_padded_pos_ids)
+            # pad mask
+            all_image_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
+            all_image_out.append(image_padded_feat)
+
+        return (
+            all_image_out,
+            all_cap_feats_out,
+            all_image_size,
+            all_image_pos_ids,
+            all_cap_pos_ids,
+            all_image_pad_mask,
+            all_cap_pad_mask,
+        )
+
+    def forward(
+        self,
+        x: List[torch.Tensor],
+        t,
+        cap_feats: List[torch.Tensor],
+        patch_size=2,
+        f_patch_size=1,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        assert patch_size in self.all_patch_size
+        assert f_patch_size in self.all_f_patch_size
+
+        bsz = len(x)
+        device = x[0].device
+        t = t * self.t_scale
+        t = self.t_embedder(t)
+
+        adaln_input = t
+
+        (
+            x,
+            cap_feats,
+            x_size,
+            x_pos_ids,
+            cap_pos_ids,
+            x_inner_pad_mask,
+            cap_inner_pad_mask,
+        ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+
+        # x embed & refine
+        x_item_seqlens = [len(_) for _ in x]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
+        x_max_item_seqlen = max(x_item_seqlens)
+
+        x = torch.cat(x, dim=0)
+        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
+        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        x = list(x.split(x_item_seqlens, dim=0))
+        x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
+
+        x = pad_sequence(x, batch_first=True, padding_value=0.0)
+        x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
+        x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(x_item_seqlens):
+            x_attn_mask[i, :seq_len] = 1
+
+        for layer in self.noise_refiner:
+            x = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                x=x,
+                attn_mask=x_attn_mask,
+                freqs_cis=x_freqs_cis,
+                adaln_input=adaln_input,
+            )
+
+        # cap embed & refine
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
+        cap_max_item_seqlen = max(cap_item_seqlens)
+
+        cap_feats = torch.cat(cap_feats, dim=0)
+        cap_feats = self.cap_embedder(cap_feats)
+        cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token
+        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
+        cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
+
+        cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
+        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
+        cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(cap_item_seqlens):
+            cap_attn_mask[i, :seq_len] = 1
+
+        for layer in self.context_refiner:
+            cap_feats = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                x=cap_feats,
+                attn_mask=cap_attn_mask,
+                freqs_cis=cap_freqs_cis,
+            )
+
+        # unified
+        unified = []
+        unified_freqs_cis = []
+        for i in range(bsz):
+            x_len = x_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
+            unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
+        unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
+        assert unified_item_seqlens == [len(_) for _ in unified]
+        unified_max_item_seqlen = max(unified_item_seqlens)
+
+        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
+        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
+        unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(unified_item_seqlens):
+            unified_attn_mask[i, :seq_len] = 1
+
+        for layer in self.layers:
+            unified = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                x=unified,
+                attn_mask=unified_attn_mask,
+                freqs_cis=unified_freqs_cis,
+                adaln_input=adaln_input,
+            )
+
+        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
+        unified = list(unified.unbind(dim=0))
+        x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
+
+        return x, {}
--- a/diffsynth/models/z_image_text_encoder.py
+++ b/diffsynth/models/z_image_text_encoder.py
@@ -0,0 +1,41 @@
+from transformers import Qwen3Model, Qwen3Config
+import torch
+
+
+class ZImageTextEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        config = Qwen3Config(**{
+            "architectures": [
+                "Qwen3ForCausalLM"
+            ],
+            "attention_bias": False,
+            "attention_dropout": 0.0,
+            "bos_token_id": 151643,
+            "eos_token_id": 151645,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_size": 2560,
+            "initializer_range": 0.02,
+            "intermediate_size": 9728,
+            "max_position_embeddings": 40960,
+            "max_window_layers": 36,
+            "model_type": "qwen3",
+            "num_attention_heads": 32,
+            "num_hidden_layers": 36,
+            "num_key_value_heads": 8,
+            "rms_norm_eps": 1e-06,
+            "rope_scaling": None,
+            "rope_theta": 1000000,
+            "sliding_window": None,
+            "tie_word_embeddings": True,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.51.0",
+            "use_cache": True,
+            "use_sliding_window": False,
+            "vocab_size": 151936
+        })
+        self.model = Qwen3Model(config)
+    
+    def forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
--- a/diffsynth/pipelines/z_image.py
+++ b/diffsynth/pipelines/z_image.py
@@ -0,0 +1,257 @@
+import torch, math
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange
+import numpy as np
+from typing import Union, List, Optional, Tuple
+
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig, gradient_checkpoint_forward
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+
+from transformers import AutoTokenizer
+from ..models.z_image_text_encoder import ZImageTextEncoder
+from ..models.z_image_dit import ZImageDiT
+from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+
+
+class ZImagePipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler()
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: ZImageDiT = None
+        self.vae_encoder: FluxVAEEncoder = None
+        self.vae_decoder: FluxVAEDecoder = None
+        self.tokenizer: AutoTokenizer = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            ZImageUnit_ShapeChecker(),
+            ZImageUnit_PromptEmbedder(),
+            ZImageUnit_NoiseInitializer(),
+            ZImageUnit_InputImageEmbedder(),
+        ]
+        self.model_fn = model_fn_z_image
+    
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = "cuda",
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("z_image_dit")
+        pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
+        pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 1.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 8,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
+        
+        # Parameters
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae_decoder(inputs_shared["latents"])
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class ZImageUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width"),
+            output_params=("height", "width"),
+        )
+
+    def process(self, pipe: ZImagePipeline, height, width):
+        height, width = pipe.check_resize_height_width(height, width)
+        return {"height": height, "width": width}
+
+
+class ZImageUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_embeds",),
+            onload_model_names=("text_encoder",)
+        )
+    
+    def encode_prompt(
+        self,
+        pipe,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+    ) -> List[torch.FloatTensor]:
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        for i, prompt_item in enumerate(prompt):
+            messages = [
+                {"role": "user", "content": prompt_item},
+            ]
+            prompt_item = pipe.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=True,
+            )
+            prompt[i] = prompt_item
+
+        text_inputs = pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = pipe.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-2]
+
+        embeddings_list = []
+
+        for i in range(len(prompt_embeds)):
+            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])
+
+        return embeddings_list
+
+    def process(self, pipe: ZImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
+        return {"prompt_embeds": prompt_embeds}
+
+
+class ZImageUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: ZImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+class ZImageUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(input_image)
+        input_latents = pipe.vae_encoder(image)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+def model_fn_z_image(
+    dit: ZImageDiT,
+    latents=None,
+    timestep=None,
+    prompt_embeds=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    latents = [rearrange(latents, "B C H W -> C B H W")]
+    timestep = (1000 - timestep) / 1000
+    model_output = dit(
+        latents,
+        timestep,
+        prompt_embeds,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )[0][0]
+    model_output = -model_output
+    model_output = rearrange(model_output, "C B H W -> B C H W")
+    return model_output
--- a/diffsynth/utils/state_dict_converters/flux_vae.py
+++ b/diffsynth/utils/state_dict_converters/flux_vae.py
@@ -262,3 +262,121 @@ def FluxVAEDecoderStateDictConverter(state_dict):
            param = state_dict[name]
            state_dict_[rename_dict[name]] = param
    return state_dict_
+
+
+def FluxVAEEncoderStateDictConverterDiffusers(state_dict):
+    # architecture
+    block_types = [
+        'ResnetBlock', 'ResnetBlock', 'DownSampler',
+        'ResnetBlock', 'ResnetBlock', 'DownSampler',
+        'ResnetBlock', 'ResnetBlock', 'DownSampler',
+        'ResnetBlock', 'ResnetBlock',
+        'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock'
+    ]
+
+    # Rename each parameter
+    local_rename_dict = {
+        "quant_conv": "quant_conv",
+        "encoder.conv_in": "conv_in",
+        "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm",
+        "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q",
+        "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k",
+        "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v",
+        "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out",
+        "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1",
+        "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1",
+        "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2",
+        "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2",
+        "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1",
+        "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1",
+        "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2",
+        "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2",
+        "encoder.conv_norm_out": "conv_norm_out",
+        "encoder.conv_out": "conv_out",
+    }
+    name_list = sorted([name for name in state_dict])
+    rename_dict = {}
+    block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1}
+    last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+    for name in name_list:
+        names = name.split(".")
+        name_prefix = ".".join(names[:-1])
+        if name_prefix in local_rename_dict:
+            rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+        elif name.startswith("encoder.down_blocks"):
+            block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+            block_type_with_id = ".".join(names[:5])
+            if block_type_with_id != last_block_type_with_id[block_type]:
+                block_id[block_type] += 1
+            last_block_type_with_id[block_type] = block_type_with_id
+            while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                block_id[block_type] += 1
+            block_type_with_id = ".".join(names[:5])
+            names = ["blocks", str(block_id[block_type])] + names[5:]
+            rename_dict[name] = ".".join(names)
+
+    # Convert state_dict
+    state_dict_ = {}
+    for name in state_dict:
+        if name in rename_dict:
+            state_dict_[rename_dict[name]] = state_dict[name]
+    return state_dict_
+
+
+def FluxVAEDecoderStateDictConverterDiffusers(state_dict):
+    # architecture
+        block_types = [
+            'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        local_rename_dict = {
+            "post_quant_conv": "post_quant_conv",
+            "decoder.conv_in": "conv_in",
+            "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm",
+            "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q",
+            "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k",
+            "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v",
+            "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out",
+            "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1",
+            "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1",
+            "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2",
+            "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2",
+            "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1",
+            "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1",
+            "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2",
+            "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2",
+            "decoder.conv_norm_out": "conv_norm_out",
+            "decoder.conv_out": "conv_out",
+        }
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2}
+        last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            name_prefix = ".".join(names[:-1])
+            if name_prefix in local_rename_dict:
+                rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+            elif name.startswith("decoder.up_blocks"):
+                block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+                block_type_with_id = ".".join(names[:5])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:5])
+                names = ["blocks", str(block_id[block_type])] + names[5:]
+                rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = state_dict[name]
+        return state_dict_