Merge branch 'main' into local-model-info

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

@@ -90,3 +90,15 @@ image.save("qwen_fewsteps.png")
 ## QwenImagePipelineOutput
 
 [[autodoc]] pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput
+
+## QwenImageImg2ImgPipeline
+
+[[autodoc]] QwenImageImg2ImgPipeline
+  - all
+  - __call__
+
+## QwenImageInpaintPipeline
+
+[[autodoc]] QwenImageInpaintPipeline
+  - all
+  - __call__

src/diffusers/__init__.py

@@ -489,6 +489,8 @@ else:
             "PixArtAlphaPipeline",
             "PixArtSigmaPAGPipeline",
             "PixArtSigmaPipeline",
+            "QwenImageImg2ImgPipeline",
+            "QwenImageInpaintPipeline",
             "QwenImagePipeline",
             "ReduxImageEncoder",
             "SanaControlNetPipeline",
@@ -1121,6 +1123,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
+            QwenImageImg2ImgPipeline,
+            QwenImageInpaintPipeline,
             QwenImagePipeline,
             ReduxImageEncoder,
             SanaControlNetPipeline,

src/diffusers/loaders/single_file_model.py

@@ -62,7 +62,7 @@ logger = logging.get_logger(__name__)
 if is_accelerate_available():
     from accelerate import dispatch_model, init_empty_weights
 
-    from ..models.modeling_utils import load_model_dict_into_meta
+    from ..models.model_loading_utils import load_model_dict_into_meta
 
 if is_torch_version(">=", "1.9.0") and is_accelerate_available():
     _LOW_CPU_MEM_USAGE_DEFAULT = True

src/diffusers/loaders/single_file_utils.py

@@ -55,7 +55,7 @@ if is_transformers_available():
 if is_accelerate_available():
     from accelerate import init_empty_weights
 
-    from ..models.modeling_utils import load_model_dict_into_meta
+    from ..models.model_loading_utils import load_model_dict_into_meta
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 

src/diffusers/loaders/transformer_flux.py

@@ -17,7 +17,8 @@ from ..models.embeddings import (
     ImageProjection,
     MultiIPAdapterImageProjection,
 )
-from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
+from ..models.model_loading_utils import load_model_dict_into_meta
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
 from ..utils import is_accelerate_available, is_torch_version, logging
 from ..utils.torch_utils import empty_device_cache
 

src/diffusers/loaders/transformer_sd3.py

@@ -16,7 +16,8 @@ from typing import Dict
 
 from ..models.attention_processor import SD3IPAdapterJointAttnProcessor2_0
 from ..models.embeddings import IPAdapterTimeImageProjection
-from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
+from ..models.model_loading_utils import load_model_dict_into_meta
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
 from ..utils import is_accelerate_available, is_torch_version, logging
 from ..utils.torch_utils import empty_device_cache
 

src/diffusers/loaders/unet.py

@@ -30,7 +30,8 @@ from ..models.embeddings import (
     IPAdapterPlusImageProjection,
     MultiIPAdapterImageProjection,
 )
-from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta, load_state_dict
+from ..models.model_loading_utils import load_model_dict_into_meta
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict
 from ..utils import (
     USE_PEFT_BACKEND,
     _get_model_file,

src/diffusers/models/model_loading_utils.py

@@ -14,12 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import functools
 import importlib
 import inspect
 import math
 import os
 from array import array
 from collections import OrderedDict, defaultdict
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 from zipfile import is_zipfile
@@ -31,6 +33,7 @@ from huggingface_hub.utils import EntryNotFoundError
 
 from ..quantizers import DiffusersQuantizer
 from ..utils import (
+    DEFAULT_HF_PARALLEL_LOADING_WORKERS,
     GGUF_FILE_EXTENSION,
     SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_FILE_EXTENSION,
@@ -310,6 +313,161 @@ def load_model_dict_into_meta(
     return offload_index, state_dict_index
 
 
+def check_support_param_buffer_assignment(model_to_load, state_dict, start_prefix=""):
+    """
+    Checks if `model_to_load` supports param buffer assignment (such as when loading in empty weights) by first
+    checking if the model explicitly disables it, then by ensuring that the state dict keys are a subset of the model's
+    parameters.
+
+    """
+    if model_to_load.device.type == "meta":
+        return False
+
+    if len([key for key in state_dict if key.startswith(start_prefix)]) == 0:
+        return False
+
+    # Some models explicitly do not support param buffer assignment
+    if not getattr(model_to_load, "_supports_param_buffer_assignment", True):
+        logger.debug(
+            f"{model_to_load.__class__.__name__} does not support param buffer assignment, loading will be slower"
+        )
+        return False
+
+    # If the model does, the incoming `state_dict` and the `model_to_load` must be the same dtype
+    first_key = next(iter(model_to_load.state_dict().keys()))
+    if start_prefix + first_key in state_dict:
+        return state_dict[start_prefix + first_key].dtype == model_to_load.state_dict()[first_key].dtype
+
+    return False
+
+
+def _load_shard_file(
+    shard_file,
+    model,
+    model_state_dict,
+    device_map=None,
+    dtype=None,
+    hf_quantizer=None,
+    keep_in_fp32_modules=None,
+    dduf_entries=None,
+    loaded_keys=None,
+    unexpected_keys=None,
+    offload_index=None,
+    offload_folder=None,
+    state_dict_index=None,
+    state_dict_folder=None,
+    ignore_mismatched_sizes=False,
+    low_cpu_mem_usage=False,
+):
+    state_dict = load_state_dict(shard_file, dduf_entries=dduf_entries)
+    mismatched_keys = _find_mismatched_keys(
+        state_dict,
+        model_state_dict,
+        loaded_keys,
+        ignore_mismatched_sizes,
+    )
+    error_msgs = []
+    if low_cpu_mem_usage:
+        offload_index, state_dict_index = load_model_dict_into_meta(
+            model,
+            state_dict,
+            device_map=device_map,
+            dtype=dtype,
+            hf_quantizer=hf_quantizer,
+            keep_in_fp32_modules=keep_in_fp32_modules,
+            unexpected_keys=unexpected_keys,
+            offload_folder=offload_folder,
+            offload_index=offload_index,
+            state_dict_index=state_dict_index,
+            state_dict_folder=state_dict_folder,
+        )
+    else:
+        assign_to_params_buffers = check_support_param_buffer_assignment(model, state_dict)
+
+        error_msgs += _load_state_dict_into_model(model, state_dict, assign_to_params_buffers)
+    return offload_index, state_dict_index, mismatched_keys, error_msgs
+
+
+def _load_shard_files_with_threadpool(
+    shard_files,
+    model,
+    model_state_dict,
+    device_map=None,
+    dtype=None,
+    hf_quantizer=None,
+    keep_in_fp32_modules=None,
+    dduf_entries=None,
+    loaded_keys=None,
+    unexpected_keys=None,
+    offload_index=None,
+    offload_folder=None,
+    state_dict_index=None,
+    state_dict_folder=None,
+    ignore_mismatched_sizes=False,
+    low_cpu_mem_usage=False,
+):
+    # Do not spawn anymore workers than you need
+    num_workers = min(len(shard_files), DEFAULT_HF_PARALLEL_LOADING_WORKERS)
+
+    logger.info(f"Loading model weights in parallel with {num_workers} workers...")
+
+    error_msgs = []
+    mismatched_keys = []
+
+    load_one = functools.partial(
+        _load_shard_file,
+        model=model,
+        model_state_dict=model_state_dict,
+        device_map=device_map,
+        dtype=dtype,
+        hf_quantizer=hf_quantizer,
+        keep_in_fp32_modules=keep_in_fp32_modules,
+        dduf_entries=dduf_entries,
+        loaded_keys=loaded_keys,
+        unexpected_keys=unexpected_keys,
+        offload_index=offload_index,
+        offload_folder=offload_folder,
+        state_dict_index=state_dict_index,
+        state_dict_folder=state_dict_folder,
+        ignore_mismatched_sizes=ignore_mismatched_sizes,
+        low_cpu_mem_usage=low_cpu_mem_usage,
+    )
+
+    with ThreadPoolExecutor(max_workers=num_workers) as executor:
+        with logging.tqdm(total=len(shard_files), desc="Loading checkpoint shards") as pbar:
+            futures = [executor.submit(load_one, shard_file) for shard_file in shard_files]
+            for future in as_completed(futures):
+                result = future.result()
+                offload_index, state_dict_index, _mismatched_keys, _error_msgs = result
+                error_msgs += _error_msgs
+                mismatched_keys += _mismatched_keys
+                pbar.update(1)
+
+    return offload_index, state_dict_index, mismatched_keys, error_msgs
+
+
+def _find_mismatched_keys(
+    state_dict,
+    model_state_dict,
+    loaded_keys,
+    ignore_mismatched_sizes,
+):
+    mismatched_keys = []
+    if ignore_mismatched_sizes:
+        for checkpoint_key in loaded_keys:
+            model_key = checkpoint_key
+            # If the checkpoint is sharded, we may not have the key here.
+            if checkpoint_key not in state_dict:
+                continue
+
+            if model_key in model_state_dict and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape:
+                mismatched_keys.append(
+                    (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
+                )
+                del state_dict[checkpoint_key]
+    return mismatched_keys
+
+
 def _load_state_dict_into_model(
     model_to_load, state_dict: OrderedDict, assign_to_params_buffers: bool = False
 ) -> List[str]:

src/diffusers/models/modeling_utils.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 import copy
+import functools
 import inspect
 import itertools
 import json
@@ -41,7 +42,9 @@ from ..quantizers import DiffusersAutoQuantizer, DiffusersQuantizer
 from ..quantizers.quantization_config import QuantizationMethod
 from ..utils import (
     CONFIG_NAME,
+    ENV_VARS_TRUE_VALUES,
     FLAX_WEIGHTS_NAME,
+    HF_PARALLEL_LOADING_FLAG,
     SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_WEIGHTS_NAME,
     WEIGHTS_INDEX_NAME,
@@ -69,9 +72,8 @@ from .model_loading_utils import (
     _expand_device_map,
     _fetch_index_file,
     _fetch_index_file_legacy,
-    _find_mismatched_keys,
-    _load_state_dict_into_model,
-    load_model_dict_into_meta,
+    _load_shard_file,
+    _load_shard_files_with_threadpool,
     load_state_dict,
 )
 
@@ -208,34 +210,6 @@ def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:
         return last_tuple[1].dtype
 
 
-def check_support_param_buffer_assignment(model_to_load, state_dict, start_prefix=""):
-    """
-    Checks if `model_to_load` supports param buffer assignment (such as when loading in empty weights) by first
-    checking if the model explicitly disables it, then by ensuring that the state dict keys are a subset of the model's
-    parameters.
-
-    """
-    if model_to_load.device.type == "meta":
-        return False
-
-    if len([key for key in state_dict if key.startswith(start_prefix)]) == 0:
-        return False
-
-    # Some models explicitly do not support param buffer assignment
-    if not getattr(model_to_load, "_supports_param_buffer_assignment", True):
-        logger.debug(
-            f"{model_to_load.__class__.__name__} does not support param buffer assignment, loading will be slower"
-        )
-        return False
-
-    # If the model does, the incoming `state_dict` and the `model_to_load` must be the same dtype
-    first_key = next(iter(model_to_load.state_dict().keys()))
-    if start_prefix + first_key in state_dict:
-        return state_dict[start_prefix + first_key].dtype == model_to_load.state_dict()[first_key].dtype
-
-    return False
-
-
 @contextmanager
 def no_init_weights():
     """
@@ -988,6 +962,10 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
+        is_parallel_loading_enabled = os.environ.get(HF_PARALLEL_LOADING_FLAG, "").upper() in ENV_VARS_TRUE_VALUES
+        if is_parallel_loading_enabled and not low_cpu_mem_usage:
+            raise NotImplementedError("Parallel loading is not supported when not using `low_cpu_mem_usage`.")
+
         if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
@@ -1323,6 +1301,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             hf_quantizer=hf_quantizer,
             keep_in_fp32_modules=keep_in_fp32_modules,
             dduf_entries=dduf_entries,
+            is_parallel_loading_enabled=is_parallel_loading_enabled,
         )
         loading_info = {
             "missing_keys": missing_keys,
@@ -1518,6 +1497,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         offload_state_dict: Optional[bool] = None,
         offload_folder: Optional[Union[str, os.PathLike]] = None,
         dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
+        is_parallel_loading_enabled: Optional[bool] = False,
     ):
         model_state_dict = model.state_dict()
         expected_keys = list(model_state_dict.keys())
@@ -1531,6 +1511,9 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             for pat in cls._keys_to_ignore_on_load_unexpected:
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
+        mismatched_keys = []
+        error_msgs = []
+
         # Deal with offload
         if device_map is not None and "disk" in device_map.values():
             if offload_folder is None:
@@ -1566,37 +1549,39 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             # if state dict is not None, it means that we don't need to read the files from resolved_model_file also
             resolved_model_file = [state_dict]
 
-        if len(resolved_model_file) > 1:
-            resolved_model_file = logging.tqdm(resolved_model_file, desc="Loading checkpoint shards")
+        # Prepare the loading function sharing the attributes shared between them.
+        load_fn = functools.partial(
+            _load_shard_files_with_threadpool if is_parallel_loading_enabled else _load_shard_file,
+            model=model,
+            model_state_dict=model_state_dict,
+            device_map=device_map,
+            dtype=dtype,
+            hf_quantizer=hf_quantizer,
+            keep_in_fp32_modules=keep_in_fp32_modules,
+            dduf_entries=dduf_entries,
+            loaded_keys=loaded_keys,
+            unexpected_keys=unexpected_keys,
+            offload_index=offload_index,
+            offload_folder=offload_folder,
+            state_dict_index=state_dict_index,
+            state_dict_folder=state_dict_folder,
+            ignore_mismatched_sizes=ignore_mismatched_sizes,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
-        mismatched_keys = []
-        assign_to_params_buffers = None
-        error_msgs = []
+        if is_parallel_loading_enabled:
+            offload_index, state_dict_index, _mismatched_keys, _error_msgs = load_fn(resolved_model_file)
+            error_msgs += _error_msgs
+            mismatched_keys += _mismatched_keys
+        else:
+            shard_files = resolved_model_file
+            if len(resolved_model_file) > 1:
+                shard_files = logging.tqdm(resolved_model_file, desc="Loading checkpoint shards")
 
-        for shard_file in resolved_model_file:
-            state_dict = load_state_dict(shard_file, dduf_entries=dduf_entries)
-            mismatched_keys += _find_mismatched_keys(
-                state_dict, model_state_dict, loaded_keys, ignore_mismatched_sizes
-            )
-
-            if low_cpu_mem_usage:
-                offload_index, state_dict_index = load_model_dict_into_meta(
-                    model,
-                    state_dict,
-                    device_map=device_map,
-                    dtype=dtype,
-                    hf_quantizer=hf_quantizer,
-                    keep_in_fp32_modules=keep_in_fp32_modules,
-                    unexpected_keys=unexpected_keys,
-                    offload_folder=offload_folder,
-                    offload_index=offload_index,
-                    state_dict_index=state_dict_index,
-                    state_dict_folder=state_dict_folder,
-                )
-            else:
-                if assign_to_params_buffers is None:
-                    assign_to_params_buffers = check_support_param_buffer_assignment(model, state_dict)
-                error_msgs += _load_state_dict_into_model(model, state_dict, assign_to_params_buffers)
+            for shard_file in shard_files:
+                offload_index, state_dict_index, _mismatched_keys, _error_msgs = load_fn(shard_file)
+                error_msgs += _error_msgs
+                mismatched_keys += _mismatched_keys
 
         empty_device_cache()
 

src/diffusers/pipelines/__init__.py

@@ -387,7 +387,11 @@ else:
         "SkyReelsV2ImageToVideoPipeline",
         "SkyReelsV2Pipeline",
     ]
-    _import_structure["qwenimage"] = ["QwenImagePipeline"]
+    _import_structure["qwenimage"] = [
+        "QwenImagePipeline",
+        "QwenImageImg2ImgPipeline",
+        "QwenImageInpaintPipeline",
+    ]
 try:
     if not is_onnx_available():
         raise OptionalDependencyNotAvailable()
@@ -704,7 +708,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .paint_by_example import PaintByExamplePipeline
         from .pia import PIAPipeline
         from .pixart_alpha import PixArtAlphaPipeline, PixArtSigmaPipeline
-        from .qwenimage import QwenImagePipeline
+        from .qwenimage import QwenImageImg2ImgPipeline, QwenImageInpaintPipeline, QwenImagePipeline
         from .sana import SanaControlNetPipeline, SanaPipeline, SanaSprintImg2ImgPipeline, SanaSprintPipeline
         from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
         from .shap_e import ShapEImg2ImgPipeline, ShapEPipeline

src/diffusers/pipelines/qwenimage/__init__.py

@@ -24,6 +24,8 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["modeling_qwenimage"] = ["ReduxImageEncoder"]
     _import_structure["pipeline_qwenimage"] = ["QwenImagePipeline"]
+    _import_structure["pipeline_qwenimage_img2img"] = ["QwenImageImg2ImgPipeline"]
+    _import_structure["pipeline_qwenimage_inpaint"] = ["QwenImageInpaintPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -33,6 +35,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
         from .pipeline_qwenimage import QwenImagePipeline
+        from .pipeline_qwenimage_img2img import QwenImageImg2ImgPipeline
+        from .pipeline_qwenimage_inpaint import QwenImageInpaintPipeline
 else:
     import sys
 

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_img2img.py

@@ -0,0 +1,839 @@
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import QwenImageLoraLoaderMixin
+from ...models import AutoencoderKLQwenImage, QwenImageTransformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import QwenImagePipelineOutput
+
+
+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 QwenImageImg2ImgPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = QwenImageImg2ImgPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
+        >>> pipe = 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))
+        >>> prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney"
+        >>> images = pipe(prompt=prompt, negative_prompt=" ", image=init_image, strength=0.95).images[0]
+        >>> images.save("qwenimage_img2img.png")
+        ```
+"""
+
+
+# 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.qwenimage.pipeline_qwenimage.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.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 QwenImageImg2ImgPipeline(DiffusionPipeline, QwenImageLoraLoaderMixin):
+    r"""
+    The QwenImage pipeline for text-to-image generation.
+
+    Args:
+        transformer ([`QwenImageTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        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 ([`Qwen2.5-VL-7B-Instruct`]):
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant.
+        tokenizer (`QwenTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKLQwenImage,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        tokenizer: Qwen2Tokenizer,
+        transformer: QwenImageTransformer2DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        # QwenImage latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible
+        # by the patch size. So the vae scale factor is multiplied by the patch size to account for this
+        self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
+        self.image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor * 2, vae_latent_channels=self.latent_channels
+        )
+        self.tokenizer_max_length = 1024
+        self.prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.prompt_template_encode_start_idx = 34
+        self.default_sample_size = 128
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._extract_masked_hidden
+    def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
+        bool_mask = mask.bool()
+        valid_lengths = bool_mask.sum(dim=1)
+        selected = hidden_states[bool_mask]
+        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
+
+        return split_result
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._get_qwen_prompt_embeds
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(e) for e in prompt]
+        txt_tokens = self.tokenizer(
+            txt, max_length=self.tokenizer_max_length + drop_idx, padding=True, truncation=True, return_tensors="pt"
+        ).to(device)
+        encoder_hidden_states = self.text_encoder(
+            input_ids=txt_tokens.input_ids,
+            attention_mask=txt_tokens.attention_mask,
+            output_hidden_states=True,
+        )
+        hidden_states = encoder_hidden_states.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, txt_tokens.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]
+        )
+        encoder_attention_mask = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]
+        )
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        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)
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.vae.config.z_dim, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            image_latents.device, image_latents.dtype
+        )
+
+        image_latents = (image_latents - latents_mean) * latents_std
+
+        return image_latents
+
+    # 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
+
+    # Copied fromCopied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 1024,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *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.
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, device)
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    def check_inputs(
+        self,
+        prompt,
+        strength,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        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 prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 1024:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
+
+    @staticmethod
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._prepare_latent_image_ids
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+        latent_image_ids = torch.zeros(height, width, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids.reshape(
+            latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+
+        return latent_image_ids.to(device=device, dtype=dtype)
+
+    @staticmethod
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._pack_latents
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (vae_scale_factor * 2))
+        width = 2 * (int(width) // (vae_scale_factor * 2))
+
+        latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width)
+
+        return latents
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def prepare_latents(
+        self,
+        image,
+        timestep,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, 1, num_channels_latents, height, width)
+
+        # If image is [B,C,H,W] -> add T=1. If it's already [B,C,T,H,W], leave it.
+        if image.dim() == 4:
+            image = image.unsqueeze(2)
+        elif image.dim() != 5:
+            raise ValueError(f"Expected image dims 4 or 5, got {image.dim()}.")
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
+        image = image.to(device=device, dtype=dtype)
+        if image.shape[1] != self.latent_channels:
+            image_latents = self._encode_vae_image(image=image, generator=generator)  # [B,z,1,H',W']
+        else:
+            image_latents = image
+        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            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."
+            )
+        else:
+            image_latents = torch.cat([image_latents], dim=0)
+
+        image_latents = image_latents.transpose(1, 2)  # [B,1,z,H',W']
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+
+        return latents, latent_image_ids
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @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,
+        negative_prompt: Union[str, List[str]] = None,
+        true_cfg_scale: float = 4.0,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        strength: float = 0.6,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 1.0,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        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.
+            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 `true_cfg_scale` is
+                not greater than `1`).
+            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
+                or 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)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            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`.
+            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 3.5):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
+            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.Tensor`, *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 (`torch.Tensor`, *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 (`torch.Tensor`, *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.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple.
+            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` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.qwenimage.QwenImagePipelineOutput`] or `tuple`:
+            [`~pipelines.qwenimage.QwenImagePipelineOutput`] 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 self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            strength,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Preprocess image
+        init_image = self.image_processor.preprocess(image, height=height, width=width)
+        init_image = init_image.to(dtype=torch.float32)
+
+        # 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 = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+        if do_true_cfg:
+            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+            )
+
+        # 4. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
+        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),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        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(batch_size * num_images_per_prompt)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            init_image,
+            latent_timestep,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        img_shapes = [(1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2)] * batch_size
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None
+        negative_txt_seq_lens = (
+            negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None
+        )
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                with self.transformer.cache_context("cond"):
+                    noise_pred = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        encoder_hidden_states_mask=prompt_embeds_mask,
+                        encoder_hidden_states=prompt_embeds,
+                        img_shapes=img_shapes,
+                        txt_seq_lens=txt_seq_lens,
+                        attention_kwargs=self.attention_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                if do_true_cfg:
+                    with self.transformer.cache_context("uncond"):
+                        neg_noise_pred = self.transformer(
+                            hidden_states=latents,
+                            timestep=timestep / 1000,
+                            guidance=guidance,
+                            encoder_hidden_states_mask=negative_prompt_embeds_mask,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            img_shapes=img_shapes,
+                            txt_seq_lens=negative_txt_seq_lens,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                    comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
+                    cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
+                    noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
+                    noise_pred = comb_pred * (cond_norm / noise_norm)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                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)
+
+                # 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()
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+
+            latents = latents / latents_std + latents_mean
+            image = self.vae.decode(latents, return_dict=False)[0][:, :, 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 QwenImagePipelineOutput(images=image)

src/diffusers/utils/__init__.py

@@ -20,11 +20,13 @@ from packaging import version
 from .. import __version__
 from .constants import (
     CONFIG_NAME,
+    DEFAULT_HF_PARALLEL_LOADING_WORKERS,
     DEPRECATED_REVISION_ARGS,
     DIFFUSERS_DYNAMIC_MODULE_NAME,
     FLAX_WEIGHTS_NAME,
     GGUF_FILE_EXTENSION,
     HF_MODULES_CACHE,
+    HF_PARALLEL_LOADING_FLAG,
     HUGGINGFACE_CO_RESOLVE_ENDPOINT,
     MIN_PEFT_VERSION,
     ONNX_EXTERNAL_WEIGHTS_NAME,

src/diffusers/utils/constants.py

@@ -43,6 +43,8 @@ DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
 DIFFUSERS_REQUEST_TIMEOUT = 60
 DIFFUSERS_ATTN_BACKEND = os.getenv("DIFFUSERS_ATTN_BACKEND", "native")
 DIFFUSERS_ATTN_CHECKS = os.getenv("DIFFUSERS_ATTN_CHECKS", "0") in ENV_VARS_TRUE_VALUES
+DEFAULT_HF_PARALLEL_LOADING_WORKERS = 8
+HF_PARALLEL_LOADING_FLAG = "HF_ENABLE_PARALLEL_LOADING"
 
 # Below should be `True` if the current version of `peft` and `transformers` are compatible with
 # PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1742,6 +1742,36 @@ class PixArtSigmaPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class QwenImageImg2ImgPipeline(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 QwenImageInpaintPipeline(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 QwenImagePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/models/test_modeling_common.py

@@ -1428,6 +1428,41 @@ class ModelTesterMixin:
 
             self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
+    @require_torch_accelerator
+    def test_sharded_checkpoints_with_parallel_loading(self):
+        torch.manual_seed(0)
+        config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**config).eval()
+        model = model.to(torch_device)
+
+        base_output = model(**inputs_dict)
+
+        model_size = compute_module_persistent_sizes(model)[""]
+        max_shard_size = int((model_size * 0.75) / (2**10))  # Convert to KB as these test models are small.
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.cpu().save_pretrained(tmp_dir, max_shard_size=f"{max_shard_size}KB")
+            self.assertTrue(os.path.exists(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
+
+            # Now check if the right number of shards exists. First, let's get the number of shards.
+            # Since this number can be dependent on the model being tested, it's important that we calculate it
+            # instead of hardcoding it.
+            expected_num_shards = caculate_expected_num_shards(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))
+            actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
+            self.assertTrue(actual_num_shards == expected_num_shards)
+
+            # Load with parallel loading
+            os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes"
+            new_model = self.model_class.from_pretrained(tmp_dir).eval()
+            new_model = new_model.to(torch_device)
+
+            torch.manual_seed(0)
+            if "generator" in inputs_dict:
+                _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+            new_output = new_model(**inputs_dict)
+            self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+            # set to no.
+            os.environ["HF_ENABLE_PARALLEL_LOADING"] = "no"
+
     @require_torch_accelerator
     def test_sharded_checkpoints_device_map(self):
         if self.model_class._no_split_modules is None:

tests/pipelines/qwenimage/test_qwenimage_img2img.py

@@ -0,0 +1,218 @@
+import random
+import unittest
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
+
+from diffusers import (
+    AutoencoderKLQwenImage,
+    FlowMatchEulerDiscreteScheduler,
+    QwenImageImg2ImgPipeline,
+    QwenImageTransformer2DModel,
+)
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    torch_device,
+)
+
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class QwenImageImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
+    pipeline_class = QwenImageImg2ImgPipeline
+    params = frozenset(["prompt", "image", "height", "width", "guidance_scale", "true_cfg_scale", "strength"])
+    batch_params = frozenset(["prompt", "image"])
+    image_params = frozenset(["image"])
+    image_latents_params = frozenset(["latents"])
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    supports_dduf = False
+    test_xformers_attention = False
+    test_attention_slicing = True
+    test_layerwise_casting = True
+    test_group_offloading = True
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = QwenImageTransformer2DModel(
+            patch_size=2,
+            in_channels=16,
+            out_channels=4,
+            num_layers=2,
+            attention_head_dim=16,
+            num_attention_heads=3,
+            joint_attention_dim=16,
+            guidance_embeds=False,
+            axes_dims_rope=(8, 4, 4),
+        )
+
+        torch.manual_seed(0)
+        z_dim = 4
+        vae = AutoencoderKLQwenImage(
+            base_dim=z_dim * 6,
+            z_dim=z_dim,
+            dim_mult=[1, 2, 4],
+            num_res_blocks=1,
+            temperal_downsample=[False, True],
+            latents_mean=[0.0] * 4,
+            latents_std=[1.0] * 4,
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        torch.manual_seed(0)
+        config = Qwen2_5_VLConfig(
+            text_config={
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 2,
+                "num_key_value_heads": 2,
+                "rope_scaling": {
+                    "mrope_section": [1, 1, 2],
+                    "rope_type": "default",
+                    "type": "default",
+                },
+                "rope_theta": 1000000.0,
+            },
+            vision_config={
+                "depth": 2,
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_heads": 2,
+                "out_hidden_size": 16,
+            },
+            hidden_size=16,
+            vocab_size=152064,
+            vision_end_token_id=151653,
+            vision_start_token_id=151652,
+            vision_token_id=151654,
+        )
+        text_encoder = Qwen2_5_VLForConditionalGeneration(config)
+        tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
+
+        return {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+
+    def get_dummy_inputs(self, device, seed=0):
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        inputs = {
+            "image": image,
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 3.0,
+            "true_cfg_scale": 1.0,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+
+        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, (3, 32, 32))
+
+    def test_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1)
+
+    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).images[0]
+
+        pipe.enable_attention_slicing(slice_size=1)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing1 = pipe(**inputs).images[0]
+
+        pipe.enable_attention_slicing(slice_size=2)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing2 = pipe(**inputs).images[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 = 0.2):
+        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
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling
+        pipe.vae.enable_tiling(
+            tile_sample_min_height=96,
+            tile_sample_min_width=96,
+            tile_sample_stride_height=64,
+            tile_sample_stride_width=64,
+        )
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        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",
+        )

tests/pipelines/qwenimage/test_qwenimage_inpaint.py

@@ -0,0 +1,233 @@
+# Copyright 2025 The HuggingFace Team.
+#
+# 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 random
+import unittest
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
+
+from diffusers import (
+    AutoencoderKLQwenImage,
+    FlowMatchEulerDiscreteScheduler,
+    QwenImageInpaintPipeline,
+    QwenImageTransformer2DModel,
+)
+from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class QwenImageInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = QwenImageInpaintPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "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 get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = QwenImageTransformer2DModel(
+            patch_size=2,
+            in_channels=16,
+            out_channels=4,
+            num_layers=2,
+            attention_head_dim=16,
+            num_attention_heads=3,
+            joint_attention_dim=16,
+            guidance_embeds=False,
+            axes_dims_rope=(8, 4, 4),
+        )
+
+        torch.manual_seed(0)
+        z_dim = 4
+        vae = AutoencoderKLQwenImage(
+            base_dim=z_dim * 6,
+            z_dim=z_dim,
+            dim_mult=[1, 2, 4],
+            num_res_blocks=1,
+            temperal_downsample=[False, True],
+            # fmt: off
+            latents_mean=[0.0] * 4,
+            latents_std=[1.0] * 4,
+            # fmt: on
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        torch.manual_seed(0)
+        config = Qwen2_5_VLConfig(
+            text_config={
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 2,
+                "num_key_value_heads": 2,
+                "rope_scaling": {
+                    "mrope_section": [1, 1, 2],
+                    "rope_type": "default",
+                    "type": "default",
+                },
+                "rope_theta": 1000000.0,
+            },
+            vision_config={
+                "depth": 2,
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_heads": 2,
+                "out_hidden_size": 16,
+            },
+            hidden_size=16,
+            vocab_size=152064,
+            vision_end_token_id=151653,
+            vision_start_token_id=151652,
+            vision_token_id=151654,
+        )
+        text_encoder = Qwen2_5_VLForConditionalGeneration(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):
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        mask_image = torch.ones((1, 1, 32, 32)).to(device)
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "image": image,
+            "mask_image": mask_image,
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 3.0,
+            "true_cfg_scale": 1.0,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+
+        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, (3, 32, 32))
+
+    def test_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1)
+
+    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 = 0.2):
+        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
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling
+        pipe.vae.enable_tiling(
+            tile_sample_min_height=96,
+            tile_sample_min_width=96,
+            tile_sample_stride_height=64,
+            tile_sample_stride_width=64,
+        )
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        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",
+        )