Update qwenimage/pipeline_qwenimage_edit_plus.py

qwenimage/pipeline_qwenimage_edit_plus.py

@@ -1,607 +1,828 @@
-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,
-    processor: Qwen2VLProcessor,
-    transformer: QwenImageTransformer2DModel,
-):
-    super().__init__()
-    self.register_modules(
-        vae=vae,
-        text_encoder=text_encoder,
-        tokenizer=tokenizer,
-        processor=processor,
-        transformer=transformer,
-        scheduler=scheduler,
+from __future__ import annotations
+
+# Copyright 2025 Qwen-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
+import math
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image, ImageOps
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import QwenImageLoraLoaderMixin
+from diffusers.models import AutoencoderKLQwenImage, QwenImageTransformer2DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.qwenimage.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 QwenImageEditPlusPipeline
+>>> from diffusers.utils import load_image
+
+>>> pipe = QwenImageEditPlusPipeline.from_pretrained(
+...     "Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16
+... ).to("cuda")
+
+>>> image = load_image(
+... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png"
+... ).convert("RGB")
+
+>>> prompt = "Make Pikachu hold a sign that says 'Qwen Edit is awesome', yarn art style, detailed, vibrant colors"
+
+>>> out = pipe(image=image, prompt=prompt, num_inference_steps=50).images[0]
+>>> out.save("qwenimage_edit_plus.png")
+```
+"""
+
+CONDITION_IMAGE_SIZE = 384 * 384
+VAE_IMAGE_SIZE = 1024 * 1024
+
+
+def pad_to_aspect(img: Image.Image, target_w: int, target_h: int) -> Image.Image:
+    """Pad (letterbox) to target aspect ratio without warping."""
+    return ImageOps.pad(
+        img.convert("RGB"),
+        (int(target_w), int(target_h)),
+        method=Image.Resampling.LANCZOS,
+        color=(0, 0, 0),
+        centering=(0.5, 0.5),
     )
 
-    self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
-    self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
 
-    # QwenImage latents are packed as 2x2 patches => multiply by patch size
-    self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
-    self.tokenizer_max_length = 1024
+def choose_condition_area(canvas_area: int, base_area: int = CONDITION_IMAGE_SIZE) -> int:
+    """Choose a conditioning target area derived from canvas area with sensible bounds."""
+    scaled = int(canvas_area * (base_area / (1024 * 1024)))
+    return int(min(base_area, max(256 * 256, scaled)))
 
-    self.prompt_template_encode = (
-        "<|im_start|>system\n"
-        "Describe the key features of the input image (color, shape, size, texture, objects, background), "
-        "then explain how the user's text instruction should alter or modify the image.\n"
-        "Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate."
-        "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
-    )
-    self.prompt_template_encode_start_idx = 64
-    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
-
-def _get_qwen_prompt_embeds(
-    self,
-    prompt: Union[str, List[str]] = None,
-    image: Optional[torch.Tensor] = None,
-    device: Optional[torch.device] = None,
-    dtype: Optional[torch.dtype] = None,
+
+# 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,
 ):
-    device = device or self._execution_device
-    dtype = dtype or self.text_encoder.dtype
-
-    prompt = [prompt] if isinstance(prompt, str) else prompt
-    img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
-
-    if isinstance(image, list):
-        base_img_prompt = ""
-        for i, _ in enumerate(image):
-            base_img_prompt += img_prompt_template.format(i + 1)
-    elif image is not None:
-        base_img_prompt = img_prompt_template.format(1)
+    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,
+):
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one.")
+
+    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 timesteps."
+            )
+        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 sigmas."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+
     else:
-        base_img_prompt = ""
-
-    template = self.prompt_template_encode
-    drop_idx = self.prompt_template_encode_start_idx
-    txt = [template.format(base_img_prompt + e) for e in prompt]
-
-    model_inputs = self.processor(
-        text=txt,
-        images=image,
-        padding=True,
-        return_tensors="pt",
-    ).to(device)
-
-    outputs = self.text_encoder(
-        input_ids=model_inputs.input_ids,
-        attention_mask=model_inputs.attention_mask,
-        pixel_values=model_inputs.pixel_values,
-        image_grid_thw=model_inputs.image_grid_thw,
-        output_hidden_states=True,
-    )
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
 
-    hidden_states = outputs.hidden_states[-1]
-    split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
-    split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+    return timesteps, num_inference_steps
 
-    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
-
-# Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.encode_prompt
-def encode_prompt(
-    self,
-    prompt: Union[str, List[str]],
-    image: Optional[torch.Tensor] = None,
-    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,
-):
-    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, image, 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
-
-# Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.check_inputs
-def check_inputs(
-    self,
-    prompt,
-    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 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."
-        )
+# 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)
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    if hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    raise AttributeError("Could not access latents of provided encoder_output")
+
+
+def calculate_dimensions(target_area: int, ratio: float, multiple: int = 32):
+    """
+    Area-based sizing while snapping to a chosen lattice multiple.
+    Used for canvas sizing AND conditioning sizing (anti-drift).
+    """
+    m = int(multiple) if multiple else 32
+    m = max(1, m)
+
+    width = math.sqrt(float(target_area) * float(ratio))
+    height = width / float(ratio)
+
+    width = round(width / m) * m
+    height = round(height / m) * m
+    return int(width), int(height)
+
 
-    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
+# Optional: decoder VAE (Wan2x)
+_ALT_VAE_WAN2X = None
+
+
+def _get_wan2x_vae(device: torch.device, dtype: torch.dtype):
+    """
+    Decoder-only finetune that outputs 2x resolution via pixel-shuffle.
+    Lazy-loaded so it doesn't impact startup unless used.
+    """
+    global _ALT_VAE_WAN2X
+    if _ALT_VAE_WAN2X is None:
+        from diffusers import AutoencoderKLWan
+
+        _ALT_VAE_WAN2X = AutoencoderKLWan.from_pretrained(
+            "spacepxl/Wan2.1-VAE-upscale2x",
+            subfolder="diffusers/Wan2.1_VAE_upscale2x_imageonly_real_v1",
+            torch_dtype=dtype,
+        )
+        _ALT_VAE_WAN2X.eval()
+    _ALT_VAE_WAN2X = _ALT_VAE_WAN2X.to(device=device, dtype=dtype)
+    return _ALT_VAE_WAN2X
+
+
+class QwenImageEditPlusPipeline(DiffusionPipeline, QwenImageLoraLoaderMixin):
+    r"""
+    The Qwen-Image-Edit pipeline for image editing.
+    """
+
+    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,
+        processor: Qwen2VLProcessor,
+        transformer: QwenImageTransformer2DModel,
     ):
-        raise ValueError(
-            f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found "
-            f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            processor=processor,
+            transformer=transformer,
+            scheduler=scheduler,
         )
 
-    if prompt is not None and prompt_embeds is not None:
-        raise ValueError("Cannot forward both `prompt` and `prompt_embeds`.")
-    elif prompt is None and prompt_embeds is None:
-        raise ValueError("Provide either `prompt` or `prompt_embeds`.")
-    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("Cannot forward both `negative_prompt` and `negative_prompt_embeds`.")
-
-    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.")
-
-    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.")
-
-    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
-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
-def _unpack_latents(latents, height, width, vae_scale_factor):
-    batch_size, _, channels = latents.shape
-    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 // 4, 1, height, width)
-    return latents
-
-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], sample_mode="argmax")
-            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, sample_mode="argmax")
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
 
-    latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, self.latent_channels, 1, 1, 1).to(
-        image_latents.device, image_latents.dtype
-    )
-    latents_std = torch.tensor(self.vae.config.latents_std).view(1, self.latent_channels, 1, 1, 1).to(
-        image_latents.device, image_latents.dtype
-    )
-    image_latents = (image_latents - latents_mean) / latents_std
-    return image_latents
-
-def prepare_latents(
-    self,
-    images,
-    batch_size,
-    num_channels_latents,
-    height,
-    width,
-    dtype,
-    device,
-    generator,
-    latents=None,
-):
-    height = 2 * (int(height) // (self.vae_scale_factor * 2))
-    width = 2 * (int(width) // (self.vae_scale_factor * 2))
-    shape = (batch_size, 1, num_channels_latents, height, width)
-
-    image_latents = None
-    if images is not None:
-        if not isinstance(images, list):
-            images = [images]
-        all_image_latents = []
-        for image in images:
-            image = image.to(device=device, dtype=dtype)
-            if image.shape[1] != self.latent_channels:
-                image_latents = self._encode_vae_image(image=image, generator=generator)
-            else:
-                image_latents = image
-
-            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."
-                )
+        # QwenImage latents are turned into 2x2 patches and packed; multiply scale-factor by patch size
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+        self.tokenizer_max_length = 1024
 
-            image_latent_height, image_latent_width = image_latents.shape[3:]
-            image_latents = self._pack_latents(
-                image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
-            )
-            all_image_latents.append(image_latents)
+        self.prompt_template_encode = (
+            "<|im_start|>system\n"
+            "Describe the key features of the input image (color, shape, size, texture, objects, background), "
+            "then explain how the user's text instruction should alter or modify the image.\n"
+            "Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate."
+            "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        )
+        self.prompt_template_encode_start_idx = 64
+        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
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Optional[torch.Tensor] = 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
+        img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
+
+        if isinstance(image, list):
+            base_img_prompt = ""
+            for i, _ in enumerate(image):
+                base_img_prompt += img_prompt_template.format(i + 1)
+        elif image is not None:
+            base_img_prompt = img_prompt_template.format(1)
+        else:
+            base_img_prompt = ""
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(base_img_prompt + e) for e in prompt]
+
+        model_inputs = self.processor(text=txt, images=image, padding=True, return_tensors="pt").to(device)
+
+        outputs = self.text_encoder(
+            input_ids=model_inputs.input_ids,
+            attention_mask=model_inputs.attention_mask,
+            pixel_values=model_inputs.pixel_values,
+            image_grid_thw=model_inputs.image_grid_thw,
+            output_hidden_states=True,
+        )
+
+        hidden_states = outputs.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
 
-        image_latents = torch.cat(all_image_latents, dim=1)
+        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])
 
-    if isinstance(generator, list) and len(generator) != batch_size:
-        raise ValueError(
-            f"You passed a list of generators of length {len(generator)}, but requested an effective batch size of {batch_size}."
+        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]
         )
 
-    if latents is None:
-        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
-    else:
-        latents = latents.to(device=device, dtype=dtype)
-
-    return latents, image_latents
-
-@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,
-    image: Optional[PipelineImageInput] = None,
-    prompt: Union[str, List[str]] = None,
-    negative_prompt: Union[str, List[str]] = None,
-    true_cfg_scale: float = 4.0,
-    height: Optional[int] = None,
-    width: Optional[int] = None,
-    condition_area: Optional[int] = None,
-    vae_image_indices: Optional[List[int]] = None,
-    pad_to_canvas: bool = True,
-    # NEW:
-    resolution_multiple: Optional[int] = None,
-    vae_ref_area: Optional[int] = None,
-    vae_ref_start_index: int = 2,
-    num_inference_steps: int = 50,
-    sigmas: Optional[List[float]] = None,
-    guidance_scale: Optional[float] = None,
-    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,
-):
-    image_size = image[0].size if isinstance(image, list) else image.size
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        return prompt_embeds, encoder_attention_mask
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        image: Optional[torch.Tensor] = None,
+        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,
+    ):
+        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]
 
-    multiple_of = int(resolution_multiple) if resolution_multiple is not None else int(self.vae_scale_factor * 2)
-    multiple_of = max(1, multiple_of)
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, image, device)
 
-    calculated_width, calculated_height = calculate_dimensions(
-        1024 * 1024, image_size[0] / image_size[1], multiple=multiple_of
-    )
-    height = height or calculated_height
-    width = width or calculated_width
+        _, seq_len, _ = prompt_embeds.shape
 
-    width = (int(width) // multiple_of) * multiple_of
-    height = (int(height) // multiple_of) * multiple_of
+        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)
 
-    # 1. Check inputs
-    self.check_inputs(
+        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
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.check_inputs
+    def check_inputs(
+        self,
         prompt,
         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
+        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 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."
+            )
 
-    # 2. Define call parameters
-    if prompt is not None and isinstance(prompt, str):
-        batch_size = 1
-    elif prompt is not None and isinstance(prompt, list):
-        batch_size = len(prompt)
-    else:
-        batch_size = prompt_embeds.shape[0]
-
-    device = self._execution_device
-
-    # 3. Preprocess image
-    if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
-        if not isinstance(image, list):
-            image = [image]
-
-        canvas_area = int(width) * int(height)
-        cond_area = int(condition_area) if condition_area is not None else choose_condition_area(canvas_area)
-
-        cond_w, cond_h = calculate_dimensions(cond_area, float(width) / float(height), multiple=multiple_of)
-
-        # Optional VAE ref override (for extra refs)
-        ref_w = ref_h = None
-        if vae_ref_area is not None:
-            try:
-                ref_w, ref_h = calculate_dimensions(int(vae_ref_area), float(width) / float(height), multiple=multiple_of)
-            except Exception:
-                ref_w = ref_h = None
-
-        condition_images = []
-        vae_images = []
-        vae_image_sizes = []
-
-        if vae_image_indices is None:
-            vae_image_indices = list(range(len(image)))
-        vae_set = set(int(i) for i in vae_image_indices)
-
-        for idx, img in enumerate(image):
-            pil = img.convert("RGB") if isinstance(img, Image.Image) else img
-
-            if pad_to_canvas and isinstance(pil, Image.Image):
-                pil = pad_to_aspect(pil, int(width), int(height))
-
-            # Conditioning path: always
-            condition_images.append(self.image_processor.resize(pil, cond_h, cond_w))
-
-            # VAE path: selected indices only
-            if idx in vae_set:
-                if (
-                    ref_w is not None
-                    and ref_h is not None
-                    and vae_ref_area is not None
-                    and int(idx) >= int(vae_ref_start_index)
-                ):
-                    vw, vh = int(ref_w), int(ref_h)
-                else:
-                    vw, vh = int(width), int(height)
+        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 "
+                f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
 
-                vae_image_sizes.append((vw, vh))
-                vae_images.append(self.image_processor.preprocess(pil, int(vh), int(vw)).unsqueeze(2))
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError("Cannot forward both `prompt` and `prompt_embeds`.")
+        if prompt is None and prompt_embeds is None:
+            raise ValueError("Provide either `prompt` or `prompt_embeds`.")
+        if 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("Cannot forward both `negative_prompt` and `negative_prompt_embeds`.")
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError("If `prompt_embeds` are provided, `prompt_embeds_mask` must also be passed.")
+
+        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` must also be passed.")
+
+        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
+    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
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, _, channels = latents.shape
+        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 // 4, 1, height, width)
+        return latents
+
+    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], sample_mode="argmax")
+                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, sample_mode="argmax")
 
-        has_neg_prompt = negative_prompt is not None or (
-            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+        latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, self.latent_channels, 1, 1, 1).to(
+            image_latents.device, image_latents.dtype
+        )
+        latents_std = torch.tensor(self.vae.config.latents_std).view(1, self.latent_channels, 1, 1, 1).to(
+            image_latents.device, image_latents.dtype
         )
+        image_latents = (image_latents - latents_mean) / latents_std
+        return image_latents
+
+    def prepare_latents(
+        self,
+        images,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+        shape = (batch_size, 1, num_channels_latents, height, width)
+
+        image_latents = None
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+            all_image_latents = []
+
+            for image in images:
+                image = image.to(device=device, dtype=dtype)
+                if image.shape[1] != self.latent_channels:
+                    image_latents = self._encode_vae_image(image=image, generator=generator)
+                else:
+                    image_latents = image
+
+                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."
+                    )
+
+                image_latent_height, image_latent_width = image_latents.shape[3:]
+                image_latents = self._pack_latents(
+                    image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
+                )
+                all_image_latents.append(image_latents)
 
-        if true_cfg_scale > 1 and not has_neg_prompt:
-            logger.warning(
-                f"true_cfg_scale is passed as {true_cfg_scale}, but classifier-free guidance is not enabled since no negative_prompt is provided."
+            image_latents = torch.cat(all_image_latents, dim=1)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You passed a list of generators of length {len(generator)}, but requested an effective batch size of {batch_size}."
             )
-        elif true_cfg_scale <= 1 and has_neg_prompt:
-            logger.warning("negative_prompt is passed but classifier-free guidance is not enabled since true_cfg_scale <= 1")
 
-        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        return latents, image_latents
+
+    @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,
+        image: Optional[PipelineImageInput] = None,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        true_cfg_scale: float = 4.0,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        condition_area: Optional[int] = None,
+        vae_image_indices: Optional[List[int]] = None,
+        pad_to_canvas: bool = True,
+        # NEW: lattice + VAE ref override
+        resolution_multiple: Optional[int] = None,
+        vae_ref_area: Optional[int] = None,
+        vae_ref_start_index: int = 2,
+        # Optional: decoder swap
+        decoder_vae: str = "qwen",  # "qwen" | "wan2x"
+        keep_decoder_2x: bool = False,
+        # standard args
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: Optional[float] = None,
+        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,
+    ):
+        # ---- determine input size ----
+        if isinstance(image, list):
+            image_size = image[0].size
+        else:
+            image_size = image.size
+
+        # Lattice multiple used throughout (canvas sizing + condition sizing)
+        multiple_of = int(resolution_multiple) if resolution_multiple is not None else (self.vae_scale_factor * 2)
+        multiple_of = max(1, multiple_of)
+
+        calculated_width, calculated_height = calculate_dimensions(
+            1024 * 1024, float(image_size[0]) / float(image_size[1]), multiple=multiple_of
+        )
+        height = height or calculated_height
+        width = width or calculated_width
+
+        width = (int(width) // multiple_of) * multiple_of
+        height = (int(height) // multiple_of) * multiple_of
 
-        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
-            image=condition_images,
-            prompt=prompt,
+        # ---- validate ----
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
             prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
             prompt_embeds_mask=prompt_embeds_mask,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
+            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,
         )
 
-        if do_true_cfg:
-            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # ---- call params ----
+        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
+
+        # ---- preprocess ----
+        condition_images = None
+        vae_images = None
+        vae_image_sizes: List[tuple[int, int]] = []
+
+        # support pre-latent tensors (rare, but keep compatibility)
+        if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
+            if not isinstance(image, list):
+                image = [image]
+
+            canvas_area = int(width) * int(height)
+            cond_area = int(condition_area) if condition_area is not None else choose_condition_area(canvas_area)
+
+            cond_w, cond_h = calculate_dimensions(cond_area, float(width) / float(height), multiple=multiple_of)
+
+            # Optional VAE ref override sizing (applied only to indices >= vae_ref_start_index)
+            ref_w = ref_h = None
+            if vae_ref_area is not None:
+                try:
+                    ref_w, ref_h = calculate_dimensions(
+                        int(vae_ref_area),
+                        float(width) / float(height),
+                        multiple=multiple_of,
+                    )
+                except Exception:
+                    ref_w = ref_h = None
+
+            condition_images = []
+            vae_images = []
+
+            if vae_image_indices is None:
+                vae_image_indices = list(range(len(image)))
+            vae_set = set(int(i) for i in vae_image_indices)
+
+            for idx, img in enumerate(image):
+                pil = img.convert("RGB") if isinstance(img, Image.Image) else img
+
+                if pad_to_canvas and isinstance(pil, Image.Image):
+                    pil = pad_to_aspect(pil, int(width), int(height))
+
+                # conditioning stream (always)
+                condition_images.append(self.image_processor.resize(pil, cond_h, cond_w))
+
+                # VAE stream (selective)
+                if idx in vae_set:
+                    if (ref_w is not None) and (ref_h is not None) and (int(idx) >= int(vae_ref_start_index)):
+                        vw, vh = int(ref_w), int(ref_h)
+                    else:
+                        vw, vh = int(width), int(height)
+
+                    vae_image_sizes.append((vw, vh))
+                    vae_images.append(self.image_processor.preprocess(pil, int(vh), int(vw)).unsqueeze(2))
+
+            has_neg_prompt = negative_prompt is not None or (
+                negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+            )
+            if true_cfg_scale > 1 and not has_neg_prompt:
+                logger.warning(
+                    f"true_cfg_scale={true_cfg_scale} but CFG disabled because no negative prompt was provided."
+                )
+            if true_cfg_scale <= 1 and has_neg_prompt:
+                logger.warning("negative_prompt provided but CFG disabled because true_cfg_scale <= 1")
+
+            do_true_cfg = (true_cfg_scale > 1) and has_neg_prompt
+
+            prompt_embeds, prompt_embeds_mask = self.encode_prompt(
                 image=condition_images,
-                prompt=negative_prompt,
-                prompt_embeds=negative_prompt_embeds,
-                prompt_embeds_mask=negative_prompt_embeds_mask,
+                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,
             )
 
-        # 4. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels // 4
-        latents, image_latents = self.prepare_latents(
-            vae_images,
-            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),
-                *[
-                    (1, vae_h // self.vae_scale_factor // 2, vae_w // self.vae_scale_factor // 2)
-                    for (vae_w, vae_h) in vae_image_sizes
-                ],
-            ]
-        ] * batch_size
-
-    # 5. Prepare timesteps
-    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
-
-    image_seq_len = latents.shape[1]
-    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
-    )
+            if do_true_cfg:
+                negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                    image=condition_images,
+                    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,
+                )
 
-    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 and guidance_scale is None:
-        raise ValueError("guidance_scale is required for guidance-distilled model.")
-    elif self.transformer.config.guidance_embeds:
-        guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0])
-    elif not self.transformer.config.guidance_embeds and guidance_scale is not None:
-        logger.warning(
-            f"guidance_scale is passed as {guidance_scale}, but ignored since the model is not guidance-distilled."
-        )
-        guidance = None
-    else:
-        guidance = None
+            # ---- prepare latents ----
+            num_channels_latents = self.transformer.config.in_channels // 4
+            latents, image_latents = self.prepare_latents(
+                vae_images,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+            )
 
-    if self.attention_kwargs is None:
-        self._attention_kwargs = {}
+            img_shapes = [
+                [
+                    (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
+                    *[
+                        (1, vae_h // self.vae_scale_factor // 2, vae_w // self.vae_scale_factor // 2)
+                        for (vae_w, vae_h) in vae_image_sizes
+                    ],
+                ]
+            ] * batch_size
+
+        else:
+            raise ValueError(
+                "This Space pipeline expects `image` as PIL/np inputs (not pre-latents) in this setup."
+            )
 
-    txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None
-    image_rotary_emb = self.transformer.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+        # ---- timesteps ----
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
 
-    if do_true_cfg:
-        negative_txt_seq_lens = (
-            negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None
+        image_seq_len = latents.shape[1]
+        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
         )
-        uncond_image_rotary_emb = self.transformer.pos_embed(img_shapes, negative_txt_seq_lens, device=latents.device)
-    else:
-        uncond_image_rotary_emb = None
-
-    # 6. Denoising loop
-    self.scheduler.set_begin_index(0)
-    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
-
-            latent_model_input = latents
-            if image_latents is not None:
-                latent_model_input = torch.cat([latents, image_latents], dim=1)
-
-            timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-            with self.transformer.cache_context("cond"):
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep / 1000,
-                    guidance=guidance,
-                    encoder_hidden_states_mask=prompt_embeds_mask,
-                    encoder_hidden_states=prompt_embeds,
-                    image_rotary_emb=image_rotary_emb,
-                    attention_kwargs=self.attention_kwargs,
-                    return_dict=False,
-                )[0]
-            noise_pred = noise_pred[:, : latents.size(1)]
 
-            if do_true_cfg:
-                with self.transformer.cache_context("uncond"):
-                    neg_noise_pred = self.transformer(
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # guidance-distilled models need explicit guidance input
+        if self.transformer.config.guidance_embeds and guidance_scale is None:
+            raise ValueError("guidance_scale is required for guidance-distilled model.")
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0])
+        else:
+            if guidance_scale is not None:
+                logger.warning("guidance_scale passed but ignored since model is not guidance-distilled.")
+            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
+        image_rotary_emb = self.transformer.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+
+        do_true_cfg = (
+            (true_cfg_scale > 1)
+            and (negative_prompt_embeds is not None)
+            and (negative_prompt_embeds_mask is not None)
+        )
+        if do_true_cfg:
+            negative_txt_seq_lens = negative_prompt_embeds_mask.sum(dim=1).tolist()
+            uncond_image_rotary_emb = self.transformer.pos_embed(img_shapes, negative_txt_seq_lens, device=latents.device)
+        else:
+            uncond_image_rotary_emb = None
+
+        # ---- denoise ----
+        self.scheduler.set_begin_index(0)
+        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
+
+                latent_model_input = latents
+                if image_latents is not None:
+                    latent_model_input = torch.cat([latents, image_latents], dim=1)
+
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                with self.transformer.cache_context("cond"):
+                    noise_pred = self.transformer(
                         hidden_states=latent_model_input,
                         timestep=timestep / 1000,
                         guidance=guidance,
-                        encoder_hidden_states_mask=negative_prompt_embeds_mask,
-                        encoder_hidden_states=negative_prompt_embeds,
-                        image_rotary_emb=uncond_image_rotary_emb,
+                        encoder_hidden_states_mask=prompt_embeds_mask,
+                        encoder_hidden_states=prompt_embeds,
+                        image_rotary_emb=image_rotary_emb,
                         attention_kwargs=self.attention_kwargs,
                         return_dict=False,
                     )[0]
-                neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
-
-                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)
-
-            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():
+                noise_pred = noise_pred[:, : latents.size(1)]
+
+                if do_true_cfg:
+                    with self.transformer.cache_context("uncond"):
+                        neg_noise_pred = self.transformer(
+                            hidden_states=latent_model_input,
+                            timestep=timestep / 1000,
+                            guidance=guidance,
+                            encoder_hidden_states_mask=negative_prompt_embeds_mask,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            image_rotary_emb=uncond_image_rotary_emb,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                    neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
+
+                    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 + 1e-8))
+
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                if latents.dtype != latents_dtype and torch.backends.mps.is_available():
                     latents = latents.to(latents_dtype)
 
-            if callback_on_step_end is not None:
-                callback_kwargs = {k: locals()[k] for k in callback_on_step_end_tensor_inputs}
-                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)
+                if callback_on_step_end is not None:
+                    callback_kwargs = {k: locals()[k] for k in callback_on_step_end_tensor_inputs}
+                    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)
 
-            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                progress_bar.update()
+                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 XLA_AVAILABLE:
+                    xm.mark_step()
 
-    self._current_timestep = None
+        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)
+        # ---- decode ----
+        if output_type == "latent":
+            image_out = 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
+            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
+
+            if decoder_vae == "wan2x":
+                alt_vae = _get_wan2x_vae(latents.device, self.vae.dtype)
+                decoder_out = alt_vae.decode(latents, return_dict=False)[0]  # [B, 12, F, H, W]
+                img_2x = F.pixel_shuffle(decoder_out[:, :, 0], upscale_factor=2)  # [B, 3, 2H, 2W]
+                if keep_decoder_2x:
+                    decoded = img_2x
+                else:
+                    decoded = F.interpolate(img_2x, size=(int(height), int(width)), mode="area")
+            else:
+                decoded = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
 
-        image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
-        image = self.image_processor.postprocess(image, output_type=output_type)
+            image_out = self.image_processor.postprocess(decoded, output_type=output_type)
 
-    self.maybe_free_model_hooks()
+        self.maybe_free_model_hooks()
 
-    if not return_dict:
-        return (image,)
-    return QwenImagePipelineOutput(images=image)
+        if not return_dict:
+            return (image_out,)
+        return QwenImagePipelineOutput(images=image_out)