Apply suggestion from @yiyixuxu

Even if the `qweight_type` is one of the `UNQUANTIZED_TYPES`, qweight still has to be "dequantized" because it is stored as an 8-bit tensor. Without doing so, it is therefore a shape mismatch in the following matmul.

Side notes:
 - why isn't DIFFUSERS_GGUF_CUDA_KERNELS on by default? It's significantly faster and only used when installed
 - https://huggingface.co/Isotr0py/ggml/tree/main/build has no build for torch 2.8 (or the upcoming 2.9). Who can we contact to make such a build?

Co-authored-by: YiYi Xu <yixu310@gmail.com>

docs/source/en/api/pipelines/z_image.md

@@ -53,6 +53,41 @@ image = pipe(
 image.save("zimage_img2img.png")
 ```
 
+## Inpainting
+
+Use [`ZImageInpaintPipeline`] to inpaint specific regions of an image based on a text prompt and mask.
+
+```python
+import torch
+import numpy as np
+from PIL import Image
+from diffusers import ZImageInpaintPipeline
+from diffusers.utils import load_image
+
+pipe = ZImageInpaintPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+init_image = load_image(url).resize((1024, 1024))
+
+# Create a mask (white = inpaint, black = preserve)
+mask = np.zeros((1024, 1024), dtype=np.uint8)
+mask[256:768, 256:768] = 255  # Inpaint center region
+mask_image = Image.fromarray(mask)
+
+prompt = "A beautiful lake with mountains in the background"
+image = pipe(
+    prompt,
+    image=init_image,
+    mask_image=mask_image,
+    strength=1.0,
+    num_inference_steps=9,
+    guidance_scale=0.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).images[0]
+image.save("zimage_inpaint.png")
+```
+
 ## ZImagePipeline
 
 [[autodoc]] ZImagePipeline
@@ -64,3 +99,9 @@ image.save("zimage_img2img.png")
 [[autodoc]] ZImageImg2ImgPipeline
 	- all
 	- __call__
+
+## ZImageInpaintPipeline
+
+[[autodoc]] ZImageInpaintPipeline
+	- all
+	- __call__

docs/source/en/modular_diffusers/components_manager.md

@@ -12,179 +12,85 @@ specific language governing permissions and limitations under the License.
 
 # ComponentsManager
 
-The [`ComponentsManager`] is a model registry and management system for Modular Diffusers. It adds and tracks models, stores useful metadata (model size, device placement, adapters), prevents duplicate model instances, and supports offloading.
+The [`ComponentsManager`] is a model registry and management system for Modular Diffusers. It adds and tracks models, stores useful metadata (model size, device placement, adapters), and supports offloading.
 
 This guide will show you how to use [`ComponentsManager`] to manage components and device memory.
 
-## Add a component
+## Connect to a pipeline
 
-The [`ComponentsManager`] should be created alongside a [`ModularPipeline`] in either [`~ModularPipeline.from_pretrained`] or [`~ModularPipelineBlocks.init_pipeline`].
+Create a [`ComponentsManager`] and pass it to a [`ModularPipeline`] with either [`~ModularPipeline.from_pretrained`] or [`~ModularPipelineBlocks.init_pipeline`]. 
 
-> [!TIP]
-> The `collection` parameter is optional but makes it easier to organize and manage components.
 
 <hfoptions id="create">
 <hfoption id="from_pretrained">
 
 ```py
 from diffusers import ModularPipeline, ComponentsManager
+import torch
 
-comp = ComponentsManager()
-pipe = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test1")
+manager = ComponentsManager()
+pipe = ModularPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", components_manager=manager)
+pipe.load_components(torch_dtype=torch.bfloat16)
 ```
 
 </hfoption>
 <hfoption id="init_pipeline">
 
 ```py
-from diffusers import ComponentsManager
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS
-
-t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)
-
-modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
-components = ComponentsManager()
-t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components)
+from diffusers import ModularPipelineBlocks, ComponentsManager
+import torch
+manager = ComponentsManager()
+blocks = ModularPipelineBlocks.from_pretrained("diffusers/Florence2-image-Annotator", trust_remote_code=True)
+pipe= blocks.init_pipeline(components_manager=manager)
+pipe.load_components(torch_dtype=torch.bfloat16)
 ```
 
 </hfoption>
 </hfoptions>
 
-Components are only loaded and registered when using [`~ModularPipeline.load_components`] or [`~ModularPipeline.load_components`]. The example below uses [`~ModularPipeline.load_components`] to create a second pipeline that reuses all the components from the first one, and assigns it to a different collection
+Components loaded by the pipeline are automatically registered in the manager. You can inspect them right away.
+
+## Inspect components
+
+Print the [`ComponentsManager`] to see all registered components, including their class, device placement, dtype, memory size, and load ID.
+
+The output below corresponds to the `from_pretrained` example above
 
 ```py
-pipe.load_components()
-pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2")
+Components:
+=============================================================================================================================
+Models:
+-----------------------------------------------------------------------------------------------------------------------------
+Name_ID                      | Class                    | Device: act(exec) | Dtype          | Size (GB) | Load ID
+-----------------------------------------------------------------------------------------------------------------------------
+text_encoder_140458257514752 | Qwen3Model               | cpu               | torch.bfloat16 | 7.49      | Tongyi-MAI/Z-Image-Turbo|text_encoder|null|null
+vae_140458257515376          | AutoencoderKL            | cpu               | torch.bfloat16 | 0.16      | Tongyi-MAI/Z-Image-Turbo|vae|null|null
+transformer_140458257515616  | ZImageTransformer2DModel | cpu               | torch.bfloat16 | 11.46     | Tongyi-MAI/Z-Image-Turbo|transformer|null|null
+-----------------------------------------------------------------------------------------------------------------------------
+
+Other Components:
+-----------------------------------------------------------------------------------------------------------------------------
+ID                           | Class                           | Collection
+-----------------------------------------------------------------------------------------------------------------------------
+scheduler_140461023555264    | FlowMatchEulerDiscreteScheduler | N/A
+tokenizer_140458256346432    | Qwen2Tokenizer                  | N/A
+-----------------------------------------------------------------------------------------------------------------------------
 ```
 
-Use the [`~ModularPipeline.null_component_names`] property to identify any components that need to be loaded, retrieve them with [`~ComponentsManager.get_components_by_names`], and then call [`~ModularPipeline.update_components`] to add the missing components.
-
-```py
-pipe2.null_component_names 
-['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'image_encoder', 'unet', 'vae', 'scheduler', 'controlnet']
-
-comp_dict = comp.get_components_by_names(names=pipe2.null_component_names)
-pipe2.update_components(**comp_dict)
-```
-
-To add individual components, use the [`~ComponentsManager.add`] method. This registers a component with a unique id.
-
-```py
-from diffusers import AutoModel
-
-text_encoder = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
-component_id = comp.add("text_encoder", text_encoder)
-comp
-```
-
-Use [`~ComponentsManager.remove`] to remove a component using their id.
-
-```py
-comp.remove("text_encoder_139917733042864")
-```
-
-## Retrieve a component
-
-The [`ComponentsManager`] provides several methods to retrieve registered components.
-
-### get_one
-
-The [`~ComponentsManager.get_one`] method returns a single component and supports pattern matching for the `name` parameter. If multiple components match, [`~ComponentsManager.get_one`] returns an error.
-
-| Pattern     | Example                          | Description                               |
-|-------------|----------------------------------|-------------------------------------------|
-| exact       | `comp.get_one(name="unet")`      | exact name match                          |
-| wildcard    | `comp.get_one(name="unet*")`     | names starting with "unet"                |
-| exclusion   | `comp.get_one(name="!unet")`     | exclude components named "unet"           |
-| or          | `comp.get_one(name="unet&#124;vae")`  | name is "unet" or "vae"                   |
-
-[`~ComponentsManager.get_one`] also filters components by the `collection` argument or `load_id` argument.
-
-```py
-comp.get_one(name="unet", collection="sdxl")
-```
-
-### get_components_by_names
-
-The [`~ComponentsManager.get_components_by_names`] method accepts a list of names and returns a dictionary mapping names to components. This is especially useful with [`ModularPipeline`] since they provide lists of required component names and the returned dictionary can be passed directly to [`~ModularPipeline.update_components`].
-
-```py
-component_dict = comp.get_components_by_names(names=["text_encoder", "unet", "vae"])
-{"text_encoder": component1, "unet": component2, "vae": component3}
-```
-
-## Duplicate detection
-
-It is recommended to load model components with [`ComponentSpec`] to assign components with a unique id that encodes their loading parameters. This allows [`ComponentsManager`] to automatically detect and prevent duplicate model instances even when different objects represent the same underlying checkpoint.
-
-```py
-from diffusers import ComponentSpec, ComponentsManager
-from transformers import CLIPTextModel
-
-comp = ComponentsManager()
-
-# Create ComponentSpec for the first text encoder
-spec = ComponentSpec(name="text_encoder", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=AutoModel)
-# Create ComponentSpec for a duplicate text encoder (it is same checkpoint, from the same repo/subfolder)
-spec_duplicated = ComponentSpec(name="text_encoder_duplicated", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=CLIPTextModel)
-
-# Load and add both components - the manager will detect they're the same model
-comp.add("text_encoder", spec.load())
-comp.add("text_encoder_duplicated", spec_duplicated.load())
-```
-
-This returns a warning with instructions for removing the duplicate.
-
-```py
-ComponentsManager: adding component 'text_encoder_duplicated_139917580682672', but it has duplicate load_id 'stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null' with existing components: text_encoder_139918506246832. To remove a duplicate, call `components_manager.remove('<component_id>')`.
-'text_encoder_duplicated_139917580682672'
-```
-
-You could also add a component without using [`ComponentSpec`] and duplicate detection still works in most cases even if you're adding the same component under a different name.
-
-However, [`ComponentManager`] can't detect duplicates when you load the same component into different objects. In this case, you should load a model with [`ComponentSpec`].
-
-```py
-text_encoder_2 = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
-comp.add("text_encoder", text_encoder_2)
-'text_encoder_139917732983664'
-```
-
-## Collections
-
-Collections are labels assigned to components for better organization and management. Add a component to a collection with the `collection` argument in [`~ComponentsManager.add`].
-
-Only one component per name is allowed in each collection. Adding a second component with the same name automatically removes the first component.
-
-```py
-from diffusers import ComponentSpec, ComponentsManager
-
-comp = ComponentsManager()
-# Create ComponentSpec for the first UNet
-spec = ComponentSpec(name="unet", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", type_hint=AutoModel)
-# Create ComponentSpec for a different UNet
-spec2 = ComponentSpec(name="unet", repo="RunDiffusion/Juggernaut-XL-v9", subfolder="unet", type_hint=AutoModel, variant="fp16")
-
-# Add both UNets to the same collection - the second one will replace the first
-comp.add("unet", spec.load(), collection="sdxl")
-comp.add("unet", spec2.load(), collection="sdxl")
-```
-
-This makes it convenient to work with node-based systems because you can:
-
-- Mark all models as loaded from one node with the `collection` label.
-- Automatically replace models when new checkpoints are loaded under the same name.
-- Batch delete all models in a collection when a node is removed.
+The table shows models (with device, dtype, and memory info) separately from other components like schedulers and tokenizers. If any models have LoRA adapters, IP-Adapters, or quantization applied, that information is displayed in an additional section at the bottom.
 
 ## Offloading
 
 The [`~ComponentsManager.enable_auto_cpu_offload`] method is a global offloading strategy that works across all models regardless of which pipeline is using them. Once enabled, you don't need to worry about device placement if you add or remove components.
 
 ```py
-comp.enable_auto_cpu_offload(device="cuda")
+manager.enable_auto_cpu_offload(device="cuda")
 ```
 
 All models begin on the CPU and [`ComponentsManager`] moves them to the appropriate device right before they're needed, and moves other models back to the CPU when GPU memory is low.
 
-You can set your own rules for which models to offload first.
+To disable offloading, call [~ComponentsManager.disable_auto_cpu_offload].
+
+```py
+manager.disable_auto_cpu_offload()
+```

src/diffusers/__init__.py

@@ -696,6 +696,7 @@ else:
             "ZImageControlNetInpaintPipeline",
             "ZImageControlNetPipeline",
             "ZImageImg2ImgPipeline",
+            "ZImageInpaintPipeline",
             "ZImageOmniPipeline",
             "ZImagePipeline",
         ]
@@ -1428,6 +1429,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ZImageControlNetInpaintPipeline,
             ZImageControlNetPipeline,
             ZImageImg2ImgPipeline,
+            ZImageInpaintPipeline,
             ZImageOmniPipeline,
             ZImagePipeline,
         )

src/diffusers/pipelines/__init__.py

@@ -410,11 +410,12 @@ else:
         "Kandinsky5I2IPipeline",
     ]
     _import_structure["z_image"] = [
-        "ZImageImg2ImgPipeline",
-        "ZImagePipeline",
-        "ZImageControlNetPipeline",
         "ZImageControlNetInpaintPipeline",
+        "ZImageControlNetPipeline",
+        "ZImageImg2ImgPipeline",
+        "ZImageInpaintPipeline",
         "ZImageOmniPipeline",
+        "ZImagePipeline",
     ]
     _import_structure["skyreels_v2"] = [
         "SkyReelsV2DiffusionForcingPipeline",
@@ -870,6 +871,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ZImageControlNetInpaintPipeline,
             ZImageControlNetPipeline,
             ZImageImg2ImgPipeline,
+            ZImageInpaintPipeline,
             ZImageOmniPipeline,
             ZImagePipeline,
         )

src/diffusers/pipelines/auto_pipeline.py

@@ -127,6 +127,7 @@ from .z_image import (
     ZImageControlNetInpaintPipeline,
     ZImageControlNetPipeline,
     ZImageImg2ImgPipeline,
+    ZImageInpaintPipeline,
     ZImageOmniPipeline,
     ZImagePipeline,
 )
@@ -235,6 +236,7 @@ AUTO_INPAINT_PIPELINES_MAPPING = OrderedDict(
         ("stable-diffusion-pag", StableDiffusionPAGInpaintPipeline),
         ("qwenimage", QwenImageInpaintPipeline),
         ("qwenimage-edit", QwenImageEditInpaintPipeline),
+        ("z-image", ZImageInpaintPipeline),
     ]
 )
 

src/diffusers/pipelines/z_image/__init__.py

@@ -26,6 +26,7 @@ else:
     _import_structure["pipeline_z_image_controlnet"] = ["ZImageControlNetPipeline"]
     _import_structure["pipeline_z_image_controlnet_inpaint"] = ["ZImageControlNetInpaintPipeline"]
     _import_structure["pipeline_z_image_img2img"] = ["ZImageImg2ImgPipeline"]
+    _import_structure["pipeline_z_image_inpaint"] = ["ZImageInpaintPipeline"]
     _import_structure["pipeline_z_image_omni"] = ["ZImageOmniPipeline"]
 
 
@@ -42,6 +43,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_z_image_controlnet import ZImageControlNetPipeline
         from .pipeline_z_image_controlnet_inpaint import ZImageControlNetInpaintPipeline
         from .pipeline_z_image_img2img import ZImageImg2ImgPipeline
+        from .pipeline_z_image_inpaint import ZImageInpaintPipeline
         from .pipeline_z_image_omni import ZImageOmniPipeline
 else:
     import sys

src/diffusers/pipelines/z_image/pipeline_z_image_inpaint.py

@@ -0,0 +1,932 @@
+# 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, ZImageLoraLoaderMixin
+from ...models.autoencoders import AutoencoderKL
+from ...models.transformers import ZImageTransformer2DModel
+from ...pipelines.pipeline_utils import DiffusionPipeline
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from .pipeline_output import ZImagePipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import ZImageInpaintPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = ZImageInpaintPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+        >>> init_image = load_image(url).resize((1024, 1024))
+
+        >>> # Create a mask (white = inpaint, black = preserve)
+        >>> import numpy as np
+        >>> from PIL import Image
+
+        >>> mask = np.zeros((1024, 1024), dtype=np.uint8)
+        >>> mask[256:768, 256:768] = 255  # Inpaint center region
+        >>> mask_image = Image.fromarray(mask)
+
+        >>> prompt = "A beautiful lake with mountains in the background"
+        >>> image = pipe(
+        ...     prompt,
+        ...     image=init_image,
+        ...     mask_image=mask_image,
+        ...     strength=1.0,
+        ...     num_inference_steps=9,
+        ...     guidance_scale=0.0,
+        ...     generator=torch.Generator("cuda").manual_seed(42),
+        ... ).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 ZImageInpaintPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
+    r"""
+    The ZImage pipeline for inpainting.
+
+    Args:
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`PreTrainedModel`]):
+            A text encoder model to encode text prompts.
+        tokenizer ([`AutoTokenizer`]):
+            A tokenizer to tokenize text prompts.
+        transformer ([`ZImageTransformer2DModel`]):
+            A ZImage transformer model to denoise the encoded image latents.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "mask", "masked_image_latents"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: PreTrainedModel,
+        tokenizer: AutoTokenizer,
+        transformer: ZImageTransformer2DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            scheduler=scheduler,
+            transformer=transformer,
+        )
+        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 * 2,
+            do_normalize=False,
+            do_binarize=True,
+            do_convert_grayscale=True,
+        )
+
+    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline.encode_prompt
+    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
+
+    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline._encode_prompt
+    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
+
+    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(num_inference_steps * strength, num_inference_steps)
+
+        t_start = int(max(num_inference_steps - init_timestep, 0))
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+    ):
+        """Prepare mask and masked image latents for inpainting.
+
+        Args:
+            mask: Binary mask tensor where 1 = inpaint region, 0 = preserve region.
+            masked_image: Original image with masked regions zeroed out.
+            batch_size: Number of images to generate.
+            height: Output image height.
+            width: Output image width.
+            dtype: Data type for the tensors.
+            device: Device to place tensors on.
+            generator: Random generator for reproducibility.
+
+        Returns:
+            Tuple of (mask, masked_image_latents) prepared for the denoising loop.
+        """
+        # Calculate latent dimensions
+        latent_height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        latent_width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        # Resize mask to latent dimensions
+        mask = torch.nn.functional.interpolate(mask, size=(latent_height, latent_width), mode="nearest")
+        mask = mask.to(device=device, dtype=dtype)
+
+        # Encode masked image to latents
+        masked_image = masked_image.to(device=device, dtype=dtype)
+        if isinstance(generator, list):
+            masked_image_latents = [
+                retrieve_latents(self.vae.encode(masked_image[i : i + 1]), generator=generator[i])
+                for i in range(masked_image.shape[0])
+            ]
+            masked_image_latents = torch.cat(masked_image_latents, dim=0)
+        else:
+            masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)
+
+        # Apply VAE scaling
+        masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+        # Expand for batch size
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
+
+        return mask, masked_image_latents
+
+    def prepare_latents(
+        self,
+        image,
+        timestep,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        """Prepare latents for inpainting, returning noise and image_latents for blending.
+
+        Returns:
+            Tuple of (latents, noise, image_latents) where:
+            - latents: Noised image latents for denoising
+            - noise: The noise tensor used for blending
+            - image_latents: Clean image latents for blending
+        """
+        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 not None:
+            # Generate noise for blending even if latents are provided
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # Encode image for blending
+            image = image.to(device=device, dtype=dtype)
+            if isinstance(generator, list):
+                image_latents = [
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(image.shape[0])
+                ]
+                image_latents = torch.cat(image_latents, dim=0)
+            else:
+                image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+            if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+                image_latents = torch.cat([image_latents] * (batch_size // image_latents.shape[0]), dim=0)
+            return latents.to(device=device, dtype=dtype), noise, image_latents
+
+        # Encode the input image
+        image = image.to(device=device, dtype=dtype)
+        if image.shape[1] != num_channels_latents:
+            if isinstance(generator, list):
+                image_latents = [
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(image.shape[0])
+                ]
+                image_latents = torch.cat(image_latents, dim=0)
+            else:
+                image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+            # Apply scaling (inverse of decoding: decode does latents/scaling_factor + shift_factor)
+            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        else:
+            image_latents = image
+
+        # Handle batch size expansion
+        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+            additional_image_per_prompt = batch_size // image_latents.shape[0]
+            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+            )
+
+        # Generate noise for both initial noising and later blending
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # Add noise using flow matching scale_noise
+        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+
+        return latents, noise, image_latents
+
+    @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
+
+    def check_inputs(
+        self,
+        prompt,
+        image,
+        mask_image,
+        strength,
+        height,
+        width,
+        output_type,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should be in [0.0, 1.0] but is {strength}")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if image is None:
+            raise ValueError("`image` input cannot be undefined for inpainting.")
+
+        if mask_image is None:
+            raise ValueError("`mask_image` input cannot be undefined for inpainting.")
+
+        if output_type not in ["latent", "pil", "np", "pt"]:
+            raise ValueError(f"`output_type` must be one of 'latent', 'pil', 'np', or 'pt', but got {output_type}")
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        masked_image_latents: Optional[torch.FloatTensor] = None,
+        strength: float = 1.0,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 5.0,
+        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 inpainting.
+
+        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.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a
+                list of tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or
+                a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
+            mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing a mask image for inpainting. White pixels (value 1) in the
+                mask will be inpainted, black pixels (value 0) will be preserved from the original image.
+            masked_image_latents (`torch.FloatTensor`, *optional*):
+                Pre-encoded masked image latents. If provided, the masked image encoding step will be skipped.
+            strength (`float`, *optional*, defaults to 1.0):
+                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+                essentially ignores `image` in the masked region.
+            height (`int`, *optional*, defaults to 1024):
+                The height in pixels of the generated image. If not provided, uses the input image height.
+            width (`int`, *optional*, defaults to 1024):
+                The width in pixels of the generated image. If not provided, uses the input image width.
+            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.
+        """
+        # 1. Check inputs
+        self.check_inputs(
+            prompt=prompt,
+            image=image,
+            mask_image=mask_image,
+            strength=strength,
+            height=height,
+            width=width,
+            output_type=output_type,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+        )
+
+        # 2. Preprocess image and mask
+        init_image = self.image_processor.preprocess(image)
+        init_image = init_image.to(dtype=torch.float32)
+
+        # Get dimensions from the preprocessed image if not specified
+        if height is None:
+            height = init_image.shape[-2]
+        if width is None:
+            width = init_image.shape[-1]
+
+        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}."
+            )
+
+        # Preprocess mask
+        mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
+
+        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
+
+        # 3. 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
+
+        # 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
+
+        # Calculate latent dimensions for image_seq_len
+        latent_height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        latent_width = 2 * (int(width) // (self.vae_scale_factor * 2))
+        image_seq_len = (latent_height // 2) * (latent_width // 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,
+        )
+
+        # 6. Adjust timesteps based on strength
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        if num_inference_steps < 1:
+            raise ValueError(
+                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline "
+                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+            )
+        latent_timestep = timesteps[:1].repeat(actual_batch_size)
+
+        # 7. Prepare latents from image (returns noise and image_latents for blending)
+        latents, noise, image_latents = self.prepare_latents(
+            init_image,
+            latent_timestep,
+            actual_batch_size,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds[0].dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 8. Prepare mask and masked image latents
+        # Create masked image: preserve only unmasked regions (mask=0)
+        if masked_image_latents is None:
+            masked_image = init_image * (mask < 0.5)
+        else:
+            masked_image = None  # Will use provided masked_image_latents
+
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask,
+            masked_image if masked_image is not None else init_image,
+            actual_batch_size,
+            height,
+            width,
+            prompt_embeds[0].dtype,
+            device,
+            generator,
+        )
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 9. 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))
+
+                model_out_list = self.transformer(
+                    latent_model_input_list,
+                    timestep_model_input,
+                    prompt_embeds_model_input,
+                )[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
+
+                # Inpainting blend: combine denoised latents with original image latents
+                init_latents_proper = image_latents
+
+                # Re-scale original latents to current noise level for proper blending
+                if i < len(timesteps) - 1:
+                    noise_timestep = timesteps[i + 1]
+                    init_latents_proper = self.scheduler.scale_noise(
+                        init_latents_proper, torch.tensor([noise_timestep]), noise
+                    )
+
+                # Blend: mask=1 for inpaint region (use denoised), mask=0 for preserve region (use original)
+                latents = (1 - mask) * init_latents_proper + mask * latents
+
+                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)
+                    mask = callback_outputs.pop("mask", mask)
+                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)
+
+                # 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 XLA_AVAILABLE:
+                    xm.mark_step()
+
+        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)

src/diffusers/quantizers/gguf/utils.py

@@ -79,7 +79,8 @@ MMQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES
 def _fused_mul_mat_gguf(x: torch.Tensor, qweight: torch.Tensor, qweight_type: int) -> torch.Tensor:
     # there is no need to call any kernel for fp16/bf16
     if qweight_type in UNQUANTIZED_TYPES:
-        return x @ qweight.T
+        weight = dequantize_gguf_tensor(qweight)
+        return x @ weight.T
 
     # TODO(Isotr0py): GGUF's MMQ and MMVQ implementation are designed for
     # contiguous batching and inefficient with diffusers' batching,

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -4112,6 +4112,21 @@ class ZImageImg2ImgPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class ZImageInpaintPipeline(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 ZImageOmniPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/z_image/test_z_image_inpaint.py

@@ -0,0 +1,396 @@
+# 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 gc
+import os
+import unittest
+
+import numpy as np
+import torch
+from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model
+
+from diffusers import (
+    AutoencoderKL,
+    FlowMatchEulerDiscreteScheduler,
+    ZImageInpaintPipeline,
+    ZImageTransformer2DModel,
+)
+from diffusers.utils.testing_utils import floats_tensor
+
+from ...testing_utils import torch_device
+from ..pipeline_params import (
+    IMAGE_TO_IMAGE_IMAGE_PARAMS,
+    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
+    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
+)
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations
+# Cannot use enable_full_determinism() which sets it to True
+# Note: Z-Image does not support FP16 inference due to complex64 RoPE embeddings
+os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
+torch.use_deterministic_algorithms(False)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+if hasattr(torch.backends, "cuda"):
+    torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class ZImageInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = ZImageInpaintPipeline
+    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset(["image", "mask_image"])
+    image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "strength",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    supports_dduf = False
+    test_xformers_attention = False
+    test_layerwise_casting = True
+    test_group_offloading = True
+
+    def setUp(self):
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = ZImageTransformer2DModel(
+            all_patch_size=(2,),
+            all_f_patch_size=(1,),
+            in_channels=16,
+            dim=32,
+            n_layers=2,
+            n_refiner_layers=1,
+            n_heads=2,
+            n_kv_heads=2,
+            norm_eps=1e-5,
+            qk_norm=True,
+            cap_feat_dim=16,
+            rope_theta=256.0,
+            t_scale=1000.0,
+            axes_dims=[8, 4, 4],
+            axes_lens=[256, 32, 32],
+        )
+        # `x_pad_token` and `cap_pad_token` are initialized with `torch.empty` which contains
+        # uninitialized memory. Set them to known values for deterministic test behavior.
+        with torch.no_grad():
+            transformer.x_pad_token.copy_(torch.ones_like(transformer.x_pad_token.data))
+            transformer.cap_pad_token.copy_(torch.ones_like(transformer.cap_pad_token.data))
+
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            block_out_channels=[32, 64],
+            layers_per_block=1,
+            latent_channels=16,
+            norm_num_groups=32,
+            sample_size=32,
+            scaling_factor=0.3611,
+            shift_factor=0.1159,
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        torch.manual_seed(0)
+        config = Qwen3Config(
+            hidden_size=16,
+            intermediate_size=16,
+            num_hidden_layers=2,
+            num_attention_heads=2,
+            num_key_value_heads=2,
+            vocab_size=151936,
+            max_position_embeddings=512,
+        )
+        text_encoder = Qwen3Model(config)
+        tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        import random
+
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        # Create mask: 1 = inpaint region, 0 = preserve region
+        mask_image = torch.zeros((1, 1, 32, 32), device=device)
+        mask_image[:, :, 8:24, 8:24] = 1.0  # Inpaint center region
+
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "image": image,
+            "mask_image": mask_image,
+            "strength": 1.0,
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 3.0,
+            "cfg_normalization": False,
+            "cfg_truncation": 1.0,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": 16,
+            "output_type": "np",
+        }
+
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = pipe(**inputs).images
+        generated_image = image[0]
+        self.assertEqual(generated_image.shape, (32, 32, 3))
+
+    def test_inference_batch_single_identical(self):
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1)
+
+    def test_num_images_per_prompt(self):
+        import inspect
+
+        sig = inspect.signature(self.pipeline_class.__call__)
+
+        if "num_images_per_prompt" not in sig.parameters:
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        batch_sizes = [1, 2]
+        num_images_per_prompts = [1, 2]
+
+        for batch_size in batch_sizes:
+            for num_images_per_prompt in num_images_per_prompts:
+                inputs = self.get_dummy_inputs(torch_device)
+
+                for key in inputs.keys():
+                    if key in self.batch_params:
+                        inputs[key] = batch_size * [inputs[key]]
+
+                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
+
+                assert images.shape[0] == batch_size * num_images_per_prompt
+
+        del pipe
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
+
+    def test_attention_slicing_forward_pass(
+        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
+    ):
+        if not self.test_attention_slicing:
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for component in pipe.components.values():
+            if hasattr(component, "set_default_attn_processor"):
+                component.set_default_attn_processor()
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
+        output_without_slicing = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=1)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing1 = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=2)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing2 = pipe(**inputs)[0]
+
+        if test_max_difference:
+            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
+            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
+            self.assertLess(
+                max(max_diff1, max_diff2),
+                expected_max_diff,
+                "Attention slicing should not affect the inference results",
+            )
+
+    def test_vae_tiling(self, expected_diff_max: float = 0.7):
+        import random
+
+        generator_device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = self.pipeline_class(**components)
+        pipe.to("cpu")
+        pipe.set_progress_bar_config(disable=None)
+
+        # Without tiling
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        # Generate a larger image for the input
+        inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu")
+        # Generate a larger mask for the input
+        mask = torch.zeros((1, 1, 128, 128), device="cpu")
+        mask[:, :, 32:96, 32:96] = 1.0
+        inputs["mask_image"] = mask
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling (standard AutoencoderKL doesn't accept parameters)
+        pipe.vae.enable_tiling()
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu")
+        inputs["mask_image"] = mask
+        output_with_tiling = pipe(**inputs)[0]
+
+        self.assertLess(
+            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
+            expected_diff_max,
+            "VAE tiling should not affect the inference results",
+        )
+
+    def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-3):
+        # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance
+        # Inpainting mask blending adds additional numerical variance
+        super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference)
+
+    def test_group_offloading_inference(self):
+        # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine.
+        self.skipTest("Using test_pipeline_level_group_offloading_inference instead")
+
+    def test_save_load_float16(self, expected_max_diff=1e-2):
+        # Z-Image does not support FP16 due to complex64 RoPE embeddings
+        self.skipTest("Z-Image does not support FP16 inference")
+
+    def test_float16_inference(self, expected_max_diff=5e-2):
+        # Z-Image does not support FP16 due to complex64 RoPE embeddings
+        self.skipTest("Z-Image does not support FP16 inference")
+
+    def test_strength_parameter(self):
+        """Test that strength parameter affects the output correctly."""
+        device = "cpu"
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        # Test with different strength values
+        inputs_low_strength = self.get_dummy_inputs(device)
+        inputs_low_strength["strength"] = 0.2
+
+        inputs_high_strength = self.get_dummy_inputs(device)
+        inputs_high_strength["strength"] = 0.8
+
+        # Both should complete without errors
+        output_low = pipe(**inputs_low_strength).images[0]
+        output_high = pipe(**inputs_high_strength).images[0]
+
+        # Outputs should be different (different amount of transformation)
+        self.assertFalse(np.allclose(output_low, output_high, atol=1e-3))
+
+    def test_invalid_strength(self):
+        """Test that invalid strength values raise appropriate errors."""
+        device = "cpu"
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+
+        inputs = self.get_dummy_inputs(device)
+
+        # Test strength < 0
+        inputs["strength"] = -0.1
+        with self.assertRaises(ValueError):
+            pipe(**inputs)
+
+        # Test strength > 1
+        inputs["strength"] = 1.5
+        with self.assertRaises(ValueError):
+            pipe(**inputs)
+
+    def test_mask_inpainting(self):
+        """Test that the mask properly controls which regions are inpainted."""
+        device = "cpu"
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        # Generate with full mask (inpaint everything)
+        inputs_full = self.get_dummy_inputs(device)
+        inputs_full["mask_image"] = torch.ones((1, 1, 32, 32), device=device)
+
+        # Generate with no mask (preserve everything)
+        inputs_none = self.get_dummy_inputs(device)
+        inputs_none["mask_image"] = torch.zeros((1, 1, 32, 32), device=device)
+
+        # Both should complete without errors
+        output_full = pipe(**inputs_full).images[0]
+        output_none = pipe(**inputs_none).images[0]
+
+        # Outputs should be different (full inpaint vs preserve)
+        self.assertFalse(np.allclose(output_full, output_none, atol=1e-3))