From 2354fda9c0d66ae1e1606463a767b7b8173ff73a Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 17:44:12 +0000
Subject: [PATCH 01/38] init

---
 ...convert_z_image_controlnet_to_diffusers.py | 103 +++
 src/diffusers/models/controlnets/__init__.py  |   1 +
 .../models/controlnets/controlnet_z_image.py  | 528 ++++++++++++++
 .../transformers/transformer_z_image.py       |  11 +-
 .../z_image/pipeline_z_image_controlnet.py    | 674 ++++++++++++++++++
 5 files changed, 1315 insertions(+), 2 deletions(-)
 create mode 100644 scripts/convert_z_image_controlnet_to_diffusers.py
 create mode 100644 src/diffusers/models/controlnets/controlnet_z_image.py
 create mode 100644 src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py

diff --git a/scripts/convert_z_image_controlnet_to_diffusers.py b/scripts/convert_z_image_controlnet_to_diffusers.py
new file mode 100644
index 000000000000..c4b96cda02af
--- /dev/null
+++ b/scripts/convert_z_image_controlnet_to_diffusers.py
@@ -0,0 +1,103 @@
+import argparse
+from contextlib import nullcontext
+
+import torch
+import safetensors.torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download
+
+from diffusers.utils.import_utils import is_accelerate_available
+from diffusers.models import ZImageTransformer2DModel
+from diffusers.models.controlnets.controlnet_z_image import ZImageControlNetModel
+
+"""
+python scripts/convert_z_image_controlnet_to_diffusers.py  \
+--original_z_image_repo_id "Tongyi-MAI/Z-Image-Turbo" \
+--original_controlnet_repo_id "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union" \
+--filename "Z-Image-Turbo-Fun-Controlnet-Union.safetensors"
+--output_path "z-image-controlnet-hf/"
+"""
+
+
+CTX = init_empty_weights if is_accelerate_available else nullcontext
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--original_z_image_repo_id", default="Tongyi-MAI/Z-Image-Turbo", type=str)
+parser.add_argument("--original_controlnet_repo_id", default=None, type=str)
+parser.add_argument("--filename", default="Z-Image-Turbo-Fun-Controlnet-Union.safetensors", type=str)
+parser.add_argument("--checkpoint_path", default=None, type=str)
+parser.add_argument("--output_path", type=str)
+
+args = parser.parse_args()
+
+
+def load_original_checkpoint(args):
+    if args.original_controlnet_repo_id is not None:
+        ckpt_path = hf_hub_download(repo_id=args.original_controlnet_repo_id, filename=args.filename)
+    elif args.checkpoint_path is not None:
+        ckpt_path = args.checkpoint_path
+    else:
+        raise ValueError(" please provide either `original_controlnet_repo_id` or a local `checkpoint_path`")
+
+    original_state_dict = safetensors.torch.load_file(ckpt_path)
+    return original_state_dict
+
+def load_z_image(args):
+    model = ZImageTransformer2DModel.from_pretrained(args.original_z_image_repo_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+    return model.state_dict(), model.config
+
+def convert_z_image_controlnet_checkpoint_to_diffusers(z_image, original_state_dict):
+    converted_state_dict = {}
+
+    converted_state_dict.update(original_state_dict)
+
+    to_copy = {"all_x_embedder.", "noise_refiner.", "context_refiner.", "t_embedder.", "cap_embedder.", "x_pad_token", "cap_pad_token"}
+
+    for key in z_image.keys():
+        for copy_key in to_copy:
+            if key.startswith(copy_key):
+                converted_state_dict[key] = z_image[key]
+
+    return converted_state_dict
+
+
+def main(args):
+    original_ckpt = load_original_checkpoint(args)
+    z_image, config = load_z_image(args)
+
+    control_in_dim = 16
+    control_layers_places = [0, 5, 10, 15, 20, 25]
+
+    converted_controlnet_state_dict = convert_z_image_controlnet_checkpoint_to_diffusers(z_image, original_ckpt)
+
+    for key, tensor in converted_controlnet_state_dict.items():
+        print(f"{key} - {tensor.dtype}")
+
+    controlnet = ZImageControlNetModel(
+        all_patch_size=config["all_patch_size"],
+        all_f_patch_size=config["all_f_patch_size"],
+        in_channels=config["in_channels"],
+        dim=config["dim"],
+        n_layers=config["n_layers"],
+        n_refiner_layers=config["n_refiner_layers"],
+        n_heads=config["n_heads"],
+        n_kv_heads=config["n_kv_heads"],
+        norm_eps=config["norm_eps"],
+        qk_norm=config["qk_norm"],
+        cap_feat_dim=config["cap_feat_dim"],
+        rope_theta=config["rope_theta"],
+        t_scale=config["t_scale"],
+        axes_dims=config["axes_dims"],
+        axes_lens=config["axes_lens"],
+        control_layers_places=control_layers_places,
+        control_in_dim=control_in_dim,
+    )
+    missing, unexpected = controlnet.load_state_dict(converted_controlnet_state_dict)
+    print(f"{missing=}")
+    print(f"{unexpected=}")
+    print("Saving Z-Image ControlNet in Diffusers format")
+    controlnet.save_pretrained(args.output_path, max_shard_size="5GB")
+
+
+if __name__ == "__main__":
+    main(args)
diff --git a/src/diffusers/models/controlnets/__init__.py b/src/diffusers/models/controlnets/__init__.py
index 7ce352879daa..fee7f231e899 100644
--- a/src/diffusers/models/controlnets/__init__.py
+++ b/src/diffusers/models/controlnets/__init__.py
@@ -19,6 +19,7 @@
     )
     from .controlnet_union import ControlNetUnionModel
     from .controlnet_xs import ControlNetXSAdapter, ControlNetXSOutput, UNetControlNetXSModel
+    from .controlnet_z_image import ZImageControlNetModel
     from .multicontrolnet import MultiControlNetModel
     from .multicontrolnet_union import MultiControlNetUnionModel
 
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
new file mode 100644
index 000000000000..d6cede86812d
--- /dev/null
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -0,0 +1,528 @@
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List
+
+import torch
+import torch.nn as nn
+from torch.nn.utils.rnn import pad_sequence
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import PeftAdapterMixin
+from ...models.normalization import RMSNorm
+from ..controlnets.controlnet import zero_module
+from ..modeling_utils import ModelMixin
+from ..transformers.transformer_z_image import ZImageTransformerBlock, RopeEmbedder, TimestepEmbedder, SEQ_MULTI_OF, ADALN_EMBED_DIM
+
+
+class ZImageControlTransformerBlock(ZImageTransformerBlock):
+    def __init__(
+        self, 
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        block_id=0
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm, modulation)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = zero_module(nn.Linear(self.dim, self.dim))
+        self.after_proj = zero_module(nn.Linear(self.dim, self.dim))
+
+    def forward(self, c: torch.Tensor, x: torch.Tensor, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+            all_c = []
+        else:
+            all_c = list(torch.unbind(c))
+            c = all_c.pop(-1)
+
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        all_c += [c_skip, c]
+        c = torch.stack(all_c)
+        return c
+    
+class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    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],
+        control_layers_places: List[int]=None,
+        control_in_dim=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
+        self.n_layers = n_layers
+
+        assert len(all_patch_size) == len(all_f_patch_size)
+
+        all_x_embedder = {}
+        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
+
+        self.all_x_embedder = nn.ModuleDict(all_x_embedder)
+        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.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+
+        self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
+
+        ## Original Control layers
+
+        self.control_layers_places = control_layers_places
+        self.control_in_dim = control_in_dim
+
+        assert 0 in self.control_layers_places
+
+        # control blocks
+        self.control_layers = nn.ModuleList(
+            [
+                ZImageControlTransformerBlock(
+                    i, 
+                    dim, 
+                    n_heads, 
+                    n_kv_heads, 
+                    norm_eps, 
+                    qk_norm,
+                    block_id=i
+                )
+                for i in self.control_layers_places
+            ]
+        )
+
+        # control patch embeddings
+        all_x_embedder = {}
+        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 * self.control_in_dim, dim, bias=True)
+            all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.control_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)
+            ]
+        )
+
+    @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(
+        self,
+        all_image: List[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
+        cap_padding_len: 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 = []
+
+        for i, image in enumerate(all_image):
+            ### 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_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_image_size,
+            all_image_pos_ids,
+            all_image_pad_mask,
+        )
+
+    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],
+        cap_feats: List[torch.Tensor],
+        control_context: List[torch.Tensor],
+        t=None,
+        patch_size=2,
+        f_patch_size=1,
+        conditioning_scale: float = 1.0,
+    ):
+        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)
+
+        (
+            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)
+
+        # Match t_embedder output dtype to x for layerwise casting compatibility
+        adaln_input = t.type_as(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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.noise_refiner:
+                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
+        else:
+            for layer in self.noise_refiner:
+                x = layer(x, x_attn_mask, x_freqs_cis, 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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.context_refiner:
+                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
+        else:
+            for layer in self.context_refiner:
+                cap_feats = layer(cap_feats, cap_attn_mask, 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
+
+        ## Original forward_control
+
+        # embeddings
+        bsz = len(control_context)
+        device = control_context[0].device
+        (
+            control_context,
+            x_size,
+            x_pos_ids,
+            x_inner_pad_mask,
+        ) = self.patchify(control_context, patch_size, f_patch_size, cap_feats[0].size(0))
+
+        # control_context embed & refine
+        x_item_seqlens = [len(_) for _ in control_context]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
+        x_max_item_seqlen = max(x_item_seqlens)
+
+        control_context = torch.cat(control_context, dim=0)
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+        # Match t_embedder output dtype to control_context for layerwise casting compatibility
+        adaln_input = t.type_as(control_context)
+        control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        control_context = list(control_context.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))
+
+        control_context = pad_sequence(control_context, 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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.control_noise_refiner:
+                control_context = self._gradient_checkpointing_func(layer, control_context, x_attn_mask, x_freqs_cis, adaln_input)
+        else:
+            for layer in self.control_noise_refiner:
+                control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # unified
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        control_context_unified = []
+        for i in range(bsz):
+            x_len = x_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            control_context_unified.append(torch.cat([control_context[i][:x_len], cap_feats[i][:cap_len]]))
+        control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
+        c = control_context_unified
+
+        new_kwargs = dict(x=unified, attn_mask=unified_attn_mask, freqs_cis=unified_freqs_cis, adaln_input=adaln_input)
+        
+        for layer in self.control_layers:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                c = self._gradient_checkpointing_func(layer, c, **new_kwargs)
+            else:
+                c = layer(c, **new_kwargs)
+ 
+        hints = torch.unbind(c)[:-1] * conditioning_scale
+        controlnet_block_samples = {}
+        for layer_idx in range(self.n_layers):
+            if layer_idx in self.control_layers_places:
+                hints_idx = self.control_layers_places.index(layer_idx)
+                controlnet_block_samples[layer_idx] = hints[hints_idx]
+
+        return controlnet_block_samples
diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 5c401b9d202b..2d332217d897 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -538,6 +538,7 @@ def forward(
         cap_feats: List[torch.Tensor],
         patch_size=2,
         f_patch_size=1,
+        controlnet_block_samples: Optional[dict[int, torch.Tensor]]=None,
         return_dict: bool = True,
     ):
         assert patch_size in self.all_patch_size
@@ -635,13 +636,19 @@ def forward(
             unified_attn_mask[i, :seq_len] = 1
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.layers:
+            for layer_idx, layer in enumerate(self.layers):
                 unified = self._gradient_checkpointing_func(
                     layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
                 )
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
         else:
-            for layer in self.layers:
+            for layer_idx, layer in enumerate(self.layers):
                 unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
 
         unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
         unified = list(unified.unbind(dim=0))
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
new file mode 100644
index 000000000000..609b141be796
--- /dev/null
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -0,0 +1,674 @@
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+from transformers import AutoTokenizer, PreTrainedModel
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin
+from ...models.autoencoders import AutoencoderKL
+from ...models.controlnets import ZImageControlNetModel
+from ...models.transformers import ZImageTransformer2DModel
+from ...pipelines.pipeline_utils import DiffusionPipeline
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from .pipeline_output import ZImagePipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import ZImagePipeline
+
+        >>> pipe = ZImagePipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
+        >>> # (1) Use flash attention 2
+        >>> # pipe.transformer.set_attention_backend("flash")
+        >>> # (2) Use flash attention 3
+        >>> # pipe.transformer.set_attention_backend("_flash_3")
+
+        >>> prompt = "一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。"
+        >>> image = pipe(
+        ...     prompt,
+        ...     height=1024,
+        ...     width=1024,
+        ...     num_inference_steps=9,
+        ...     guidance_scale=0.0,
+        ...     generator=torch.Generator("cuda").manual_seed(42),
+        ... ).images[0]
+        >>> image.save("zimage.png")
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class ZImageControlNetPipeline(DiffusionPipeline, FromSingleFileMixin):
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: PreTrainedModel,
+        tokenizer: AutoTokenizer,
+        transformer: ZImageTransformer2DModel,
+        controlnet: ZImageControlNetModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            scheduler=scheduler,
+            transformer=transformer,
+            controlnet=controlnet,
+        )
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 512,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt_embeds = self._encode_prompt(
+            prompt=prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            max_sequence_length=max_sequence_length,
+        )
+
+        if do_classifier_free_guidance:
+            if negative_prompt is None:
+                negative_prompt = ["" for _ in prompt]
+            else:
+                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            assert len(prompt) == len(negative_prompt)
+            negative_prompt_embeds = self._encode_prompt(
+                prompt=negative_prompt,
+                device=device,
+                prompt_embeds=negative_prompt_embeds,
+                max_sequence_length=max_sequence_length,
+            )
+        else:
+            negative_prompt_embeds = []
+        return prompt_embeds, negative_prompt_embeds
+
+    def _encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        max_sequence_length: int = 512,
+    ) -> List[torch.FloatTensor]:
+        device = device or self._execution_device
+
+        if prompt_embeds is not None:
+            return prompt_embeds
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        for i, prompt_item in enumerate(prompt):
+            messages = [
+                {"role": "user", "content": prompt_item},
+            ]
+            prompt_item = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=True,
+            )
+            prompt[i] = prompt_item
+
+        text_inputs = self.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 = self.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 prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+        return latents
+
+    # Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.prepare_image
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 5.0,
+        control_image: PipelineImageInput = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        cfg_normalization: bool = False,
+        cfg_truncation: float = 1.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to 1024):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 1024):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            cfg_normalization (`bool`, *optional*, defaults to False):
+                Whether to apply configuration normalization.
+            cfg_truncation (`float`, *optional*, defaults to 1.0):
+                The truncation value for configuration.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            prompt_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain
+                tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, *optional*, defaults to 512):
+                Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+        height = height or 1024
+        width = width or 1024
+
+        vae_scale = self.vae_scale_factor * 2
+        if height % vae_scale != 0:
+            raise ValueError(
+                f"Height must be divisible by {vae_scale} (got {height}). "
+                f"Please adjust the height to a multiple of {vae_scale}."
+            )
+        if width % vae_scale != 0:
+            raise ValueError(
+                f"Width must be divisible by {vae_scale} (got {width}). "
+                f"Please adjust the width to a multiple of {vae_scale}."
+            )
+
+        device = self._execution_device
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+        self._cfg_normalization = cfg_normalization
+        self._cfg_truncation = cfg_truncation
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = len(prompt_embeds)
+
+        # If prompt_embeds is provided and prompt is None, skip encoding
+        if prompt_embeds is not None and prompt is None:
+            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
+                raise ValueError(
+                    "When `prompt_embeds` is provided without `prompt`, "
+                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
+                )
+        else:
+            (
+                prompt_embeds,
+                negative_prompt_embeds,
+            ) = self.encode_prompt(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.in_channels
+
+        control_image = self.prepare_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )        
+        height, width = control_image.shape[-2:]
+        control_image = retrieve_latents(self.vae.encode(control_image), generator=generator)
+        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        control_image = control_image.unsqueeze(2)
+
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        # Repeat prompt_embeds for num_images_per_prompt
+        if num_images_per_prompt > 1:
+            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
+            if self.do_classifier_free_guidance and negative_prompt_embeds:
+                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
+
+        actual_batch_size = batch_size * num_images_per_prompt
+        image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
+
+        # 5. Prepare timesteps
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        self.scheduler.sigma_min = 0.0
+        scheduler_kwargs = {"mu": mu}
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            **scheduler_kwargs,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0])
+                timestep = (1000 - timestep) / 1000
+                # Normalized time for time-aware config (0 at start, 1 at end)
+                t_norm = timestep[0].item()
+
+                # Handle cfg truncation
+                current_guidance_scale = self.guidance_scale
+                if (
+                    self.do_classifier_free_guidance
+                    and self._cfg_truncation is not None
+                    and float(self._cfg_truncation) <= 1
+                ):
+                    if t_norm > self._cfg_truncation:
+                        current_guidance_scale = 0.0
+
+                # Run CFG only if configured AND scale is non-zero
+                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
+
+                if apply_cfg:
+                    latents_typed = latents.to(self.transformer.dtype)
+                    latent_model_input = latents_typed.repeat(2, 1, 1, 1)
+                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
+                    timestep_model_input = timestep.repeat(2)
+                else:
+                    latent_model_input = latents.to(self.transformer.dtype)
+                    prompt_embeds_model_input = prompt_embeds
+                    timestep_model_input = timestep
+
+                latent_model_input = latent_model_input.unsqueeze(2)
+                latent_model_input_list = list(latent_model_input.unbind(dim=0))
+
+                controlnet_block_samples = self.controlnet(
+                    latent_model_input_list,
+                    prompt_embeds_model_input,
+                    control_image,
+                    timestep_model_input,
+                    conditioning_scale=controlnet_conditioning_scale,
+                )
+
+                model_out_list = self.transformer(
+                    latent_model_input_list,
+                    timestep_model_input,
+                    prompt_embeds_model_input,
+                    controlnet_block_samples=controlnet_block_samples,
+                )[0]
+
+                if apply_cfg:
+                    # Perform CFG
+                    pos_out = model_out_list[:actual_batch_size]
+                    neg_out = model_out_list[actual_batch_size:]
+
+                    noise_pred = []
+                    for j in range(actual_batch_size):
+                        pos = pos_out[j].float()
+                        neg = neg_out[j].float()
+
+                        pred = pos + current_guidance_scale * (pos - neg)
+
+                        # Renormalization
+                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
+                            ori_pos_norm = torch.linalg.vector_norm(pos)
+                            new_pos_norm = torch.linalg.vector_norm(pred)
+                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
+                            if new_pos_norm > max_new_norm:
+                                pred = pred * (max_new_norm / new_pos_norm)
+
+                        noise_pred.append(pred)
+
+                    noise_pred = torch.stack(noise_pred, dim=0)
+                else:
+                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
+
+                noise_pred = noise_pred.squeeze(2)
+                noise_pred = -noise_pred
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
+                assert latents.dtype == torch.float32
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = latents.to(self.vae.dtype)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return ZImagePipelineOutput(images=image)

From 1e2009de435516caf7b6e67ab215f8f6299c375f Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 17:48:38 +0000
Subject: [PATCH 02/38] passed transformer

---
 .../models/controlnets/controlnet_z_image.py  | 98 +++----------------
 .../z_image/pipeline_z_image_controlnet.py    |  1 +
 2 files changed, 15 insertions(+), 84 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index d6cede86812d..6fe9d38ce3d1 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -23,7 +23,7 @@
 from ...models.normalization import RMSNorm
 from ..controlnets.controlnet import zero_module
 from ..modeling_utils import ModelMixin
-from ..transformers.transformer_z_image import ZImageTransformerBlock, RopeEmbedder, TimestepEmbedder, SEQ_MULTI_OF, ADALN_EMBED_DIM
+from ..transformers.transformer_z_image import ZImageTransformer2DModel, ZImageTransformerBlock, RopeEmbedder, TimestepEmbedder, SEQ_MULTI_OF, ADALN_EMBED_DIM
 
 
 class ZImageControlTransformerBlock(ZImageTransformerBlock):
@@ -66,87 +66,16 @@ 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],
         control_layers_places: List[int]=None,
         control_in_dim=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
-        self.n_layers = n_layers
-
-        assert len(all_patch_size) == len(all_f_patch_size)
-
-        all_x_embedder = {}
-        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
-
-        self.all_x_embedder = nn.ModuleDict(all_x_embedder)
-        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.axes_dims = axes_dims
-        self.axes_lens = axes_lens
-
-        self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
-
-        ## Original Control layers
-
         self.control_layers_places = control_layers_places
         self.control_in_dim = control_in_dim
 
@@ -366,6 +295,7 @@ def patchify_and_embed(
 
     def forward(
         self,
+        transformer: ZImageTransformer2DModel,
         x: List[torch.Tensor],
         cap_feats: List[torch.Tensor],
         control_context: List[torch.Tensor],
@@ -380,7 +310,7 @@ def forward(
         bsz = len(x)
         device = x[0].device
         t = t * self.t_scale
-        t = self.t_embedder(t)
+        t = transformer.t_embedder(t)
 
         (
             x,
@@ -398,13 +328,13 @@ def forward(
         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 = transformer.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
 
         # Match t_embedder output dtype to x for layerwise casting compatibility
         adaln_input = t.type_as(x)
-        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        x[torch.cat(x_inner_pad_mask)] = transformer.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_freqs_cis = list(transformer.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)
@@ -413,10 +343,10 @@ def forward(
             x_attn_mask[i, :seq_len] = 1
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.noise_refiner:
+            for layer in transformer.noise_refiner:
                 x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
         else:
-            for layer in self.noise_refiner:
+            for layer in transformer.noise_refiner:
                 x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
 
         # cap embed & refine
@@ -425,10 +355,10 @@ def forward(
         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 = transformer.cap_embedder(cap_feats)
+        cap_feats[torch.cat(cap_inner_pad_mask)] = transformer.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_freqs_cis = list(transformer.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)
@@ -437,10 +367,10 @@ def forward(
             cap_attn_mask[i, :seq_len] = 1
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.context_refiner:
+            for layer in transformer.context_refiner:
                 cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
         else:
-            for layer in self.context_refiner:
+            for layer in transformer.context_refiner:
                 cap_feats = layer(cap_feats, cap_attn_mask, cap_freqs_cis)
 
         # unified
@@ -485,7 +415,7 @@ def forward(
         adaln_input = t.type_as(control_context)
         control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
         control_context = list(control_context.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_freqs_cis = list(transformer.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
 
         control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
         x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 609b141be796..d374b8032ea8 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -594,6 +594,7 @@ def __call__(
                 latent_model_input_list = list(latent_model_input.unbind(dim=0))
 
                 controlnet_block_samples = self.controlnet(
+                    self.transformer,
                     latent_model_input_list,
                     prompt_embeds_model_input,
                     control_image,

From 0c308394049f2c7a65c697cf88ea9c40d9ca4333 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 17:50:01 +0000
Subject: [PATCH 03/38] ruff

---
 ...convert_z_image_controlnet_to_diffusers.py | 21 ++++++++---
 .../models/controlnets/controlnet_z_image.py  | 36 +++++++++----------
 .../transformers/transformer_z_image.py       |  2 +-
 .../z_image/pipeline_z_image_controlnet.py    |  3 +-
 4 files changed, 36 insertions(+), 26 deletions(-)

diff --git a/scripts/convert_z_image_controlnet_to_diffusers.py b/scripts/convert_z_image_controlnet_to_diffusers.py
index c4b96cda02af..a9f97d81676d 100644
--- a/scripts/convert_z_image_controlnet_to_diffusers.py
+++ b/scripts/convert_z_image_controlnet_to_diffusers.py
@@ -1,14 +1,15 @@
 import argparse
 from contextlib import nullcontext
 
-import torch
 import safetensors.torch
+import torch
 from accelerate import init_empty_weights
 from huggingface_hub import hf_hub_download
 
-from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.models import ZImageTransformer2DModel
 from diffusers.models.controlnets.controlnet_z_image import ZImageControlNetModel
+from diffusers.utils.import_utils import is_accelerate_available
+
 
 """
 python scripts/convert_z_image_controlnet_to_diffusers.py  \
@@ -42,16 +43,28 @@ def load_original_checkpoint(args):
     original_state_dict = safetensors.torch.load_file(ckpt_path)
     return original_state_dict
 
+
 def load_z_image(args):
-    model = ZImageTransformer2DModel.from_pretrained(args.original_z_image_repo_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+    model = ZImageTransformer2DModel.from_pretrained(
+        args.original_z_image_repo_id, subfolder="transformer", torch_dtype=torch.bfloat16
+    )
     return model.state_dict(), model.config
 
+
 def convert_z_image_controlnet_checkpoint_to_diffusers(z_image, original_state_dict):
     converted_state_dict = {}
 
     converted_state_dict.update(original_state_dict)
 
-    to_copy = {"all_x_embedder.", "noise_refiner.", "context_refiner.", "t_embedder.", "cap_embedder.", "x_pad_token", "cap_pad_token"}
+    to_copy = {
+        "all_x_embedder.",
+        "noise_refiner.",
+        "context_refiner.",
+        "t_embedder.",
+        "cap_embedder.",
+        "x_pad_token",
+        "cap_pad_token",
+    }
 
     for key in z_image.keys():
         for copy_key in to_copy:
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 6fe9d38ce3d1..b76a2c54c3d8 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -20,15 +20,18 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...models.normalization import RMSNorm
 from ..controlnets.controlnet import zero_module
 from ..modeling_utils import ModelMixin
-from ..transformers.transformer_z_image import ZImageTransformer2DModel, ZImageTransformerBlock, RopeEmbedder, TimestepEmbedder, SEQ_MULTI_OF, ADALN_EMBED_DIM
+from ..transformers.transformer_z_image import (
+    SEQ_MULTI_OF,
+    ZImageTransformer2DModel,
+    ZImageTransformerBlock,
+)
 
 
 class ZImageControlTransformerBlock(ZImageTransformerBlock):
     def __init__(
-        self, 
+        self,
         layer_id: int,
         dim: int,
         n_heads: int,
@@ -36,7 +39,7 @@ def __init__(
         norm_eps: float,
         qk_norm: bool,
         modulation=True,
-        block_id=0
+        block_id=0,
     ):
         super().__init__(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm, modulation)
         self.block_id = block_id
@@ -57,7 +60,8 @@ def forward(self, c: torch.Tensor, x: torch.Tensor, **kwargs):
         all_c += [c_skip, c]
         c = torch.stack(all_c)
         return c
-    
+
+
 class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
     _supports_gradient_checkpointing = True
 
@@ -72,7 +76,7 @@ def __init__(
         n_kv_heads=30,
         norm_eps=1e-5,
         qk_norm=True,
-        control_layers_places: List[int]=None,
+        control_layers_places: List[int] = None,
         control_in_dim=None,
     ):
         super().__init__()
@@ -84,15 +88,7 @@ def __init__(
         # control blocks
         self.control_layers = nn.ModuleList(
             [
-                ZImageControlTransformerBlock(
-                    i, 
-                    dim, 
-                    n_heads, 
-                    n_kv_heads, 
-                    norm_eps, 
-                    qk_norm,
-                    block_id=i
-                )
+                ZImageControlTransformerBlock(i, dim, n_heads, n_kv_heads, norm_eps, qk_norm, block_id=i)
                 for i in self.control_layers_places
             ]
         )
@@ -425,7 +421,9 @@ def forward(
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
             for layer in self.control_noise_refiner:
-                control_context = self._gradient_checkpointing_func(layer, control_context, x_attn_mask, x_freqs_cis, adaln_input)
+                control_context = self._gradient_checkpointing_func(
+                    layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
+                )
         else:
             for layer in self.control_noise_refiner:
                 control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
@@ -440,14 +438,14 @@ def forward(
         control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
         c = control_context_unified
 
-        new_kwargs = dict(x=unified, attn_mask=unified_attn_mask, freqs_cis=unified_freqs_cis, adaln_input=adaln_input)
-        
+        new_kwargs = {"x": unified, "attn_mask": unified_attn_mask, "freqs_cis": unified_freqs_cis, "adaln_input": adaln_input}
+
         for layer in self.control_layers:
             if torch.is_grad_enabled() and self.gradient_checkpointing:
                 c = self._gradient_checkpointing_func(layer, c, **new_kwargs)
             else:
                 c = layer(c, **new_kwargs)
- 
+
         hints = torch.unbind(c)[:-1] * conditioning_scale
         controlnet_block_samples = {}
         for layer_idx in range(self.n_layers):
diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 2d332217d897..70ffced8b63a 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -538,7 +538,7 @@ def forward(
         cap_feats: List[torch.Tensor],
         patch_size=2,
         f_patch_size=1,
-        controlnet_block_samples: Optional[dict[int, torch.Tensor]]=None,
+        controlnet_block_samples: Optional[dict[int, torch.Tensor]] = None,
         return_dict: bool = True,
     ):
         assert patch_size in self.all_patch_size
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index d374b8032ea8..44906a0db519 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -89,7 +89,6 @@ def retrieve_latents(
         raise AttributeError("Could not access latents of provided encoder_output")
 
 
-
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
 def retrieve_timesteps(
     scheduler,
@@ -509,7 +508,7 @@ def __call__(
             num_images_per_prompt=num_images_per_prompt,
             device=device,
             dtype=self.vae.dtype,
-        )        
+        )
         height, width = control_image.shape[-2:]
         control_image = retrieve_latents(self.vae.encode(control_image), generator=generator)
         control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor

From 52f996e226dfd1e7f1a1b0d001c022dae71e24a8 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:03:23 +0000
Subject: [PATCH 04/38] convert passed

---
 ...convert_z_image_controlnet_to_diffusers.py | 58 ++-----------------
 1 file changed, 6 insertions(+), 52 deletions(-)

diff --git a/scripts/convert_z_image_controlnet_to_diffusers.py b/scripts/convert_z_image_controlnet_to_diffusers.py
index a9f97d81676d..aed27c14f205 100644
--- a/scripts/convert_z_image_controlnet_to_diffusers.py
+++ b/scripts/convert_z_image_controlnet_to_diffusers.py
@@ -1,19 +1,17 @@
 import argparse
 from contextlib import nullcontext
 
-import safetensors.torch
 import torch
+import safetensors.torch
 from accelerate import init_empty_weights
 from huggingface_hub import hf_hub_download
 
-from diffusers.models import ZImageTransformer2DModel
 from diffusers.models.controlnets.controlnet_z_image import ZImageControlNetModel
 from diffusers.utils.import_utils import is_accelerate_available
 
 
 """
 python scripts/convert_z_image_controlnet_to_diffusers.py  \
---original_z_image_repo_id "Tongyi-MAI/Z-Image-Turbo" \
 --original_controlnet_repo_id "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union" \
 --filename "Z-Image-Turbo-Fun-Controlnet-Union.safetensors"
 --output_path "z-image-controlnet-hf/"
@@ -23,7 +21,6 @@
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--original_z_image_repo_id", default="Tongyi-MAI/Z-Image-Turbo", type=str)
 parser.add_argument("--original_controlnet_repo_id", default=None, type=str)
 parser.add_argument("--filename", default="Z-Image-Turbo-Fun-Controlnet-Union.safetensors", type=str)
 parser.add_argument("--checkpoint_path", default=None, type=str)
@@ -44,72 +41,29 @@ def load_original_checkpoint(args):
     return original_state_dict
 
 
-def load_z_image(args):
-    model = ZImageTransformer2DModel.from_pretrained(
-        args.original_z_image_repo_id, subfolder="transformer", torch_dtype=torch.bfloat16
-    )
-    return model.state_dict(), model.config
-
-
-def convert_z_image_controlnet_checkpoint_to_diffusers(z_image, original_state_dict):
+def convert_z_image_controlnet_checkpoint_to_diffusers(original_state_dict):
     converted_state_dict = {}
 
     converted_state_dict.update(original_state_dict)
 
-    to_copy = {
-        "all_x_embedder.",
-        "noise_refiner.",
-        "context_refiner.",
-        "t_embedder.",
-        "cap_embedder.",
-        "x_pad_token",
-        "cap_pad_token",
-    }
-
-    for key in z_image.keys():
-        for copy_key in to_copy:
-            if key.startswith(copy_key):
-                converted_state_dict[key] = z_image[key]
-
     return converted_state_dict
 
 
 def main(args):
     original_ckpt = load_original_checkpoint(args)
-    z_image, config = load_z_image(args)
 
     control_in_dim = 16
     control_layers_places = [0, 5, 10, 15, 20, 25]
 
-    converted_controlnet_state_dict = convert_z_image_controlnet_checkpoint_to_diffusers(z_image, original_ckpt)
-
-    for key, tensor in converted_controlnet_state_dict.items():
-        print(f"{key} - {tensor.dtype}")
+    converted_controlnet_state_dict = convert_z_image_controlnet_checkpoint_to_diffusers(original_ckpt)
 
     controlnet = ZImageControlNetModel(
-        all_patch_size=config["all_patch_size"],
-        all_f_patch_size=config["all_f_patch_size"],
-        in_channels=config["in_channels"],
-        dim=config["dim"],
-        n_layers=config["n_layers"],
-        n_refiner_layers=config["n_refiner_layers"],
-        n_heads=config["n_heads"],
-        n_kv_heads=config["n_kv_heads"],
-        norm_eps=config["norm_eps"],
-        qk_norm=config["qk_norm"],
-        cap_feat_dim=config["cap_feat_dim"],
-        rope_theta=config["rope_theta"],
-        t_scale=config["t_scale"],
-        axes_dims=config["axes_dims"],
-        axes_lens=config["axes_lens"],
         control_layers_places=control_layers_places,
         control_in_dim=control_in_dim,
-    )
-    missing, unexpected = controlnet.load_state_dict(converted_controlnet_state_dict)
-    print(f"{missing=}")
-    print(f"{unexpected=}")
+    ).to(torch.bfloat16)
+    controlnet.load_state_dict(converted_controlnet_state_dict)
     print("Saving Z-Image ControlNet in Diffusers format")
-    controlnet.save_pretrained(args.output_path, max_shard_size="5GB")
+    controlnet.save_pretrained(args.output_path)
 
 
 if __name__ == "__main__":

From 4b446b394150575b322836b048acd3eeeb2072a3 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:03:30 +0000
Subject: [PATCH 05/38] __init__

---
 src/diffusers/__init__.py                   | 4 ++++
 src/diffusers/models/__init__.py            | 2 ++
 src/diffusers/pipelines/__init__.py         | 4 ++--
 src/diffusers/pipelines/z_image/__init__.py | 2 ++
 4 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index eb8e86c4c89d..f45be1560716 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -277,6 +277,7 @@
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
             "ZImageTransformer2DModel",
+            "ZImageControlNetModel",
             "attention_backend",
         ]
     )
@@ -661,6 +662,7 @@
             "WuerstchenDecoderPipeline",
             "WuerstchenPriorPipeline",
             "ZImagePipeline",
+            "ZImageControlNetPipeline",
         ]
     )
 
@@ -1004,6 +1006,7 @@
             WanTransformer3DModel,
             WanVACETransformer3DModel,
             ZImageTransformer2DModel,
+            ZImageControlNetModel,
             attention_backend,
         )
         from .modular_pipelines import ComponentsManager, ComponentSpec, ModularPipeline, ModularPipelineBlocks
@@ -1357,6 +1360,7 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
             ZImagePipeline,
+            ZImageControlNetPipeline,
         )
 
     try:
diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py
index 29d8b0b5a55d..7ea15ef2a215 100755
--- a/src/diffusers/models/__init__.py
+++ b/src/diffusers/models/__init__.py
@@ -66,6 +66,7 @@
     _import_structure["controlnets.controlnet_sparsectrl"] = ["SparseControlNetModel"]
     _import_structure["controlnets.controlnet_union"] = ["ControlNetUnionModel"]
     _import_structure["controlnets.controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
+    _import_structure["controlnets.controlnet_z_image"] = ["ZImageControlNetModel"]
     _import_structure["controlnets.multicontrolnet"] = ["MultiControlNetModel"]
     _import_structure["controlnets.multicontrolnet_union"] = ["MultiControlNetUnionModel"]
     _import_structure["embeddings"] = ["ImageProjection"]
@@ -180,6 +181,7 @@
             SD3MultiControlNetModel,
             SparseControlNetModel,
             UNetControlNetXSModel,
+            ZImageControlNetModel,
         )
         from .embeddings import ImageProjection
         from .modeling_utils import ModelMixin
diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py
index 3d669aecf556..fe6af5cd1e0b 100644
--- a/src/diffusers/pipelines/__init__.py
+++ b/src/diffusers/pipelines/__init__.py
@@ -404,7 +404,7 @@
         "Kandinsky5T2IPipeline",
         "Kandinsky5I2IPipeline",
     ]
-    _import_structure["z_image"] = ["ZImagePipeline"]
+    _import_structure["z_image"] = ["ZImagePipeline", "ZImageControlNetPipeline"]
     _import_structure["skyreels_v2"] = [
         "SkyReelsV2DiffusionForcingPipeline",
         "SkyReelsV2DiffusionForcingImageToVideoPipeline",
@@ -841,7 +841,7 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
         )
-        from .z_image import ZImagePipeline
+        from .z_image import ZImagePipeline, ZImageControlNetPipeline
 
         try:
             if not is_onnx_available():
diff --git a/src/diffusers/pipelines/z_image/__init__.py b/src/diffusers/pipelines/z_image/__init__.py
index f95b3e5a0bed..842d5690e3d7 100644
--- a/src/diffusers/pipelines/z_image/__init__.py
+++ b/src/diffusers/pipelines/z_image/__init__.py
@@ -23,6 +23,7 @@
 else:
     _import_structure["pipeline_output"] = ["ZImagePipelineOutput"]
     _import_structure["pipeline_z_image"] = ["ZImagePipeline"]
+    _import_structure["pipeline_z_image_controlnet"] = ["ZImageControlNetPipeline"]
 
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -35,6 +36,7 @@
     else:
         from .pipeline_output import ZImagePipelineOutput
         from .pipeline_z_image import ZImagePipeline
+        from .pipeline_z_image_controlnet import ZImageControlNetPipeline
 
 else:
     import sys

From a1ff390ecebb5afb9f6282209526adfdaf31c5d5 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:03:37 +0000
Subject: [PATCH 06/38] pipeline example

---
 .../pipelines/z_image/pipeline_z_image_controlnet.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 44906a0db519..ae81105eea27 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -36,9 +36,13 @@
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import ZImagePipeline
+        >>> from diffusers import ZImageControlNetPipeline
+        >>> from diffusers import ZImageControlNetModel
 
-        >>> pipe = ZImagePipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
+        >>> controlnet_model = "..."
+        >>> controlnet = ZImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
+
+        >>> pipe = ZImageControlNetPipeline.from_pretrained("Z-a-o/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")
 
         >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
@@ -47,9 +51,11 @@
         >>> # (2) Use flash attention 3
         >>> # pipe.transformer.set_attention_backend("_flash_3")
 
-        >>> prompt = "一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。"
+        >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
+        >>> prompt = "A girl in city, 25 years old, cool, futuristic"
         >>> image = pipe(
         ...     prompt,
+        ...     control_image=control_image,
         ...     height=1024,
         ...     width=1024,
         ...     num_inference_steps=9,

From 7ab347d812a5b78076f79541f4046d59948d0464 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:04:01 +0000
Subject: [PATCH 07/38] ruff

---
 scripts/convert_z_image_controlnet_to_diffusers.py     | 2 +-
 src/diffusers/__init__.py                              | 4 ++--
 src/diffusers/models/controlnets/controlnet_z_image.py | 7 ++++++-
 src/diffusers/pipelines/__init__.py                    | 2 +-
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/scripts/convert_z_image_controlnet_to_diffusers.py b/scripts/convert_z_image_controlnet_to_diffusers.py
index aed27c14f205..e5d5f34e36e8 100644
--- a/scripts/convert_z_image_controlnet_to_diffusers.py
+++ b/scripts/convert_z_image_controlnet_to_diffusers.py
@@ -1,8 +1,8 @@
 import argparse
 from contextlib import nullcontext
 
-import torch
 import safetensors.torch
+import torch
 from accelerate import init_empty_weights
 from huggingface_hub import hf_hub_download
 
diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index f45be1560716..398f72167ad3 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -1005,8 +1005,8 @@
             WanAnimateTransformer3DModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
-            ZImageTransformer2DModel,
             ZImageControlNetModel,
+            ZImageTransformer2DModel,
             attention_backend,
         )
         from .modular_pipelines import ComponentsManager, ComponentSpec, ModularPipeline, ModularPipelineBlocks
@@ -1359,8 +1359,8 @@
             WuerstchenCombinedPipeline,
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
-            ZImagePipeline,
             ZImageControlNetPipeline,
+            ZImagePipeline,
         )
 
     try:
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index b76a2c54c3d8..ff148781f49a 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -438,7 +438,12 @@ def forward(
         control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
         c = control_context_unified
 
-        new_kwargs = {"x": unified, "attn_mask": unified_attn_mask, "freqs_cis": unified_freqs_cis, "adaln_input": adaln_input}
+        new_kwargs = {
+            "x": unified,
+            "attn_mask": unified_attn_mask,
+            "freqs_cis": unified_freqs_cis,
+            "adaln_input": adaln_input,
+        }
 
         for layer in self.control_layers:
             if torch.is_grad_enabled() and self.gradient_checkpointing:
diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py
index fe6af5cd1e0b..10ce49fe8111 100644
--- a/src/diffusers/pipelines/__init__.py
+++ b/src/diffusers/pipelines/__init__.py
@@ -841,7 +841,7 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
         )
-        from .z_image import ZImagePipeline, ZImageControlNetPipeline
+        from .z_image import ZImageControlNetPipeline, ZImagePipeline
 
         try:
             if not is_onnx_available():

From 8cab0c953c7b732324a92d4e5de067b8bb290a5d Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:05:36 +0000
Subject: [PATCH 08/38] pipeline load_image

---
 src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index ae81105eea27..67771dddabd7 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -38,6 +38,7 @@
         >>> import torch
         >>> from diffusers import ZImageControlNetPipeline
         >>> from diffusers import ZImageControlNetModel
+        >>> from diffusers.utils import load_image
 
         >>> controlnet_model = "..."
         >>> controlnet = ZImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)

From 8688fa66a110bd0d77df8d03299fc3a42130ce07 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:24:05 +0000
Subject: [PATCH 09/38] t_scale

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index ff148781f49a..070724a85883 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -305,7 +305,7 @@ def forward(
 
         bsz = len(x)
         device = x[0].device
-        t = t * self.t_scale
+        t = t * transformer.t_scale
         t = transformer.t_embedder(t)
 
         (

From 9051272d47082c5cf6bc409b6368332cdac16f97 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:28:26 +0000
Subject: [PATCH 10/38] x_pad_token

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 070724a85883..48b9a66a25a3 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -409,7 +409,7 @@ def forward(
 
         # Match t_embedder output dtype to control_context for layerwise casting compatibility
         adaln_input = t.type_as(control_context)
-        control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        control_context[torch.cat(x_inner_pad_mask)] = transformer.x_pad_token
         control_context = list(control_context.split(x_item_seqlens, dim=0))
         x_freqs_cis = list(transformer.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
 

From 0d8c3f1a28180fc85fc9a4e0696d5f4f11def56f Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:28:34 +0000
Subject: [PATCH 11/38] controlnet_block_samples

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 48b9a66a25a3..0127f7f9683f 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -452,10 +452,6 @@ def forward(
                 c = layer(c, **new_kwargs)
 
         hints = torch.unbind(c)[:-1] * conditioning_scale
-        controlnet_block_samples = {}
-        for layer_idx in range(self.n_layers):
-            if layer_idx in self.control_layers_places:
-                hints_idx = self.control_layers_places.index(layer_idx)
-                controlnet_block_samples[layer_idx] = hints[hints_idx]
+        controlnet_block_samples = {layer_idx: hints[idx] for idx, layer_idx in enumerate(self.control_layers_places)}
 
         return controlnet_block_samples

From f789325ccd8f3f6fb35dffdd4acea6f21f30084e Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:29:54 +0000
Subject: [PATCH 12/38] conditioning_scale

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 0127f7f9683f..3a200b252a01 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -451,7 +451,7 @@ def forward(
             else:
                 c = layer(c, **new_kwargs)
 
-        hints = torch.unbind(c)[:-1] * conditioning_scale
-        controlnet_block_samples = {layer_idx: hints[idx] for idx, layer_idx in enumerate(self.control_layers_places)}
+        hints = torch.unbind(c)[:-1]
+        controlnet_block_samples = {layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)}
 
         return controlnet_block_samples

From 5f8ab7bf98549ff6bdc63db500ad1433b7cf84e2 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 18:33:36 +0000
Subject: [PATCH 13/38] self.config

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 3a200b252a01..d0f8b861e0c9 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -300,8 +300,8 @@ def forward(
         f_patch_size=1,
         conditioning_scale: float = 1.0,
     ):
-        assert patch_size in self.all_patch_size
-        assert f_patch_size in self.all_f_patch_size
+        assert patch_size in self.config.all_patch_size
+        assert f_patch_size in self.config.all_f_patch_size
 
         bsz = len(x)
         device = x[0].device

From bc72f9ce93ca691018fb8f1b420684b6e18a6d55 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 20:08:24 +0000
Subject: [PATCH 14/38] sample_mode, default controlnet_conditioning_scale

---
 .../pipelines/z_image/pipeline_z_image_controlnet.py          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 67771dddabd7..d0460cf09244 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -360,7 +360,7 @@ def __call__(
         sigmas: Optional[List[float]] = None,
         guidance_scale: float = 5.0,
         control_image: PipelineImageInput = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        controlnet_conditioning_scale: Union[float, List[float]] = 0.75,
         cfg_normalization: bool = False,
         cfg_truncation: float = 1.0,
         negative_prompt: Optional[Union[str, List[str]]] = None,
@@ -517,7 +517,7 @@ def __call__(
             dtype=self.vae.dtype,
         )
         height, width = control_image.shape[-2:]
-        control_image = retrieve_latents(self.vae.encode(control_image), generator=generator)
+        control_image = retrieve_latents(self.vae.encode(control_image), generator=generator, sample_mode="argmax")
         control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
         control_image = control_image.unsqueeze(2)
 

From 13b706a99f209197352bcb8790260727d75b2b9b Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 4 Dec 2025 20:16:49 +0000
Subject: [PATCH 15/38] ruff

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index d0f8b861e0c9..c121f42c1a78 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -452,6 +452,8 @@ def forward(
                 c = layer(c, **new_kwargs)
 
         hints = torch.unbind(c)[:-1]
-        controlnet_block_samples = {layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)}
+        controlnet_block_samples = {
+            layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)
+        }
 
         return controlnet_block_samples

From 09849a77465e49f6d1ca056638d917b6937f4b95 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 5 Dec 2025 20:48:16 +0000
Subject: [PATCH 16/38] ZImageControlTransformer2DModel

---
 src/diffusers/__init__.py                     |   2 +
 src/diffusers/loaders/peft.py                 |   1 +
 src/diffusers/models/__init__.py              |   2 +
 .../models/controlnets/controlnet_z_image.py  | 360 +-------
 src/diffusers/models/transformers/__init__.py |   1 +
 .../transformers/transformer_z_image.py       |   7 -
 .../transformer_z_image_control.py            | 784 ++++++++++++++++++
 .../z_image/pipeline_z_image_controlnet.py    |  18 +-
 8 files changed, 808 insertions(+), 367 deletions(-)
 create mode 100644 src/diffusers/models/transformers/transformer_z_image_control.py

diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index 398f72167ad3..746021bfd706 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -277,6 +277,7 @@
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
             "ZImageTransformer2DModel",
+            "ZImageControlTransformer2DModel",
             "ZImageControlNetModel",
             "attention_backend",
         ]
@@ -1006,6 +1007,7 @@
             WanTransformer3DModel,
             WanVACETransformer3DModel,
             ZImageControlNetModel,
+            ZImageControlTransformer2DModel,
             ZImageTransformer2DModel,
             attention_backend,
         )
diff --git a/src/diffusers/loaders/peft.py b/src/diffusers/loaders/peft.py
index 3f8519bbfa32..62182f2d205f 100644
--- a/src/diffusers/loaders/peft.py
+++ b/src/diffusers/loaders/peft.py
@@ -65,6 +65,7 @@
     "QwenImageTransformer2DModel": lambda model_cls, weights: weights,
     "Flux2Transformer2DModel": lambda model_cls, weights: weights,
     "ZImageTransformer2DModel": lambda model_cls, weights: weights,
+    "ZImageControlTransformer2DModel": lambda model_cls, weights: weights,
 }
 
 
diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py
index 7ea15ef2a215..48d06def1c3c 100755
--- a/src/diffusers/models/__init__.py
+++ b/src/diffusers/models/__init__.py
@@ -117,6 +117,7 @@
     _import_structure["transformers.transformer_wan_animate"] = ["WanAnimateTransformer3DModel"]
     _import_structure["transformers.transformer_wan_vace"] = ["WanVACETransformer3DModel"]
     _import_structure["transformers.transformer_z_image"] = ["ZImageTransformer2DModel"]
+    _import_structure["transformers.transformer_z_image_control"] = ["ZImageControlTransformer2DModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
     _import_structure["unets.unet_2d"] = ["UNet2DModel"]
     _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
@@ -231,6 +232,7 @@
             WanAnimateTransformer3DModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
+            ZImageControlTransformer2DModel,
             ZImageTransformer2DModel,
         )
         from .unets import (
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index c121f42c1a78..0972fb46c07b 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -12,19 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
+from typing import List, Optional
 
 import torch
 import torch.nn as nn
-from torch.nn.utils.rnn import pad_sequence
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
 from ..controlnets.controlnet import zero_module
 from ..modeling_utils import ModelMixin
 from ..transformers.transformer_z_image import (
-    SEQ_MULTI_OF,
-    ZImageTransformer2DModel,
     ZImageTransformerBlock,
 )
 
@@ -47,7 +44,14 @@ def __init__(
             self.before_proj = zero_module(nn.Linear(self.dim, self.dim))
         self.after_proj = zero_module(nn.Linear(self.dim, self.dim))
 
-    def forward(self, c: torch.Tensor, x: torch.Tensor, **kwargs):
+    def forward(
+        self,
+        c: torch.Tensor,
+        x: torch.Tensor,
+        attn_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor] = None,
+    ):
         if self.block_id == 0:
             c = self.before_proj(c) + x
             all_c = []
@@ -55,7 +59,7 @@ def forward(self, c: torch.Tensor, x: torch.Tensor, **kwargs):
             all_c = list(torch.unbind(c))
             c = all_c.pop(-1)
 
-        c = super().forward(c, **kwargs)
+        c = super().forward(c, attn_mask, freqs_cis, adaln_input)
         c_skip = self.after_proj(c)
         all_c += [c_skip, c]
         c = torch.stack(all_c)
@@ -115,345 +119,5 @@ def __init__(
             ]
         )
 
-    @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(
-        self,
-        all_image: List[torch.Tensor],
-        patch_size: int,
-        f_patch_size: int,
-        cap_padding_len: 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 = []
-
-        for i, image in enumerate(all_image):
-            ### 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_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_image_size,
-            all_image_pos_ids,
-            all_image_pad_mask,
-        )
-
-    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,
-        transformer: ZImageTransformer2DModel,
-        x: List[torch.Tensor],
-        cap_feats: List[torch.Tensor],
-        control_context: List[torch.Tensor],
-        t=None,
-        patch_size=2,
-        f_patch_size=1,
-        conditioning_scale: float = 1.0,
-    ):
-        assert patch_size in self.config.all_patch_size
-        assert f_patch_size in self.config.all_f_patch_size
-
-        bsz = len(x)
-        device = x[0].device
-        t = t * transformer.t_scale
-        t = transformer.t_embedder(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 = transformer.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
-
-        # Match t_embedder output dtype to x for layerwise casting compatibility
-        adaln_input = t.type_as(x)
-        x[torch.cat(x_inner_pad_mask)] = transformer.x_pad_token
-        x = list(x.split(x_item_seqlens, dim=0))
-        x_freqs_cis = list(transformer.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
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in transformer.noise_refiner:
-                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
-        else:
-            for layer in transformer.noise_refiner:
-                x = layer(x, x_attn_mask, x_freqs_cis, 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 = transformer.cap_embedder(cap_feats)
-        cap_feats[torch.cat(cap_inner_pad_mask)] = transformer.cap_pad_token
-        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
-        cap_freqs_cis = list(transformer.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
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in transformer.context_refiner:
-                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
-        else:
-            for layer in transformer.context_refiner:
-                cap_feats = layer(cap_feats, cap_attn_mask, 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
-
-        ## Original forward_control
-
-        # embeddings
-        bsz = len(control_context)
-        device = control_context[0].device
-        (
-            control_context,
-            x_size,
-            x_pos_ids,
-            x_inner_pad_mask,
-        ) = self.patchify(control_context, patch_size, f_patch_size, cap_feats[0].size(0))
-
-        # control_context embed & refine
-        x_item_seqlens = [len(_) for _ in control_context]
-        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
-        x_max_item_seqlen = max(x_item_seqlens)
-
-        control_context = torch.cat(control_context, dim=0)
-        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
-
-        # Match t_embedder output dtype to control_context for layerwise casting compatibility
-        adaln_input = t.type_as(control_context)
-        control_context[torch.cat(x_inner_pad_mask)] = transformer.x_pad_token
-        control_context = list(control_context.split(x_item_seqlens, dim=0))
-        x_freqs_cis = list(transformer.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
-
-        control_context = pad_sequence(control_context, 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
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.control_noise_refiner:
-                control_context = self._gradient_checkpointing_func(
-                    layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
-                )
-        else:
-            for layer in self.control_noise_refiner:
-                control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
-
-        # unified
-        cap_item_seqlens = [len(_) for _ in cap_feats]
-        control_context_unified = []
-        for i in range(bsz):
-            x_len = x_item_seqlens[i]
-            cap_len = cap_item_seqlens[i]
-            control_context_unified.append(torch.cat([control_context[i][:x_len], cap_feats[i][:cap_len]]))
-        control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
-        c = control_context_unified
-
-        new_kwargs = {
-            "x": unified,
-            "attn_mask": unified_attn_mask,
-            "freqs_cis": unified_freqs_cis,
-            "adaln_input": adaln_input,
-        }
-
-        for layer in self.control_layers:
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                c = self._gradient_checkpointing_func(layer, c, **new_kwargs)
-            else:
-                c = layer(c, **new_kwargs)
-
-        hints = torch.unbind(c)[:-1]
-        controlnet_block_samples = {
-            layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)
-        }
-
-        return controlnet_block_samples
+    def forward(self):
+        pass
diff --git a/src/diffusers/models/transformers/__init__.py b/src/diffusers/models/transformers/__init__.py
index a42f6b2716e1..13322aa29ae4 100755
--- a/src/diffusers/models/transformers/__init__.py
+++ b/src/diffusers/models/transformers/__init__.py
@@ -48,3 +48,4 @@
     from .transformer_wan_animate import WanAnimateTransformer3DModel
     from .transformer_wan_vace import WanVACETransformer3DModel
     from .transformer_z_image import ZImageTransformer2DModel
+    from .transformer_z_image_control import ZImageControlTransformer2DModel
diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 70ffced8b63a..7c01361b681d 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -538,7 +538,6 @@ def forward(
         cap_feats: List[torch.Tensor],
         patch_size=2,
         f_patch_size=1,
-        controlnet_block_samples: Optional[dict[int, torch.Tensor]] = None,
         return_dict: bool = True,
     ):
         assert patch_size in self.all_patch_size
@@ -640,15 +639,9 @@ def forward(
                 unified = self._gradient_checkpointing_func(
                     layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
                 )
-                if controlnet_block_samples is not None:
-                    if layer_idx in controlnet_block_samples:
-                        unified = unified + controlnet_block_samples[layer_idx]
         else:
             for layer_idx, layer in enumerate(self.layers):
                 unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
-                if controlnet_block_samples is not None:
-                    if layer_idx in controlnet_block_samples:
-                        unified = unified + controlnet_block_samples[layer_idx]
 
         unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
         unified = list(unified.unbind(dim=0))
diff --git a/src/diffusers/models/transformers/transformer_z_image_control.py b/src/diffusers/models/transformers/transformer_z_image_control.py
new file mode 100644
index 000000000000..61b752f58f68
--- /dev/null
+++ b/src/diffusers/models/transformers/transformer_z_image_control.py
@@ -0,0 +1,784 @@
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+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 ...configuration_utils import ConfigMixin, FrozenDict, register_to_config
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
+from ...models.attention_processor import Attention
+from ...models.modeling_utils import ModelMixin
+from ...models.normalization import RMSNorm
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention_dispatch import dispatch_attention_fn
+from ..modeling_outputs import Transformer2DModelOutput
+from .transformer_z_image import ZImageTransformer2DModel
+
+
+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)
+        weight_dtype = self.mlp[0].weight.dtype
+        compute_dtype = getattr(self.mlp[0], "compute_dtype", None)
+        if weight_dtype.is_floating_point:
+            t_freq = t_freq.to(weight_dtype)
+        elif compute_dtype is not None:
+            t_freq = t_freq.to(compute_dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class ZSingleStreamAttnProcessor:
+    """
+    Processor for Z-Image single stream attention that adapts the existing Attention class to match the behavior of the
+    original Z-ImageAttention module.
+    """
+
+    _attention_backend = None
+    _parallel_config = None
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "ZSingleStreamAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(-1, (attn.heads, -1))
+        key = key.unflatten(-1, (attn.heads, -1))
+        value = value.unflatten(-1, (attn.heads, -1))
+
+        # Apply Norms
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.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)
+
+        # From [batch, seq_len] to [batch, 1, 1, seq_len] -> broadcast to [batch, heads, seq_len, seq_len]
+        if attention_mask is not None and attention_mask.ndim == 2:
+            attention_mask = attention_mask[:, None, None, :]
+
+        # Compute joint attention
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+
+        # Reshape back
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(dtype)
+
+        output = attn.to_out[0](hidden_states)
+        if len(attn.to_out) > 1:  # dropout
+            output = attn.to_out[1](output)
+
+        return output
+
+
+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)))
+
+
+@maybe_allow_in_graph
+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(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=dim // n_heads,
+            heads=n_heads,
+            qk_norm="rms_norm" if qk_norm else None,
+            eps=1e-5,
+            bias=False,
+            out_bias=False,
+            processor=ZSingleStreamAttnProcessor(),
+        )
+
+        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, attention_mask=attn_mask, 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), attention_mask=attn_mask, 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]
+        else:
+            # Ensure freqs_cis are on the same device as ids
+            if self.freqs_cis[0].device != device:
+                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 ZImageControlTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["ZImageTransformerBlock", "ZImageControlTransformerBlock"]
+    _repeated_blocks = ["ZImageTransformerBlock", "ZImageControlTransformerBlock"]
+    _skip_layerwise_casting_patterns = ["t_embedder", "cap_embedder"]  # precision sensitive layers
+
+    @register_to_config
+    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],
+        control_layers_places: List[int] = None,
+        control_in_dim=None,
+    ) -> None:
+        super().__init__()
+        from ...models.controlnets.controlnet_z_image import ZImageControlTransformerBlock
+
+        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)
+
+        self.control_layers_places = control_layers_places
+        self.control_in_dim = control_in_dim
+
+        assert 0 in self.control_layers_places
+
+        # control blocks
+        self.control_layers = nn.ModuleList(
+            [
+                ZImageControlTransformerBlock(i, dim, n_heads, n_kv_heads, norm_eps, qk_norm, block_id=i)
+                for i in self.control_layers_places
+            ]
+        )
+
+        # control patch embeddings
+        all_x_embedder = {}
+        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 * self.control_in_dim, dim, bias=True)
+            all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.control_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)
+            ]
+        )
+
+    @classmethod
+    def from_controlnet(
+        cls,
+        transformer: ZImageTransformer2DModel,
+        controlnet,
+        load_weights: bool = True,
+    ):
+        controlnet.to(device=transformer.device)
+
+        if transformer.config["dim"] != controlnet.config["dim"]:
+            raise ValueError("Incompatible ControlNet, got a different dim.")
+
+        config = dict(transformer.config)
+        config["_class_name"] = cls.__name__
+
+        config["control_layers_places"] = controlnet.config["control_layers_places"]
+        config["control_in_dim"] = controlnet.config["control_in_dim"]
+
+        expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
+        config = FrozenDict({k: config.get(k) for k in config if k in expected_kwargs or k in optional_kwargs})
+        config["_class_name"] = cls.__name__
+        model = cls.from_config(config)
+
+        if not load_weights:
+            return model
+
+        for i, control_layer in enumerate(controlnet.control_layers):
+            model.control_layers[i].load_state_dict(control_layer.state_dict())
+
+        for i, control_all_x_embedder in enumerate(controlnet.control_all_x_embedder):
+            model.control_all_x_embedder[i].load_state_dict(control_all_x_embedder.state_dict())
+
+        for i, control_noise_refiner in enumerate(controlnet.control_noise_refiner):
+            model.control_noise_refiner[i].load_state_dict(control_noise_refiner.state_dict())
+
+        model.to(transformer.dtype)
+
+        return model
+
+    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
+            cap_pad_mask = torch.cat(
+                [
+                    torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_cap_pad_mask.append(
+                cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
+            )
+
+            # 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_padded_pos_ids = torch.cat(
+                [
+                    image_ori_pos_ids,
+                    self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                    .flatten(0, 2)
+                    .repeat(image_padding_len, 1),
+                ],
+                dim=0,
+            )
+            all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
+            # pad mask
+            image_pad_mask = torch.cat(
+                [
+                    torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_image_pad_mask.append(
+                image_pad_mask
+                if image_padding_len > 0
+                else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
+            )
+            # padded feature
+            image_padded_feat = torch.cat(
+                [image, image[-1:].repeat(image_padding_len, 1)],
+                dim=0,
+            )
+            all_image_out.append(image_padded_feat if image_padding_len > 0 else image)
+
+        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,
+        control_context: Optional[List[torch.Tensor]] = None,
+        conditioning_scale: float = 1.0,
+        return_dict: bool = True,
+    ):
+        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)
+
+        (
+            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)
+
+        # Match t_embedder output dtype to x for layerwise casting compatibility
+        adaln_input = t.type_as(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([len(_) for _ in x_pos_ids], 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)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        x_freqs_cis = x_freqs_cis[:, : x.shape[1]]
+
+        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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.noise_refiner:
+                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
+        else:
+            for layer in self.noise_refiner:
+                x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # cap embed & refine
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        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([len(_) for _ in cap_pos_ids], 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)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        cap_freqs_cis = cap_freqs_cis[:, : cap_feats.shape[1]]
+
+        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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.context_refiner:
+                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
+        else:
+            for layer in self.context_refiner:
+                cap_feats = layer(cap_feats, cap_attn_mask, 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
+
+        ## ControlNet start
+
+        controlnet_block_samples = None
+        if control_context is not None:
+            control_context = torch.cat(control_context, dim=0)
+            control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+            control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+            control_context = list(control_context.split(x_item_seqlens, dim=0))
+
+            control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                for layer in self.control_noise_refiner:
+                    control_context = self._gradient_checkpointing_func(
+                        layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
+                    )
+            else:
+                for layer in self.control_noise_refiner:
+                    control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
+
+            # unified
+            control_context_unified = []
+            for i in range(bsz):
+                x_len = x_item_seqlens[i]
+                cap_len = cap_item_seqlens[i]
+                control_context_unified.append(torch.cat([control_context[i][:x_len], cap_feats[i][:cap_len]]))
+            control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
+
+            for layer in self.control_layers:
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    control_context_unified = self._gradient_checkpointing_func(
+                        layer, control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
+                    )
+                else:
+                    control_context_unified = layer(
+                        control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
+                    )
+
+            hints = torch.unbind(control_context_unified)[:-1]
+            controlnet_block_samples = {
+                layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)
+            }
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer_idx, layer in enumerate(self.layers):
+                unified = self._gradient_checkpointing_func(
+                    layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
+                )
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
+        else:
+            for layer_idx, layer in enumerate(self.layers):
+                unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
+
+        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)
+
+        if not return_dict:
+            return (x,)
+
+        return Transformer2DModelOutput(sample=x)
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index d0460cf09244..2faea94fe134 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -22,7 +22,7 @@
 from ...loaders import FromSingleFileMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.controlnets import ZImageControlNetModel
-from ...models.transformers import ZImageTransformer2DModel
+from ...models.transformers import ZImageControlTransformer2DModel, ZImageTransformer2DModel
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import logging, replace_example_docstring
@@ -167,10 +167,12 @@ def __init__(
         vae: AutoencoderKL,
         text_encoder: PreTrainedModel,
         tokenizer: AutoTokenizer,
-        transformer: ZImageTransformer2DModel,
+        transformer: Union[ZImageControlTransformer2DModel, ZImageTransformer2DModel],
         controlnet: ZImageControlNetModel,
     ):
         super().__init__()
+        if isinstance(transformer, ZImageTransformer2DModel):
+            transformer = ZImageControlTransformer2DModel.from_controlnet(transformer, controlnet)
 
         self.register_modules(
             vae=vae,
@@ -599,20 +601,12 @@ def __call__(
                 latent_model_input = latent_model_input.unsqueeze(2)
                 latent_model_input_list = list(latent_model_input.unbind(dim=0))
 
-                controlnet_block_samples = self.controlnet(
-                    self.transformer,
-                    latent_model_input_list,
-                    prompt_embeds_model_input,
-                    control_image,
-                    timestep_model_input,
-                    conditioning_scale=controlnet_conditioning_scale,
-                )
-
                 model_out_list = self.transformer(
                     latent_model_input_list,
                     timestep_model_input,
                     prompt_embeds_model_input,
-                    controlnet_block_samples=controlnet_block_samples,
+                    control_context=control_image,
+                    conditioning_scale=controlnet_conditioning_scale,
                 )[0]
 
                 if apply_cfg:

From f63a5a8ddf474ae76e04bbf98f9cb63a7d2f1c24 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 5 Dec 2025 21:11:44 +0000
Subject: [PATCH 17/38] ModuleDict

---
 .../models/transformers/transformer_z_image_control.py        | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/models/transformers/transformer_z_image_control.py b/src/diffusers/models/transformers/transformer_z_image_control.py
index 61b752f58f68..90c5bd6c7f24 100644
--- a/src/diffusers/models/transformers/transformer_z_image_control.py
+++ b/src/diffusers/models/transformers/transformer_z_image_control.py
@@ -471,8 +471,8 @@ def from_controlnet(
         for i, control_layer in enumerate(controlnet.control_layers):
             model.control_layers[i].load_state_dict(control_layer.state_dict())
 
-        for i, control_all_x_embedder in enumerate(controlnet.control_all_x_embedder):
-            model.control_all_x_embedder[i].load_state_dict(control_all_x_embedder.state_dict())
+        for key, control_all_x_embedder in controlnet.control_all_x_embedder.items():
+            model.control_all_x_embedder[key].load_state_dict(control_all_x_embedder.state_dict())
 
         for i, control_noise_refiner in enumerate(controlnet.control_noise_refiner):
             model.control_noise_refiner[i].load_state_dict(control_noise_refiner.state_dict())

From f9540cbb14e02e4498e123ddbe9d0fd6031ed830 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 5 Dec 2025 21:13:07 +0000
Subject: [PATCH 18/38] patchify control_context

---
 .../transformer_z_image_control.py            | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/src/diffusers/models/transformers/transformer_z_image_control.py b/src/diffusers/models/transformers/transformer_z_image_control.py
index 90c5bd6c7f24..9d48bb15cc6a 100644
--- a/src/diffusers/models/transformers/transformer_z_image_control.py
+++ b/src/diffusers/models/transformers/transformer_z_image_control.py
@@ -610,6 +610,34 @@ def patchify_and_embed(
             all_cap_pad_mask,
         )
 
+    def patchify(
+        self,
+        all_image: List[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        all_image_out = []
+
+        for i, image in enumerate(all_image):
+            ### Process Image
+            C, F, H, W = image.size()
+            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
+
+            # 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
+
     def forward(
         self,
         x: List[torch.Tensor],
@@ -719,6 +747,7 @@ def forward(
 
         controlnet_block_samples = None
         if control_context is not None:
+            control_context = self.patchify(control_context, patch_size, f_patch_size)
             control_context = torch.cat(control_context, dim=0)
             control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
 

From 3e472ac4a43dd7082dbe7d0851a0705afa72f0aa Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 5 Dec 2025 21:23:36 +0000
Subject: [PATCH 19/38] transformer weights

---
 .../transformer_z_image_control.py            | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/src/diffusers/models/transformers/transformer_z_image_control.py b/src/diffusers/models/transformers/transformer_z_image_control.py
index 9d48bb15cc6a..55116bb75690 100644
--- a/src/diffusers/models/transformers/transformer_z_image_control.py
+++ b/src/diffusers/models/transformers/transformer_z_image_control.py
@@ -468,6 +468,29 @@ def from_controlnet(
         if not load_weights:
             return model
 
+        for key, all_x_embedder in transformer.all_x_embedder.items():
+            model.all_x_embedder[key].load_state_dict(all_x_embedder.state_dict())
+
+        for key, all_final_layer in transformer.all_final_layer.items():
+            model.all_final_layer[key].load_state_dict(all_final_layer.state_dict())
+
+        for i, noise_refiner in enumerate(transformer.noise_refiner):
+            model.noise_refiner[i].load_state_dict(noise_refiner.state_dict())
+
+        for i, context_refiner in enumerate(transformer.context_refiner):
+            model.context_refiner[i].load_state_dict(context_refiner.state_dict())
+
+        model.t_embedder.load_state_dict(transformer.t_embedder.state_dict())
+
+        model.cap_embedder.load_state_dict(transformer.cap_embedder.state_dict())
+
+        model.x_pad_token = transformer.x_pad_token
+
+        model.cap_pad_token = transformer.cap_pad_token
+
+        for i, layer in enumerate(transformer.layers):
+            model.layers[i].load_state_dict(layer.state_dict())
+
         for i, control_layer in enumerate(controlnet.control_layers):
             model.control_layers[i].load_state_dict(control_layer.state_dict())
 

From 0e7c643f02b523dd37b3b598e2b16c4c6839f4e9 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 5 Dec 2025 21:30:08 +0000
Subject: [PATCH 20/38] -enumerate in ZImageTransformer2DModel

---
 src/diffusers/models/transformers/transformer_z_image.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 7c01361b681d..5c401b9d202b 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -635,12 +635,12 @@ def forward(
             unified_attn_mask[i, :seq_len] = 1
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer_idx, layer in enumerate(self.layers):
+            for layer in self.layers:
                 unified = self._gradient_checkpointing_func(
                     layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
                 )
         else:
-            for layer_idx, layer in enumerate(self.layers):
+            for layer in self.layers:
                 unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
 
         unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)

From a00f1048e0a214fd09764cff5dd3afb0300fba1d Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 11 Dec 2025 01:23:06 +0000
Subject: [PATCH 21/38] Option 3

---
 src/diffusers/__init__.py                     |   2 -
 src/diffusers/loaders/peft.py                 |   1 -
 src/diffusers/models/__init__.py              |   2 -
 .../models/controlnets/controlnet_z_image.py  | 395 ++++++++-
 src/diffusers/models/transformers/__init__.py |   1 -
 .../transformers/transformer_z_image.py       |  13 +-
 .../transformer_z_image_control.py            | 836 ------------------
 .../z_image/pipeline_z_image_controlnet.py    |  18 +-
 8 files changed, 412 insertions(+), 856 deletions(-)
 delete mode 100644 src/diffusers/models/transformers/transformer_z_image_control.py

diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index f9a57bdfc85b..f0af3513255a 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -279,7 +279,6 @@
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
             "ZImageTransformer2DModel",
-            "ZImageControlTransformer2DModel",
             "ZImageControlNetModel",
             "attention_backend",
         ]
@@ -1016,7 +1015,6 @@
             WanTransformer3DModel,
             WanVACETransformer3DModel,
             ZImageControlNetModel,
-            ZImageControlTransformer2DModel,
             ZImageTransformer2DModel,
             attention_backend,
         )
diff --git a/src/diffusers/loaders/peft.py b/src/diffusers/loaders/peft.py
index 62182f2d205f..3f8519bbfa32 100644
--- a/src/diffusers/loaders/peft.py
+++ b/src/diffusers/loaders/peft.py
@@ -65,7 +65,6 @@
     "QwenImageTransformer2DModel": lambda model_cls, weights: weights,
     "Flux2Transformer2DModel": lambda model_cls, weights: weights,
     "ZImageTransformer2DModel": lambda model_cls, weights: weights,
-    "ZImageControlTransformer2DModel": lambda model_cls, weights: weights,
 }
 
 
diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py
index 48d06def1c3c..7ea15ef2a215 100755
--- a/src/diffusers/models/__init__.py
+++ b/src/diffusers/models/__init__.py
@@ -117,7 +117,6 @@
     _import_structure["transformers.transformer_wan_animate"] = ["WanAnimateTransformer3DModel"]
     _import_structure["transformers.transformer_wan_vace"] = ["WanVACETransformer3DModel"]
     _import_structure["transformers.transformer_z_image"] = ["ZImageTransformer2DModel"]
-    _import_structure["transformers.transformer_z_image_control"] = ["ZImageControlTransformer2DModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
     _import_structure["unets.unet_2d"] = ["UNet2DModel"]
     _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
@@ -232,7 +231,6 @@
             WanAnimateTransformer3DModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
-            ZImageControlTransformer2DModel,
             ZImageTransformer2DModel,
         )
         from .unets import (
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 0972fb46c07b..ea8eaf0a0788 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -16,17 +16,28 @@
 
 import torch
 import torch.nn as nn
+from torch.nn.utils.rnn import pad_sequence
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
+from ...models.attention_processor import Attention
+from ...models.normalization import RMSNorm
+from ...utils.torch_utils import maybe_allow_in_graph
 from ..controlnets.controlnet import zero_module
 from ..modeling_utils import ModelMixin
 from ..transformers.transformer_z_image import (
+    ADALN_EMBED_DIM,
+    SEQ_MULTI_OF,
+    FeedForward,
+    RopeEmbedder,
+    TimestepEmbedder,
     ZImageTransformerBlock,
+    ZSingleStreamAttnProcessor,
 )
 
 
-class ZImageControlTransformerBlock(ZImageTransformerBlock):
+@maybe_allow_in_graph
+class ZImageControlTransformerBlock(nn.Module):
     def __init__(
         self,
         layer_id: int,
@@ -38,7 +49,38 @@ def __init__(
         modulation=True,
         block_id=0,
     ):
-        super().__init__(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm, modulation)
+        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(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=dim // n_heads,
+            heads=n_heads,
+            qk_norm="rms_norm" if qk_norm else None,
+            eps=1e-5,
+            bias=False,
+            out_bias=False,
+            processor=ZSingleStreamAttnProcessor(),
+        )
+
+        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))
+
+        # Control variant start
         self.block_id = block_id
         if block_id == 0:
             self.before_proj = zero_module(nn.Linear(self.dim, self.dim))
@@ -52,6 +94,7 @@ def forward(
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor] = None,
     ):
+        # Control
         if self.block_id == 0:
             c = self.before_proj(c) + x
             all_c = []
@@ -59,7 +102,30 @@ def forward(
             all_c = list(torch.unbind(c))
             c = all_c.pop(-1)
 
-        c = super().forward(c, attn_mask, freqs_cis, adaln_input)
+        # Compared to `ZImageTransformerBlock` x -> c
+        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(c) * scale_msa, attention_mask=attn_mask, freqs_cis=freqs_cis
+            )
+            c = c + gate_msa * self.attention_norm2(attn_out)
+
+            # FFN block
+            c = c + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(c) * scale_mlp))
+        else:
+            # Attention block
+            attn_out = self.attention(self.attention_norm1(c), attention_mask=attn_mask, freqs_cis=freqs_cis)
+            c = c + self.attention_norm2(attn_out)
+
+            # FFN block
+            c = c + self.ffn_norm2(self.feed_forward(self.ffn_norm1(c)))
+
+        # Control
         c_skip = self.after_proj(c)
         all_c += [c_skip, c]
         c = torch.stack(all_c)
@@ -119,5 +185,324 @@ def __init__(
             ]
         )
 
-    def forward(self):
-        pass
+        self.t_embedder: Optional[TimestepEmbedder] = None
+        self.all_x_embedder: Optional[nn.ModuleDict] = None
+        self.cap_embedder: Optional[nn.Sequential] = None
+        self.rope_embedder: Optional[RopeEmbedder] = None
+        self.noise_refiner: Optional[nn.ModuleList] = None
+        self.context_refiner: Optional[nn.ModuleList] = None
+        self.x_pad_token: Optional[nn.Parameter] = None
+        self.cap_pad_token: Optional[nn.Parameter] = None
+
+    @classmethod
+    def from_transformer(cls, controlnet, transformer):
+        controlnet.t_embedder = transformer.t_embedder
+        controlnet.all_x_embedder = transformer.all_x_embedder
+        controlnet.cap_embedder = transformer.cap_embedder
+        controlnet.rope_embedder = transformer.rope_embedder
+        controlnet.noise_refiner = transformer.noise_refiner
+        controlnet.context_refiner = transformer.context_refiner
+        controlnet.x_pad_token = transformer.x_pad_token
+        controlnet.cap_pad_token = transformer.cap_pad_token
+        return controlnet
+
+    @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
+            cap_pad_mask = torch.cat(
+                [
+                    torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_cap_pad_mask.append(
+                cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
+            )
+
+            # 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_padded_pos_ids = torch.cat(
+                [
+                    image_ori_pos_ids,
+                    self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                    .flatten(0, 2)
+                    .repeat(image_padding_len, 1),
+                ],
+                dim=0,
+            )
+            all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
+            # pad mask
+            image_pad_mask = torch.cat(
+                [
+                    torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_image_pad_mask.append(
+                image_pad_mask
+                if image_padding_len > 0
+                else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
+            )
+            # padded feature
+            image_padded_feat = torch.cat(
+                [image, image[-1:].repeat(image_padding_len, 1)],
+                dim=0,
+            )
+            all_image_out.append(image_padded_feat if image_padding_len > 0 else image)
+
+        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 patchify(
+        self,
+        all_image: List[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        all_image_out = []
+
+        for i, image in enumerate(all_image):
+            ### Process Image
+            C, F, H, W = image.size()
+            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
+
+            # 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
+
+    def forward(
+        self,
+        x: List[torch.Tensor],
+        t,
+        cap_feats: List[torch.Tensor],
+        control_context: List[torch.Tensor],
+        conditioning_scale: float = 1.0,
+        patch_size=2,
+        f_patch_size=1,
+    ):
+        if (
+            self.t_embedder is None
+            or self.all_x_embedder is None
+            or self.cap_embedder is None
+            or self.rope_embedder is None
+            or self.noise_refiner is None
+            or self.context_refiner is None
+            or self.x_pad_token is None
+            or self.cap_pad_token is None
+        ):
+            raise ValueError(
+                "Required modules are `None`, use `from_transformer` to share required modules from `transformer`."
+            )
+
+        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)
+
+        (
+            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)
+
+        # Match t_embedder output dtype to x for layerwise casting compatibility
+        adaln_input = t.type_as(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([len(_) for _ in x_pos_ids], 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)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        x_freqs_cis = x_freqs_cis[:, : x.shape[1]]
+
+        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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.noise_refiner:
+                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
+        else:
+            for layer in self.noise_refiner:
+                x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # cap embed & refine
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        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([len(_) for _ in cap_pos_ids], 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)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        cap_freqs_cis = cap_freqs_cis[:, : cap_feats.shape[1]]
+
+        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
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.context_refiner:
+                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
+        else:
+            for layer in self.context_refiner:
+                cap_feats = layer(cap_feats, cap_attn_mask, 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
+
+        ## ControlNet start
+        control_context = self.patchify(control_context, patch_size, f_patch_size)
+        control_context = torch.cat(control_context, dim=0)
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+        control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        control_context = list(control_context.split(x_item_seqlens, dim=0))
+
+        control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.control_noise_refiner:
+                control_context = self._gradient_checkpointing_func(
+                    layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
+                )
+        else:
+            for layer in self.control_noise_refiner:
+                control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # unified
+        control_context_unified = []
+        for i in range(bsz):
+            x_len = x_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            control_context_unified.append(torch.cat([control_context[i][:x_len], cap_feats[i][:cap_len]]))
+        control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
+
+        for layer in self.control_layers:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                control_context_unified = self._gradient_checkpointing_func(
+                    layer, control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
+                )
+            else:
+                control_context_unified = layer(
+                    control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
+                )
+
+        hints = torch.unbind(control_context_unified)[:-1]
+        controlnet_block_samples = {
+            layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)
+        }
+        return controlnet_block_samples
diff --git a/src/diffusers/models/transformers/__init__.py b/src/diffusers/models/transformers/__init__.py
index 13322aa29ae4..a42f6b2716e1 100755
--- a/src/diffusers/models/transformers/__init__.py
+++ b/src/diffusers/models/transformers/__init__.py
@@ -48,4 +48,3 @@
     from .transformer_wan_animate import WanAnimateTransformer3DModel
     from .transformer_wan_vace import WanVACETransformer3DModel
     from .transformer_z_image import ZImageTransformer2DModel
-    from .transformer_z_image_control import ZImageControlTransformer2DModel
diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 5c401b9d202b..17197db3a441 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -536,6 +536,7 @@ def forward(
         x: List[torch.Tensor],
         t,
         cap_feats: List[torch.Tensor],
+        controlnet_block_samples: Optional[Dict[int, torch.Tensor]] = None,
         patch_size=2,
         f_patch_size=1,
         return_dict: bool = True,
@@ -635,13 +636,19 @@ def forward(
             unified_attn_mask[i, :seq_len] = 1
 
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.layers:
+            for layer_idx, layer in enumerate(self.layers):
                 unified = self._gradient_checkpointing_func(
                     layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
                 )
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
         else:
-            for layer in self.layers:
+            for layer_idx, layer in enumerate(self.layers):
                 unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
 
         unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
         unified = list(unified.unbind(dim=0))
diff --git a/src/diffusers/models/transformers/transformer_z_image_control.py b/src/diffusers/models/transformers/transformer_z_image_control.py
deleted file mode 100644
index 55116bb75690..000000000000
--- a/src/diffusers/models/transformers/transformer_z_image_control.py
+++ /dev/null
@@ -1,836 +0,0 @@
-# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-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 ...configuration_utils import ConfigMixin, FrozenDict, register_to_config
-from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...models.attention_processor import Attention
-from ...models.modeling_utils import ModelMixin
-from ...models.normalization import RMSNorm
-from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention_dispatch import dispatch_attention_fn
-from ..modeling_outputs import Transformer2DModelOutput
-from .transformer_z_image import ZImageTransformer2DModel
-
-
-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)
-        weight_dtype = self.mlp[0].weight.dtype
-        compute_dtype = getattr(self.mlp[0], "compute_dtype", None)
-        if weight_dtype.is_floating_point:
-            t_freq = t_freq.to(weight_dtype)
-        elif compute_dtype is not None:
-            t_freq = t_freq.to(compute_dtype)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class ZSingleStreamAttnProcessor:
-    """
-    Processor for Z-Image single stream attention that adapts the existing Attention class to match the behavior of the
-    original Z-ImageAttention module.
-    """
-
-    _attention_backend = None
-    _parallel_config = None
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(
-                "ZSingleStreamAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
-            )
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        freqs_cis: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        query = query.unflatten(-1, (attn.heads, -1))
-        key = key.unflatten(-1, (attn.heads, -1))
-        value = value.unflatten(-1, (attn.heads, -1))
-
-        # Apply Norms
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.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)
-
-        # From [batch, seq_len] to [batch, 1, 1, seq_len] -> broadcast to [batch, heads, seq_len, seq_len]
-        if attention_mask is not None and attention_mask.ndim == 2:
-            attention_mask = attention_mask[:, None, None, :]
-
-        # Compute joint attention
-        hidden_states = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=False,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-
-        # Reshape back
-        hidden_states = hidden_states.flatten(2, 3)
-        hidden_states = hidden_states.to(dtype)
-
-        output = attn.to_out[0](hidden_states)
-        if len(attn.to_out) > 1:  # dropout
-            output = attn.to_out[1](output)
-
-        return output
-
-
-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)))
-
-
-@maybe_allow_in_graph
-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(
-            query_dim=dim,
-            cross_attention_dim=None,
-            dim_head=dim // n_heads,
-            heads=n_heads,
-            qk_norm="rms_norm" if qk_norm else None,
-            eps=1e-5,
-            bias=False,
-            out_bias=False,
-            processor=ZSingleStreamAttnProcessor(),
-        )
-
-        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, attention_mask=attn_mask, 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), attention_mask=attn_mask, 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]
-        else:
-            # Ensure freqs_cis are on the same device as ids
-            if self.freqs_cis[0].device != device:
-                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 ZImageControlTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
-    _supports_gradient_checkpointing = True
-    _no_split_modules = ["ZImageTransformerBlock", "ZImageControlTransformerBlock"]
-    _repeated_blocks = ["ZImageTransformerBlock", "ZImageControlTransformerBlock"]
-    _skip_layerwise_casting_patterns = ["t_embedder", "cap_embedder"]  # precision sensitive layers
-
-    @register_to_config
-    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],
-        control_layers_places: List[int] = None,
-        control_in_dim=None,
-    ) -> None:
-        super().__init__()
-        from ...models.controlnets.controlnet_z_image import ZImageControlTransformerBlock
-
-        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)
-
-        self.control_layers_places = control_layers_places
-        self.control_in_dim = control_in_dim
-
-        assert 0 in self.control_layers_places
-
-        # control blocks
-        self.control_layers = nn.ModuleList(
-            [
-                ZImageControlTransformerBlock(i, dim, n_heads, n_kv_heads, norm_eps, qk_norm, block_id=i)
-                for i in self.control_layers_places
-            ]
-        )
-
-        # control patch embeddings
-        all_x_embedder = {}
-        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 * self.control_in_dim, dim, bias=True)
-            all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
-
-        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
-        self.control_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)
-            ]
-        )
-
-    @classmethod
-    def from_controlnet(
-        cls,
-        transformer: ZImageTransformer2DModel,
-        controlnet,
-        load_weights: bool = True,
-    ):
-        controlnet.to(device=transformer.device)
-
-        if transformer.config["dim"] != controlnet.config["dim"]:
-            raise ValueError("Incompatible ControlNet, got a different dim.")
-
-        config = dict(transformer.config)
-        config["_class_name"] = cls.__name__
-
-        config["control_layers_places"] = controlnet.config["control_layers_places"]
-        config["control_in_dim"] = controlnet.config["control_in_dim"]
-
-        expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
-        config = FrozenDict({k: config.get(k) for k in config if k in expected_kwargs or k in optional_kwargs})
-        config["_class_name"] = cls.__name__
-        model = cls.from_config(config)
-
-        if not load_weights:
-            return model
-
-        for key, all_x_embedder in transformer.all_x_embedder.items():
-            model.all_x_embedder[key].load_state_dict(all_x_embedder.state_dict())
-
-        for key, all_final_layer in transformer.all_final_layer.items():
-            model.all_final_layer[key].load_state_dict(all_final_layer.state_dict())
-
-        for i, noise_refiner in enumerate(transformer.noise_refiner):
-            model.noise_refiner[i].load_state_dict(noise_refiner.state_dict())
-
-        for i, context_refiner in enumerate(transformer.context_refiner):
-            model.context_refiner[i].load_state_dict(context_refiner.state_dict())
-
-        model.t_embedder.load_state_dict(transformer.t_embedder.state_dict())
-
-        model.cap_embedder.load_state_dict(transformer.cap_embedder.state_dict())
-
-        model.x_pad_token = transformer.x_pad_token
-
-        model.cap_pad_token = transformer.cap_pad_token
-
-        for i, layer in enumerate(transformer.layers):
-            model.layers[i].load_state_dict(layer.state_dict())
-
-        for i, control_layer in enumerate(controlnet.control_layers):
-            model.control_layers[i].load_state_dict(control_layer.state_dict())
-
-        for key, control_all_x_embedder in controlnet.control_all_x_embedder.items():
-            model.control_all_x_embedder[key].load_state_dict(control_all_x_embedder.state_dict())
-
-        for i, control_noise_refiner in enumerate(controlnet.control_noise_refiner):
-            model.control_noise_refiner[i].load_state_dict(control_noise_refiner.state_dict())
-
-        model.to(transformer.dtype)
-
-        return model
-
-    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
-            cap_pad_mask = torch.cat(
-                [
-                    torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
-                    torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
-                ],
-                dim=0,
-            )
-            all_cap_pad_mask.append(
-                cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
-            )
-
-            # 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_padded_pos_ids = torch.cat(
-                [
-                    image_ori_pos_ids,
-                    self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
-                    .flatten(0, 2)
-                    .repeat(image_padding_len, 1),
-                ],
-                dim=0,
-            )
-            all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
-            # pad mask
-            image_pad_mask = torch.cat(
-                [
-                    torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
-                    torch.ones((image_padding_len,), dtype=torch.bool, device=device),
-                ],
-                dim=0,
-            )
-            all_image_pad_mask.append(
-                image_pad_mask
-                if image_padding_len > 0
-                else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
-            )
-            # padded feature
-            image_padded_feat = torch.cat(
-                [image, image[-1:].repeat(image_padding_len, 1)],
-                dim=0,
-            )
-            all_image_out.append(image_padded_feat if image_padding_len > 0 else image)
-
-        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 patchify(
-        self,
-        all_image: List[torch.Tensor],
-        patch_size: int,
-        f_patch_size: int,
-    ):
-        pH = pW = patch_size
-        pF = f_patch_size
-        all_image_out = []
-
-        for i, image in enumerate(all_image):
-            ### Process Image
-            C, F, H, W = image.size()
-            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
-
-            # 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
-
-    def forward(
-        self,
-        x: List[torch.Tensor],
-        t,
-        cap_feats: List[torch.Tensor],
-        patch_size=2,
-        f_patch_size=1,
-        control_context: Optional[List[torch.Tensor]] = None,
-        conditioning_scale: float = 1.0,
-        return_dict: bool = True,
-    ):
-        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)
-
-        (
-            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)
-
-        # Match t_embedder output dtype to x for layerwise casting compatibility
-        adaln_input = t.type_as(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([len(_) for _ in x_pos_ids], 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)
-        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
-        x_freqs_cis = x_freqs_cis[:, : x.shape[1]]
-
-        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
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.noise_refiner:
-                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
-        else:
-            for layer in self.noise_refiner:
-                x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
-
-        # cap embed & refine
-        cap_item_seqlens = [len(_) for _ in cap_feats]
-        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([len(_) for _ in cap_pos_ids], 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)
-        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
-        cap_freqs_cis = cap_freqs_cis[:, : cap_feats.shape[1]]
-
-        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
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.context_refiner:
-                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
-        else:
-            for layer in self.context_refiner:
-                cap_feats = layer(cap_feats, cap_attn_mask, 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
-
-        ## ControlNet start
-
-        controlnet_block_samples = None
-        if control_context is not None:
-            control_context = self.patchify(control_context, patch_size, f_patch_size)
-            control_context = torch.cat(control_context, dim=0)
-            control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
-
-            control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
-            control_context = list(control_context.split(x_item_seqlens, dim=0))
-
-            control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
-
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                for layer in self.control_noise_refiner:
-                    control_context = self._gradient_checkpointing_func(
-                        layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
-                    )
-            else:
-                for layer in self.control_noise_refiner:
-                    control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
-
-            # unified
-            control_context_unified = []
-            for i in range(bsz):
-                x_len = x_item_seqlens[i]
-                cap_len = cap_item_seqlens[i]
-                control_context_unified.append(torch.cat([control_context[i][:x_len], cap_feats[i][:cap_len]]))
-            control_context_unified = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
-
-            for layer in self.control_layers:
-                if torch.is_grad_enabled() and self.gradient_checkpointing:
-                    control_context_unified = self._gradient_checkpointing_func(
-                        layer, control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
-                    )
-                else:
-                    control_context_unified = layer(
-                        control_context_unified, unified, unified_attn_mask, unified_freqs_cis, adaln_input
-                    )
-
-            hints = torch.unbind(control_context_unified)[:-1]
-            controlnet_block_samples = {
-                layer_idx: hints[idx] * conditioning_scale for idx, layer_idx in enumerate(self.control_layers_places)
-            }
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer_idx, layer in enumerate(self.layers):
-                unified = self._gradient_checkpointing_func(
-                    layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
-                )
-                if controlnet_block_samples is not None:
-                    if layer_idx in controlnet_block_samples:
-                        unified = unified + controlnet_block_samples[layer_idx]
-        else:
-            for layer_idx, layer in enumerate(self.layers):
-                unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
-                if controlnet_block_samples is not None:
-                    if layer_idx in controlnet_block_samples:
-                        unified = unified + controlnet_block_samples[layer_idx]
-
-        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)
-
-        if not return_dict:
-            return (x,)
-
-        return Transformer2DModelOutput(sample=x)
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 2faea94fe134..904259f805ba 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -22,7 +22,7 @@
 from ...loaders import FromSingleFileMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.controlnets import ZImageControlNetModel
-from ...models.transformers import ZImageControlTransformer2DModel, ZImageTransformer2DModel
+from ...models.transformers import ZImageTransformer2DModel
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import logging, replace_example_docstring
@@ -167,12 +167,11 @@ def __init__(
         vae: AutoencoderKL,
         text_encoder: PreTrainedModel,
         tokenizer: AutoTokenizer,
-        transformer: Union[ZImageControlTransformer2DModel, ZImageTransformer2DModel],
+        transformer: ZImageTransformer2DModel,
         controlnet: ZImageControlNetModel,
     ):
         super().__init__()
-        if isinstance(transformer, ZImageTransformer2DModel):
-            transformer = ZImageControlTransformer2DModel.from_controlnet(transformer, controlnet)
+        controlnet = ZImageControlNetModel.from_transformer(controlnet, transformer)
 
         self.register_modules(
             vae=vae,
@@ -601,12 +600,19 @@ def __call__(
                 latent_model_input = latent_model_input.unsqueeze(2)
                 latent_model_input_list = list(latent_model_input.unbind(dim=0))
 
-                model_out_list = self.transformer(
+                controlnet_block_samples = self.controlnet(
                     latent_model_input_list,
                     timestep_model_input,
                     prompt_embeds_model_input,
-                    control_context=control_image,
+                    control_image,
                     conditioning_scale=controlnet_conditioning_scale,
+                )
+
+                model_out_list = self.transformer(
+                    latent_model_input_list,
+                    timestep_model_input,
+                    prompt_embeds_model_input,
+                    controlnet_block_samples=controlnet_block_samples,
                 )[0]
 
                 if apply_cfg:

From a961402959395d23a666a49b067bbdb12de48727 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 11 Dec 2025 03:36:40 +0000
Subject: [PATCH 22/38] from_single_file

---
 src/diffusers/loaders/single_file_model.py    |  8 ++++++++
 src/diffusers/loaders/single_file_utils.py    | 19 +++++++++++++++++++
 .../models/controlnets/controlnet_z_image.py  |  3 ++-
 3 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/src/diffusers/loaders/single_file_model.py b/src/diffusers/loaders/single_file_model.py
index 803fdfc2d952..57f1594e5e87 100644
--- a/src/diffusers/loaders/single_file_model.py
+++ b/src/diffusers/loaders/single_file_model.py
@@ -53,6 +53,7 @@
     create_controlnet_diffusers_config_from_ldm,
     create_unet_diffusers_config_from_ldm,
     create_vae_diffusers_config_from_ldm,
+    create_z_image_controlnet_config,
     fetch_diffusers_config,
     fetch_original_config,
     load_single_file_checkpoint,
@@ -172,6 +173,10 @@
         "checkpoint_mapping_fn": convert_z_image_transformer_checkpoint_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "ZImageControlNetModel": {
+        "checkpoint_mapping_fn": lambda x: x,
+        "config_create_fn": create_z_image_controlnet_config,
+    },
 }
 
 
@@ -369,6 +374,9 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             diffusers_model_config = config_mapping_fn(
                 original_config=original_config, checkpoint=checkpoint, **config_mapping_kwargs
             )
+        elif "config_create_fn" in mapping_functions:
+            config_create_fn = mapping_functions["config_create_fn"]
+            diffusers_model_config = config_create_fn()
         else:
             if config is not None:
                 if isinstance(config, str):
diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index b866a5a21ae3..6f24b0f848ec 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -121,6 +121,7 @@
     "instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
     "lumina2": ["model.diffusion_model.cap_embedder.0.weight", "cap_embedder.0.weight"],
     "z-image-turbo": "cap_embedder.0.weight",
+    "z-image-turbo-controlnet": "control_all_x_embedder.2-1.weight",
     "sana": [
         "blocks.0.cross_attn.q_linear.weight",
         "blocks.0.cross_attn.q_linear.bias",
@@ -779,6 +780,9 @@ def infer_diffusers_model_type(checkpoint):
         else:
             raise ValueError(f"Unexpected x_embedder shape: {x_embedder_shape} when loading Cosmos 2.0 model.")
 
+    elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet"] in checkpoint:
+        model_type = "z-image-turbo-controlnet"
+
     else:
         model_type = "v1"
 
@@ -3885,3 +3889,18 @@ def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str)
             handler_fn_inplace(key, converted_state_dict)
 
     return converted_state_dict
+
+
+def create_z_image_controlnet_config():
+    return {
+        "all_f_patch_size": [1],
+        "all_patch_size": [2],
+        "control_in_dim": 16,
+        "control_layers_places": [0, 5, 10, 15, 20, 25],
+        "dim": 3840,
+        "n_heads": 30,
+        "n_kv_heads": 30,
+        "n_refiner_layers": 2,
+        "norm_eps": 1e-05,
+        "qk_norm": True,
+    }
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index ea8eaf0a0788..2e518fd46f0a 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -20,6 +20,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
+from ...loaders.single_file_model import FromOriginalModelMixin
 from ...models.attention_processor import Attention
 from ...models.normalization import RMSNorm
 from ...utils.torch_utils import maybe_allow_in_graph
@@ -132,7 +133,7 @@ def forward(
         return c
 
 
-class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     _supports_gradient_checkpointing = True
 
     @register_to_config

From 6e1c2189d005ec036fd9ffe06c13878b4d18edc6 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 11 Dec 2025 03:42:05 +0000
Subject: [PATCH 23/38] Remove convert script

---
 ...convert_z_image_controlnet_to_diffusers.py | 70 -------------------
 1 file changed, 70 deletions(-)
 delete mode 100644 scripts/convert_z_image_controlnet_to_diffusers.py

diff --git a/scripts/convert_z_image_controlnet_to_diffusers.py b/scripts/convert_z_image_controlnet_to_diffusers.py
deleted file mode 100644
index e5d5f34e36e8..000000000000
--- a/scripts/convert_z_image_controlnet_to_diffusers.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import argparse
-from contextlib import nullcontext
-
-import safetensors.torch
-import torch
-from accelerate import init_empty_weights
-from huggingface_hub import hf_hub_download
-
-from diffusers.models.controlnets.controlnet_z_image import ZImageControlNetModel
-from diffusers.utils.import_utils import is_accelerate_available
-
-
-"""
-python scripts/convert_z_image_controlnet_to_diffusers.py  \
---original_controlnet_repo_id "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union" \
---filename "Z-Image-Turbo-Fun-Controlnet-Union.safetensors"
---output_path "z-image-controlnet-hf/"
-"""
-
-
-CTX = init_empty_weights if is_accelerate_available else nullcontext
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--original_controlnet_repo_id", default=None, type=str)
-parser.add_argument("--filename", default="Z-Image-Turbo-Fun-Controlnet-Union.safetensors", type=str)
-parser.add_argument("--checkpoint_path", default=None, type=str)
-parser.add_argument("--output_path", type=str)
-
-args = parser.parse_args()
-
-
-def load_original_checkpoint(args):
-    if args.original_controlnet_repo_id is not None:
-        ckpt_path = hf_hub_download(repo_id=args.original_controlnet_repo_id, filename=args.filename)
-    elif args.checkpoint_path is not None:
-        ckpt_path = args.checkpoint_path
-    else:
-        raise ValueError(" please provide either `original_controlnet_repo_id` or a local `checkpoint_path`")
-
-    original_state_dict = safetensors.torch.load_file(ckpt_path)
-    return original_state_dict
-
-
-def convert_z_image_controlnet_checkpoint_to_diffusers(original_state_dict):
-    converted_state_dict = {}
-
-    converted_state_dict.update(original_state_dict)
-
-    return converted_state_dict
-
-
-def main(args):
-    original_ckpt = load_original_checkpoint(args)
-
-    control_in_dim = 16
-    control_layers_places = [0, 5, 10, 15, 20, 25]
-
-    converted_controlnet_state_dict = convert_z_image_controlnet_checkpoint_to_diffusers(original_ckpt)
-
-    controlnet = ZImageControlNetModel(
-        control_layers_places=control_layers_places,
-        control_in_dim=control_in_dim,
-    ).to(torch.bfloat16)
-    controlnet.load_state_dict(converted_controlnet_state_dict)
-    print("Saving Z-Image ControlNet in Diffusers format")
-    controlnet.save_pretrained(args.output_path)
-
-
-if __name__ == "__main__":
-    main(args)

From 8e7743a44c410508ad7187819ab5049abed9d7b8 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 11 Dec 2025 21:09:48 +0000
Subject: [PATCH 24/38] Copied from

---
 .../models/controlnets/controlnet_z_image.py  | 276 +++++++++++++++++-
 1 file changed, 267 insertions(+), 9 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 2e518fd46f0a..5b0c0d363f4a 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -12,10 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from typing import List, Optional
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from torch.nn.utils.rnn import pad_sequence
 
 from ...configuration_utils import ConfigMixin, register_to_config
@@ -24,17 +26,271 @@
 from ...models.attention_processor import Attention
 from ...models.normalization import RMSNorm
 from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention_dispatch import dispatch_attention_fn
 from ..controlnets.controlnet import zero_module
 from ..modeling_utils import ModelMixin
-from ..transformers.transformer_z_image import (
-    ADALN_EMBED_DIM,
-    SEQ_MULTI_OF,
-    FeedForward,
-    RopeEmbedder,
-    TimestepEmbedder,
-    ZImageTransformerBlock,
-    ZSingleStreamAttnProcessor,
-)
+
+
+ADALN_EMBED_DIM = 256
+SEQ_MULTI_OF = 32
+
+
+# Copied from diffusers.models.transformers.transformer_z_image.TimestepEmbedder
+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)
+        weight_dtype = self.mlp[0].weight.dtype
+        compute_dtype = getattr(self.mlp[0], "compute_dtype", None)
+        if weight_dtype.is_floating_point:
+            t_freq = t_freq.to(weight_dtype)
+        elif compute_dtype is not None:
+            t_freq = t_freq.to(compute_dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+# Copied from diffusers.models.transformers.transformer_z_image.ZSingleStreamAttnProcessor
+class ZSingleStreamAttnProcessor:
+    """
+    Processor for Z-Image single stream attention that adapts the existing Attention class to match the behavior of the
+    original Z-ImageAttention module.
+    """
+
+    _attention_backend = None
+    _parallel_config = None
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "ZSingleStreamAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(-1, (attn.heads, -1))
+        key = key.unflatten(-1, (attn.heads, -1))
+        value = value.unflatten(-1, (attn.heads, -1))
+
+        # Apply Norms
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.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)
+
+        # From [batch, seq_len] to [batch, 1, 1, seq_len] -> broadcast to [batch, heads, seq_len, seq_len]
+        if attention_mask is not None and attention_mask.ndim == 2:
+            attention_mask = attention_mask[:, None, None, :]
+
+        # Compute joint attention
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+
+        # Reshape back
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(dtype)
+
+        output = attn.to_out[0](hidden_states)
+        if len(attn.to_out) > 1:  # dropout
+            output = attn.to_out[1](output)
+
+        return output
+
+
+# Copied from diffusers.models.transformers.transformer_z_image.FeedForward
+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)))
+
+
+@maybe_allow_in_graph
+# Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformerBlock
+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(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=dim // n_heads,
+            heads=n_heads,
+            qk_norm="rms_norm" if qk_norm else None,
+            eps=1e-5,
+            bias=False,
+            out_bias=False,
+            processor=ZSingleStreamAttnProcessor(),
+        )
+
+        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, attention_mask=attn_mask, 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), attention_mask=attn_mask, 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
+
+
+# Copied from diffusers.models.transformers.transformer_z_image.RopeEmbedder
+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]
+        else:
+            # Ensure freqs_cis are on the same device as ids
+            if self.freqs_cis[0].device != device:
+                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)
 
 
 @maybe_allow_in_graph
@@ -208,6 +464,7 @@ def from_transformer(cls, controlnet, transformer):
         return controlnet
 
     @staticmethod
+    # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformer2DModel.create_coordinate_grid
     def create_coordinate_grid(size, start=None, device=None):
         if start is None:
             start = (0 for _ in size)
@@ -216,6 +473,7 @@ def create_coordinate_grid(size, start=None, device=None):
         grids = torch.meshgrid(axes, indexing="ij")
         return torch.stack(grids, dim=-1)
 
+    # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformer2DModel.patchify_and_embed
     def patchify_and_embed(
         self,
         all_image: List[torch.Tensor],

From c13517029100fa285e245fd5f553c430f77f76fc Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Thu, 11 Dec 2025 21:42:20 +0000
Subject: [PATCH 25/38] Example

---
 .../z_image/pipeline_z_image_controlnet.py    | 23 ++++++++++++-------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 904259f805ba..33dfc1996c1f 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -39,11 +39,17 @@
         >>> from diffusers import ZImageControlNetPipeline
         >>> from diffusers import ZImageControlNetModel
         >>> from diffusers.utils import load_image
+        >>> from huggingface_hub import hf_hub_download
 
-        >>> controlnet_model = "..."
-        >>> controlnet = ZImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
+        >>> controlnet = ZImageControlNetModel.from_single_file(
+        ...     hf_hub_download(
+        ...         "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union",
+        ...         filename="Z-Image-Turbo-Fun-Controlnet-Union.safetensors",
+        ...     ),
+        ...     torch_dtype=torch.bfloat16,
+        ... )
 
-        >>> pipe = ZImageControlNetPipeline.from_pretrained("Z-a-o/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16)
+        >>> pipe = ZImageControlNetPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")
 
         >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
@@ -52,16 +58,17 @@
         >>> # (2) Use flash attention 3
         >>> # pipe.transformer.set_attention_backend("_flash_3")
 
-        >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
-        >>> prompt = "A girl in city, 25 years old, cool, futuristic"
+        >>> control_image = load_image("https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union/resolve/main/asset/pose.jpg?download=true")
+        >>> prompt = "一位年轻女子站在阳光明媚的海岸线上，白裙在轻拂的海风中微微飘动。她拥有一头鲜艳的紫色长发，在风中轻盈舞动，发间系着一个精致的黑色蝴蝶结，与身后柔和的蔚蓝天空形成鲜明对比。她面容清秀，眉目精致，透着一股甜美的青春气息；神情柔和，略带羞涩，目光静静地凝望着远方的地平线，双手自然交叠于身前，仿佛沉浸在思绪之中。在她身后，是辽阔无垠、波光粼粼的大海，阳光洒在海面上，映出温暖的金色光晕。"
         >>> image = pipe(
         ...     prompt,
         ...     control_image=control_image,
-        ...     height=1024,
-        ...     width=1024,
+        ...     controlnet_conditioning_scale=0.75,
+        ...     height=1728,
+        ...     width=992,
         ...     num_inference_steps=9,
         ...     guidance_scale=0.0,
-        ...     generator=torch.Generator("cuda").manual_seed(42),
+        ...     generator=torch.Generator("cuda").manual_seed(43),
         ... ).images[0]
         >>> image.save("zimage.png")
         ```

From a737b3cc1e139fb234a960a130fb1e9cc6dd0d92 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 12 Dec 2025 18:33:01 +0000
Subject: [PATCH 26/38] doc-builder style

---
 .../pipelines/z_image/pipeline_z_image_controlnet.py      | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 33dfc1996c1f..51b2c9bdd445 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -49,7 +49,9 @@
         ...     torch_dtype=torch.bfloat16,
         ... )
 
-        >>> pipe = ZImageControlNetPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16)
+        >>> pipe = ZImageControlNetPipeline.from_pretrained(
+        ...     "Tongyi-MAI/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16
+        ... )
         >>> pipe.to("cuda")
 
         >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
@@ -58,7 +60,9 @@
         >>> # (2) Use flash attention 3
         >>> # pipe.transformer.set_attention_backend("_flash_3")
 
-        >>> control_image = load_image("https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union/resolve/main/asset/pose.jpg?download=true")
+        >>> control_image = load_image(
+        ...     "https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union/resolve/main/asset/pose.jpg?download=true"
+        ... )
         >>> prompt = "一位年轻女子站在阳光明媚的海岸线上，白裙在轻拂的海风中微微飘动。她拥有一头鲜艳的紫色长发，在风中轻盈舞动，发间系着一个精致的黑色蝴蝶结，与身后柔和的蔚蓝天空形成鲜明对比。她面容清秀，眉目精致，透着一股甜美的青春气息；神情柔和，略带羞涩，目光静静地凝望着远方的地平线，双手自然交叠于身前，仿佛沉浸在思绪之中。在她身后，是辽阔无垠、波光粼粼的大海，阳光洒在海面上，映出温暖的金色光晕。"
         >>> image = pipe(
         ...     prompt,

From 7bc847ac389166c79efaaf64378e4b1d0590248b Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 12 Dec 2025 18:33:13 +0000
Subject: [PATCH 27/38] check_dummies

---
 src/diffusers/utils/dummy_pt_objects.py           | 15 +++++++++++++++
 .../utils/dummy_torch_and_transformers_objects.py | 15 +++++++++++++++
 2 files changed, 30 insertions(+)

diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py
index 8628893200fe..d8ff309c186c 100644
--- a/src/diffusers/utils/dummy_pt_objects.py
+++ b/src/diffusers/utils/dummy_pt_objects.py
@@ -1762,6 +1762,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch"])
 
 
+class ZImageControlNetModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class ZImageTransformer2DModel(metaclass=DummyObject):
     _backends = ["torch"]
 
diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py
index ff65372f3c89..e0388e6c143d 100644
--- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py
+++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py
@@ -3812,6 +3812,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class ZImageControlNetPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class ZImageImg2ImgPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

From ffde03532240b0808904068c35199c4ac5185cb1 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Fri, 12 Dec 2025 18:33:23 +0000
Subject: [PATCH 28/38] custom_init_isort

---
 src/diffusers/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index f0af3513255a..a66b81ab408e 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -278,8 +278,8 @@
             "WanAnimateTransformer3DModel",
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
-            "ZImageTransformer2DModel",
             "ZImageControlNetModel",
+            "ZImageTransformer2DModel",
             "attention_backend",
         ]
     )
@@ -667,9 +667,9 @@
             "WuerstchenCombinedPipeline",
             "WuerstchenDecoderPipeline",
             "WuerstchenPriorPipeline",
+            "ZImageControlNetPipeline",
             "ZImageImg2ImgPipeline",
             "ZImagePipeline",
-            "ZImageControlNetPipeline",
         ]
     )
 

From 04388f4698b303785b26bec6179a55aea652a388 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 10:56:49 +0000
Subject: [PATCH 29/38] init v2

---
 src/diffusers/loaders/single_file_model.py    |   3 +-
 src/diffusers/loaders/single_file_utils.py    |  27 ++++-
 .../models/controlnets/controlnet_z_image.py  | 101 +++++++++++++-----
 3 files changed, 98 insertions(+), 33 deletions(-)

diff --git a/src/diffusers/loaders/single_file_model.py b/src/diffusers/loaders/single_file_model.py
index 57f1594e5e87..63b31b34529f 100644
--- a/src/diffusers/loaders/single_file_model.py
+++ b/src/diffusers/loaders/single_file_model.py
@@ -376,7 +376,8 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             )
         elif "config_create_fn" in mapping_functions:
             config_create_fn = mapping_functions["config_create_fn"]
-            diffusers_model_config = config_create_fn()
+            config_create_kwargs = _get_mapping_function_kwargs(config_create_fn, **kwargs)
+            diffusers_model_config = config_create_fn(checkpoint=checkpoint, **config_create_kwargs)
         else:
             if config is not None:
                 if isinstance(config, str):
diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index 6f24b0f848ec..20ab5d827255 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -3891,10 +3891,8 @@ def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str)
     return converted_state_dict
 
 
-def create_z_image_controlnet_config():
-    return {
-        "all_f_patch_size": [1],
-        "all_patch_size": [2],
+def create_z_image_controlnet_config(checkpoint, **kwargs):
+    v1_config = {
         "control_in_dim": 16,
         "control_layers_places": [0, 5, 10, 15, 20, 25],
         "dim": 3840,
@@ -3903,4 +3901,25 @@ def create_z_image_controlnet_config():
         "n_refiner_layers": 2,
         "norm_eps": 1e-05,
         "qk_norm": True,
+        "all_f_patch_size": [1],
+        "all_patch_size": [2],
+    }
+    v2_config = {
+        "control_in_dim": 33,
+        "control_layers_places": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
+        "control_refiner_layers_places": [0, 1],
+        "add_control_noise_refiner": True,
+        "dim": 3840,
+        "n_heads": 30,
+        "n_kv_heads": 30,
+        "n_refiner_layers": 2,
+        "norm_eps": 1e-05,
+        "qk_norm": True,
+        "all_f_patch_size": [1],
+        "all_patch_size": [2],
     }
+    control_x_embedder_weight_shape = checkpoint["control_all_x_embedder.2-1.weight"].shape[1]
+    if control_x_embedder_weight_shape == 64:
+        return v1_config
+    elif control_x_embedder_weight_shape == 132:
+        return v2_config
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 5b0c0d363f4a..f7c7c4033ae4 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -395,6 +395,10 @@ class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
     @register_to_config
     def __init__(
         self,
+        control_layers_places: List[int] = None,
+        control_refiner_layers_places: List[int] = None,
+        control_in_dim=None,
+        add_control_noise_refiner=False,
         all_patch_size=(2,),
         all_f_patch_size=(1,),
         dim=3840,
@@ -403,12 +407,12 @@ def __init__(
         n_kv_heads=30,
         norm_eps=1e-5,
         qk_norm=True,
-        control_layers_places: List[int] = None,
-        control_in_dim=None,
     ):
         super().__init__()
         self.control_layers_places = control_layers_places
         self.control_in_dim = control_in_dim
+        self.control_refiner_layers_places = control_refiner_layers_places
+        self.add_control_noise_refiner = add_control_noise_refiner
 
         assert 0 in self.control_layers_places
 
@@ -427,20 +431,37 @@ def __init__(
             all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
 
         self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
-        self.control_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)
-            ]
-        )
+        if self.add_control_noise_refiner:
+            self.control_noise_refiner = nn.ModuleList(
+                [
+                    ZImageControlTransformerBlock(
+                        1000 + layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        norm_eps,
+                        qk_norm,
+                        modulation=True,
+                        block_id=layer_id,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+        else:
+            self.control_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.t_embedder: Optional[TimestepEmbedder] = None
         self.all_x_embedder: Optional[nn.ModuleDict] = None
@@ -647,11 +668,20 @@ def forward(
             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)
 
+        control_context = self.patchify(control_context, patch_size, f_patch_size)
+        control_context = torch.cat(control_context, dim=0)
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+        control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        control_context = list(control_context.split(x_item_seqlens, dim=0))
+
+        control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
+
+        # x embed & refine
         x = torch.cat(x, dim=0)
         x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
 
@@ -670,12 +700,36 @@ def forward(
         for i, seq_len in enumerate(x_item_seqlens):
             x_attn_mask[i, :seq_len] = 1
 
+        if self.add_control_noise_refiner:
+            for layer in self.control_layers:
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    control_context = self._gradient_checkpointing_func(
+                        layer, control_context, x, x_attn_mask, x_freqs_cis, adaln_input
+                    )
+                else:
+                    control_context = layer(control_context, x, x_attn_mask, x_freqs_cis, adaln_input)
+
+            hints = torch.unbind(control_context)[:-1]
+            control_context = torch.unbind(control_context)[-1]
+            noise_refiner_block_samples = {
+                layer_idx: hints[idx] * conditioning_scale
+                for idx, layer_idx in enumerate(self.control_refiner_layers_places)
+            }
+        else:
+            noise_refiner_block_samples = None
+
         if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.noise_refiner:
+            for layer_idx, layer in enumerate(self.noise_refiner):
                 x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
+                if noise_refiner_block_samples is not None:
+                    if layer_idx in noise_refiner_block_samples:
+                        x = x + noise_refiner_block_samples[layer_idx]
         else:
-            for layer in self.noise_refiner:
+            for layer_idx, layer in enumerate(self.noise_refiner):
                 x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
+                if noise_refiner_block_samples is not None:
+                    if layer_idx in noise_refiner_block_samples:
+                        x = x + noise_refiner_block_samples[layer_idx]
 
         # cap embed & refine
         cap_item_seqlens = [len(_) for _ in cap_feats]
@@ -724,15 +778,6 @@ def forward(
             unified_attn_mask[i, :seq_len] = 1
 
         ## ControlNet start
-        control_context = self.patchify(control_context, patch_size, f_patch_size)
-        control_context = torch.cat(control_context, dim=0)
-        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
-
-        control_context[torch.cat(x_inner_pad_mask)] = self.x_pad_token
-        control_context = list(control_context.split(x_item_seqlens, dim=0))
-
-        control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
-
         if torch.is_grad_enabled() and self.gradient_checkpointing:
             for layer in self.control_noise_refiner:
                 control_context = self._gradient_checkpointing_func(

From 62ee1c1d61a8beed7172d9746247b37b125b4c64 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 12:03:46 +0000
Subject: [PATCH 30/38] handle 2.0 t2i pipeline control_image dimension

---
 .../z_image/pipeline_z_image_controlnet.py         | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
index 51b2c9bdd445..62e26312ff66 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py
@@ -533,6 +533,20 @@ def __call__(
         control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
         control_image = control_image.unsqueeze(2)
 
+        if num_channels_latents != self.controlnet.config.control_in_dim:
+            # For model version 2.0
+            control_image = torch.cat(
+                [
+                    control_image,
+                    torch.zeros(
+                        control_image.shape[0],
+                        self.controlnet.config.control_in_dim - num_channels_latents,
+                        *control_image.shape[2:],
+                    ).to(device=control_image.device, dtype=control_image.dtype),
+                ],
+                dim=1,
+            )
+
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,

From faf5a24c50c691a865bec44d9b7d84d26f4a8603 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 12:28:43 +0000
Subject: [PATCH 31/38] ZImageControlNetInpaintPipeline

---
 src/diffusers/__init__.py                     |   2 +
 src/diffusers/pipelines/__init__.py           |  14 +-
 src/diffusers/pipelines/z_image/__init__.py   |   2 +
 .../pipeline_z_image_controlnet_inpaint.py    | 736 ++++++++++++++++++
 .../dummy_torch_and_transformers_objects.py   |  15 +
 5 files changed, 767 insertions(+), 2 deletions(-)
 create mode 100644 src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py

diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index a66b81ab408e..7a94c75e8a1e 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -668,6 +668,7 @@
             "WuerstchenDecoderPipeline",
             "WuerstchenPriorPipeline",
             "ZImageControlNetPipeline",
+            "ZImageControlNetInpaintPipeline",
             "ZImageImg2ImgPipeline",
             "ZImagePipeline",
         ]
@@ -1372,6 +1373,7 @@
             WuerstchenCombinedPipeline,
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
+            ZImageControlNetInpaintPipeline,
             ZImageControlNetPipeline,
             ZImageImg2ImgPipeline,
             ZImagePipeline,
diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py
index 41489c183f79..6b5ddbcf28b4 100644
--- a/src/diffusers/pipelines/__init__.py
+++ b/src/diffusers/pipelines/__init__.py
@@ -404,7 +404,12 @@
         "Kandinsky5T2IPipeline",
         "Kandinsky5I2IPipeline",
     ]
-    _import_structure["z_image"] = ["ZImageImg2ImgPipeline", "ZImagePipeline", "ZImageControlNetPipeline"]
+    _import_structure["z_image"] = [
+        "ZImageImg2ImgPipeline",
+        "ZImagePipeline",
+        "ZImageControlNetPipeline",
+        "ZImageControlNetInpaintPipeline",
+    ]
     _import_structure["skyreels_v2"] = [
         "SkyReelsV2DiffusionForcingPipeline",
         "SkyReelsV2DiffusionForcingImageToVideoPipeline",
@@ -841,7 +846,12 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
         )
-        from .z_image import ZImageControlNetPipeline, ZImageImg2ImgPipeline, ZImagePipeline
+        from .z_image import (
+            ZImageControlNetInpaintPipeline,
+            ZImageControlNetPipeline,
+            ZImageImg2ImgPipeline,
+            ZImagePipeline,
+        )
 
         try:
             if not is_onnx_available():
diff --git a/src/diffusers/pipelines/z_image/__init__.py b/src/diffusers/pipelines/z_image/__init__.py
index 4bb1900edb14..7b3cfbceea2c 100644
--- a/src/diffusers/pipelines/z_image/__init__.py
+++ b/src/diffusers/pipelines/z_image/__init__.py
@@ -24,6 +24,7 @@
     _import_structure["pipeline_output"] = ["ZImagePipelineOutput"]
     _import_structure["pipeline_z_image"] = ["ZImagePipeline"]
     _import_structure["pipeline_z_image_controlnet"] = ["ZImageControlNetPipeline"]
+    _import_structure["pipeline_z_image_controlnet_inpaint"] = ["ZImageControlNetInpaintPipeline"]
     _import_structure["pipeline_z_image_img2img"] = ["ZImageImg2ImgPipeline"]
 
 
@@ -38,6 +39,7 @@
         from .pipeline_output import ZImagePipelineOutput
         from .pipeline_z_image import ZImagePipeline
         from .pipeline_z_image_controlnet import ZImageControlNetPipeline
+        from .pipeline_z_image_controlnet_inpaint import ZImageControlNetInpaintPipeline
         from .pipeline_z_image_img2img import ZImageImg2ImgPipeline
 
 else:
diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
new file mode 100644
index 000000000000..d18172083cef
--- /dev/null
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
@@ -0,0 +1,736 @@
+# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, PreTrainedModel
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin
+from ...models.autoencoders import AutoencoderKL
+from ...models.controlnets import ZImageControlNetModel
+from ...models.transformers import ZImageTransformer2DModel
+from ...pipelines.pipeline_utils import DiffusionPipeline
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from .pipeline_output import ZImagePipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import ZImageControlNetInpaintPipeline
+        >>> from diffusers import ZImageControlNetModel
+        >>> from diffusers.utils import load_image
+        >>> from huggingface_hub import hf_hub_download
+
+        >>> controlnet = ZImageControlNetModel.from_single_file(
+        ...     hf_hub_download(
+        ...         "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0",
+        ...         filename="Z-Image-Turbo-Fun-Controlnet-Union-2.0.safetensors",
+        ...     ),
+        ...     torch_dtype=torch.bfloat16,
+        ... )
+
+        >>> pipe = ZImageControlNetInpaintPipeline.from_pretrained(
+        ...     "Tongyi-MAI/Z-Image-Turbo", controlnet=controlnet, torch_dtype=torch.bfloat16
+        ... )
+        >>> pipe.to("cuda")
+
+        >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
+        >>> # (1) Use flash attention 2
+        >>> # pipe.transformer.set_attention_backend("flash")
+        >>> # (2) Use flash attention 3
+        >>> # pipe.transformer.set_attention_backend("_flash_3")
+
+        >>> image = load_image(
+        ...     "https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0/resolve/main/asset/inpaint.jpg?download=true"
+        ... )
+        >>> mask_image = load_image(
+        ...     "https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0/resolve/main/asset/mask.jpg?download=true"
+        ... )
+        >>> control_image = load_image(
+        ...     "https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0/resolve/main/asset/pose.jpg?download=true"
+        ... )
+        >>> prompt = "一位年轻女子站在阳光明媚的海岸线上，画面为全身竖构图，身体微微侧向右侧，左手自然下垂，右臂弯曲扶在腰间，她的手指清晰可见，站姿放松而略带羞涩。她身穿轻盈的白色连衣裙，裙摆在海风中轻轻飘动，布料半透、质感柔软。女子拥有一头鲜艳的及腰紫色长发，被海风吹起，在身侧轻盈飞舞，发间系着一个精致的黑色蝴蝶结，与发色形成对比。她面容清秀，眉目精致，肤色白皙细腻，表情温柔略显羞涩，微微低头，眼神静静望向远处的海平线，流露出甜美的青春气息与若有所思的神情。背景是辽阔无垠的海洋与蔚蓝天空，阳光从侧前方洒下，海面波光粼粼，泛着温暖的金色光晕，天空清澈明亮，云朵稀薄，整体色调清新唯美。"
+        >>> image = pipe(
+        ...     prompt,
+        ...     image=image,
+        ...     mask_image=mask_image,
+        ...     control_image=control_image,
+        ...     controlnet_conditioning_scale=0.75,
+        ...     height=1728,
+        ...     width=992,
+        ...     num_inference_steps=25,
+        ...     guidance_scale=0.0,
+        ...     generator=torch.Generator("cuda").manual_seed(43),
+        ... ).images[0]
+        >>> image.save("zimage-inpaint.png")
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class ZImageControlNetInpaintPipeline(DiffusionPipeline, FromSingleFileMixin):
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: PreTrainedModel,
+        tokenizer: AutoTokenizer,
+        transformer: ZImageTransformer2DModel,
+        controlnet: ZImageControlNetModel,
+    ):
+        super().__init__()
+        if self.transformer.in_channels == self.controlnet.config.control_in_dim:
+            raise ValueError(
+                "ZImageControlNetInpaintPipeline is not compatible with `alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union`, use `alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0`."
+            )
+        controlnet = ZImageControlNetModel.from_transformer(controlnet, transformer)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            scheduler=scheduler,
+            transformer=transformer,
+            controlnet=controlnet,
+        )
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 512,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt_embeds = self._encode_prompt(
+            prompt=prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            max_sequence_length=max_sequence_length,
+        )
+
+        if do_classifier_free_guidance:
+            if negative_prompt is None:
+                negative_prompt = ["" for _ in prompt]
+            else:
+                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            assert len(prompt) == len(negative_prompt)
+            negative_prompt_embeds = self._encode_prompt(
+                prompt=negative_prompt,
+                device=device,
+                prompt_embeds=negative_prompt_embeds,
+                max_sequence_length=max_sequence_length,
+            )
+        else:
+            negative_prompt_embeds = []
+        return prompt_embeds, negative_prompt_embeds
+
+    def _encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        max_sequence_length: int = 512,
+    ) -> List[torch.FloatTensor]:
+        device = device or self._execution_device
+
+        if prompt_embeds is not None:
+            return prompt_embeds
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        for i, prompt_item in enumerate(prompt):
+            messages = [
+                {"role": "user", "content": prompt_item},
+            ]
+            prompt_item = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=True,
+            )
+            prompt[i] = prompt_item
+
+        text_inputs = self.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 = self.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 prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+        return latents
+
+    # Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.prepare_image
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 5.0,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        control_image: PipelineImageInput = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 0.75,
+        cfg_normalization: bool = False,
+        cfg_truncation: float = 1.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to 1024):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 1024):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            cfg_normalization (`bool`, *optional*, defaults to False):
+                Whether to apply configuration normalization.
+            cfg_truncation (`float`, *optional*, defaults to 1.0):
+                The truncation value for configuration.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            prompt_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain
+                tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, *optional*, defaults to 512):
+                Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+        height = height or 1024
+        width = width or 1024
+
+        vae_scale = self.vae_scale_factor * 2
+        if height % vae_scale != 0:
+            raise ValueError(
+                f"Height must be divisible by {vae_scale} (got {height}). "
+                f"Please adjust the height to a multiple of {vae_scale}."
+            )
+        if width % vae_scale != 0:
+            raise ValueError(
+                f"Width must be divisible by {vae_scale} (got {width}). "
+                f"Please adjust the width to a multiple of {vae_scale}."
+            )
+
+        device = self._execution_device
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+        self._cfg_normalization = cfg_normalization
+        self._cfg_truncation = cfg_truncation
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = len(prompt_embeds)
+
+        # If prompt_embeds is provided and prompt is None, skip encoding
+        if prompt_embeds is not None and prompt is None:
+            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
+                raise ValueError(
+                    "When `prompt_embeds` is provided without `prompt`, "
+                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
+                )
+        else:
+            (
+                prompt_embeds,
+                negative_prompt_embeds,
+            ) = self.encode_prompt(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.in_channels
+
+        control_image = self.prepare_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )
+        height, width = control_image.shape[-2:]
+        control_image = retrieve_latents(self.vae.encode(control_image), generator=generator, sample_mode="argmax")
+        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        control_image = control_image.unsqueeze(2)
+
+        mask_condition = self.mask_processor.preprocess(mask_image, height=height, width=width)
+        mask_condition = torch.tile(mask_condition, [1, 3, 1, 1]).to(
+            device=control_image.device, dtype=control_image.dtype
+        )
+
+        init_image = self.prepare_image(
+            image=image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )
+        height, width = init_image.shape[-2:]
+        init_image = retrieve_latents(self.vae.encode(init_image), generator=generator, sample_mode="argmax")
+        init_image = (init_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        init_image = init_image.unsqueeze(2)
+
+        mask_condition = F.interpolate(1 - mask_condition[:, :1], size=init_image.size()[-2:], mode="nearest").to(
+            device=control_image.device, dtype=control_image.dtype
+        )
+        mask_condition = mask_condition.unsqueeze(2)
+
+        control_image = torch.cat([control_image, mask_condition, init_image], dim=1)
+
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        # Repeat prompt_embeds for num_images_per_prompt
+        if num_images_per_prompt > 1:
+            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
+            if self.do_classifier_free_guidance and negative_prompt_embeds:
+                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
+
+        actual_batch_size = batch_size * num_images_per_prompt
+        image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
+
+        # 5. Prepare timesteps
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        self.scheduler.sigma_min = 0.0
+        scheduler_kwargs = {"mu": mu}
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            **scheduler_kwargs,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0])
+                timestep = (1000 - timestep) / 1000
+                # Normalized time for time-aware config (0 at start, 1 at end)
+                t_norm = timestep[0].item()
+
+                # Handle cfg truncation
+                current_guidance_scale = self.guidance_scale
+                if (
+                    self.do_classifier_free_guidance
+                    and self._cfg_truncation is not None
+                    and float(self._cfg_truncation) <= 1
+                ):
+                    if t_norm > self._cfg_truncation:
+                        current_guidance_scale = 0.0
+
+                # Run CFG only if configured AND scale is non-zero
+                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
+
+                if apply_cfg:
+                    latents_typed = latents.to(self.transformer.dtype)
+                    latent_model_input = latents_typed.repeat(2, 1, 1, 1)
+                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
+                    timestep_model_input = timestep.repeat(2)
+                else:
+                    latent_model_input = latents.to(self.transformer.dtype)
+                    prompt_embeds_model_input = prompt_embeds
+                    timestep_model_input = timestep
+
+                latent_model_input = latent_model_input.unsqueeze(2)
+                latent_model_input_list = list(latent_model_input.unbind(dim=0))
+
+                controlnet_block_samples = self.controlnet(
+                    latent_model_input_list,
+                    timestep_model_input,
+                    prompt_embeds_model_input,
+                    control_image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                )
+
+                model_out_list = self.transformer(
+                    latent_model_input_list,
+                    timestep_model_input,
+                    prompt_embeds_model_input,
+                    controlnet_block_samples=controlnet_block_samples,
+                )[0]
+
+                if apply_cfg:
+                    # Perform CFG
+                    pos_out = model_out_list[:actual_batch_size]
+                    neg_out = model_out_list[actual_batch_size:]
+
+                    noise_pred = []
+                    for j in range(actual_batch_size):
+                        pos = pos_out[j].float()
+                        neg = neg_out[j].float()
+
+                        pred = pos + current_guidance_scale * (pos - neg)
+
+                        # Renormalization
+                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
+                            ori_pos_norm = torch.linalg.vector_norm(pos)
+                            new_pos_norm = torch.linalg.vector_norm(pred)
+                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
+                            if new_pos_norm > max_new_norm:
+                                pred = pred * (max_new_norm / new_pos_norm)
+
+                        noise_pred.append(pred)
+
+                    noise_pred = torch.stack(noise_pred, dim=0)
+                else:
+                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
+
+                noise_pred = noise_pred.squeeze(2)
+                noise_pred = -noise_pred
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
+                assert latents.dtype == torch.float32
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = latents.to(self.vae.dtype)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return ZImagePipelineOutput(images=image)
diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py
index e0388e6c143d..e8ce55e4d5c0 100644
--- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py
+++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py
@@ -3812,6 +3812,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class ZImageControlNetInpaintPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class ZImageControlNetPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

From 6126f02da270829963e2b5c8a5190d09e59bf1ab Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 12:48:35 +0000
Subject: [PATCH 32/38] t_scale

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index f7c7c4033ae4..ccb09c8620ed 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -474,6 +474,7 @@ def __init__(
 
     @classmethod
     def from_transformer(cls, controlnet, transformer):
+        controlnet.t_scale = transformer.t_scale
         controlnet.t_embedder = transformer.t_embedder
         controlnet.all_x_embedder = transformer.all_x_embedder
         controlnet.cap_embedder = transformer.cap_embedder

From c3def6b5f2c91d6d571a45c9054b5147e3b797d9 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 12:49:32 +0000
Subject: [PATCH 33/38] config.all_patch_size

---
 src/diffusers/models/controlnets/controlnet_z_image.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index ccb09c8620ed..4d984802a3d4 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -651,8 +651,8 @@ def forward(
                 "Required modules are `None`, use `from_transformer` to share required modules from `transformer`."
             )
 
-        assert patch_size in self.all_patch_size
-        assert f_patch_size in self.all_f_patch_size
+        assert patch_size in self.config.all_patch_size
+        assert f_patch_size in self.config.all_f_patch_size
 
         bsz = len(x)
         device = x[0].device

From f80ed52fcc3372a1590a0e60011d0a13a7c59524 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 13:03:00 +0000
Subject: [PATCH 34/38] not self.add_control_noise_refiner

---
 .../models/controlnets/controlnet_z_image.py    | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index 4d984802a3d4..bc77d2a60bf6 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -779,14 +779,15 @@ def forward(
             unified_attn_mask[i, :seq_len] = 1
 
         ## ControlNet start
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for layer in self.control_noise_refiner:
-                control_context = self._gradient_checkpointing_func(
-                    layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
-                )
-        else:
-            for layer in self.control_noise_refiner:
-                control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
+        if not self.add_control_noise_refiner:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                for layer in self.control_noise_refiner:
+                    control_context = self._gradient_checkpointing_func(
+                        layer, control_context, x_attn_mask, x_freqs_cis, adaln_input
+                    )
+            else:
+                for layer in self.control_noise_refiner:
+                    control_context = layer(control_context, x_attn_mask, x_freqs_cis, adaln_input)
 
         # unified
         control_context_unified = []

From 721011e52b33d953334c87ef8bf5545bd6987669 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 13:03:24 +0000
Subject: [PATCH 35/38] -self

---
 .../pipelines/z_image/pipeline_z_image_controlnet_inpaint.py    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
index d18172083cef..156b09a571c3 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
@@ -191,7 +191,7 @@ def __init__(
         controlnet: ZImageControlNetModel,
     ):
         super().__init__()
-        if self.transformer.in_channels == self.controlnet.config.control_in_dim:
+        if transformer.in_channels == controlnet.config.control_in_dim:
             raise ValueError(
                 "ZImageControlNetInpaintPipeline is not compatible with `alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union`, use `alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0`."
             )

From efadd91f8b4989ed2af71b7cf46bd2be4fff44e4 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 13:27:47 +0000
Subject: [PATCH 36/38] * (mask_condition < 0.5)

---
 .../pipelines/z_image/pipeline_z_image_controlnet_inpaint.py     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
index 156b09a571c3..eab17ae37237 100644
--- a/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
+++ b/src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py
@@ -566,6 +566,7 @@ def __call__(
             dtype=self.vae.dtype,
         )
         height, width = init_image.shape[-2:]
+        init_image = init_image * (mask_condition < 0.5)
         init_image = retrieve_latents(self.vae.encode(init_image), generator=generator, sample_mode="argmax")
         init_image = (init_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
         init_image = init_image.unsqueeze(2)

From f4b7fcc67daefd2ac9762239a9fe0900aab95c89 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 14:09:01 +0000
Subject: [PATCH 37/38] create_z_image_controlnet_config unknown type

---
 src/diffusers/loaders/single_file_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index 20ab5d827255..ed39c0d4837f 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -3923,3 +3923,5 @@ def create_z_image_controlnet_config(checkpoint, **kwargs):
         return v1_config
     elif control_x_embedder_weight_shape == 132:
         return v2_config
+    else:
+        raise ValueError("Unknown Z-Image Turbo ControlNet type.")

From dd9775caf0906c6727fcbe2797cf6dc60cc38f45 Mon Sep 17 00:00:00 2001
From: hlky <hlky@hlky.ac>
Date: Sat, 13 Dec 2025 14:32:06 +0000
Subject: [PATCH 38/38] pop control_noise_refiner from 2.0 state_dict

---
 src/diffusers/loaders/single_file_model.py       |  9 +++++++--
 src/diffusers/loaders/single_file_utils.py       | 13 +++++++++++++
 .../models/controlnets/controlnet_z_image.py     | 16 +---------------
 3 files changed, 21 insertions(+), 17 deletions(-)

diff --git a/src/diffusers/loaders/single_file_model.py b/src/diffusers/loaders/single_file_model.py
index 63b31b34529f..43abae4208ce 100644
--- a/src/diffusers/loaders/single_file_model.py
+++ b/src/diffusers/loaders/single_file_model.py
@@ -49,6 +49,7 @@
     convert_stable_cascade_unet_single_file_to_diffusers,
     convert_wan_transformer_to_diffusers,
     convert_wan_vae_to_diffusers,
+    convert_z_image_controlnet_checkpoint_to_diffusers,
     convert_z_image_transformer_checkpoint_to_diffusers,
     create_controlnet_diffusers_config_from_ldm,
     create_unet_diffusers_config_from_ldm,
@@ -174,14 +175,18 @@
         "default_subfolder": "transformer",
     },
     "ZImageControlNetModel": {
-        "checkpoint_mapping_fn": lambda x: x,
+        "checkpoint_mapping_fn": convert_z_image_controlnet_checkpoint_to_diffusers,
         "config_create_fn": create_z_image_controlnet_config,
     },
 }
 
 
 def _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint_state_dict):
-    return not set(model_state_dict.keys()).issubset(set(checkpoint_state_dict.keys()))
+    model_state_dict_keys = set(model_state_dict.keys())
+    checkpoint_state_dict_keys = set(checkpoint_state_dict.keys())
+    is_subset = model_state_dict_keys.issubset(checkpoint_state_dict_keys)
+    is_match = model_state_dict_keys == checkpoint_state_dict_keys
+    return not (is_subset and is_match)
 
 
 def _get_single_file_loadable_mapping_class(cls):
diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index ed39c0d4837f..ed81ae752560 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -3925,3 +3925,16 @@ def create_z_image_controlnet_config(checkpoint, **kwargs):
         return v2_config
     else:
         raise ValueError("Unknown Z-Image Turbo ControlNet type.")
+
+
+def convert_z_image_controlnet_checkpoint_to_diffusers(checkpoint, **kwargs):
+    control_x_embedder_weight_shape = checkpoint["control_all_x_embedder.2-1.weight"].shape[1]
+    if control_x_embedder_weight_shape == 64:
+        return checkpoint
+    elif control_x_embedder_weight_shape == 132:
+        converted_state_dict = {
+            key: checkpoint.pop(key) for key in list(checkpoint.keys()) if not key.startswith("control_noise_refiner.")
+        }
+        return converted_state_dict
+    else:
+        raise ValueError("Unknown Z-Image Turbo ControlNet type.")
diff --git a/src/diffusers/models/controlnets/controlnet_z_image.py b/src/diffusers/models/controlnets/controlnet_z_image.py
index bc77d2a60bf6..1e628e560e8e 100644
--- a/src/diffusers/models/controlnets/controlnet_z_image.py
+++ b/src/diffusers/models/controlnets/controlnet_z_image.py
@@ -432,21 +432,7 @@ def __init__(
 
         self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
         if self.add_control_noise_refiner:
-            self.control_noise_refiner = nn.ModuleList(
-                [
-                    ZImageControlTransformerBlock(
-                        1000 + layer_id,
-                        dim,
-                        n_heads,
-                        n_kv_heads,
-                        norm_eps,
-                        qk_norm,
-                        modulation=True,
-                        block_id=layer_id,
-                    )
-                    for layer_id in range(n_refiner_layers)
-                ]
-            )
+            self.control_noise_refiner = None
         else:
             self.control_noise_refiner = nn.ModuleList(
                 [