up

.github/workflows/codeql.yml

@@ -1,22 +0,0 @@
----
-name: CodeQL Security Analysis For Github Actions
-
-on:
-  push:
-    branches: ["main"]
-  workflow_dispatch:
-  # pull_request:
-
-jobs:
-  codeql:
-    name: CodeQL Analysis
-    uses: huggingface/security-workflows/.github/workflows/codeql-reusable.yml@v1
-    permissions:
-      security-events: write
-      packages: read
-      actions: read
-      contents: read
-    with:
-      languages: '["actions","python"]'
-      queries: 'security-extended,security-and-quality'
-      runner: 'ubuntu-latest' #optional if need custom runner

.github/workflows/mirror_community_pipeline.yml

@@ -24,6 +24,7 @@ jobs:
   mirror_community_pipeline:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
+
     runs-on: ubuntu-22.04
     steps:
       # Checkout to correct ref
@@ -38,28 +39,25 @@ jobs:
       #     If ref is 'refs/heads/main' => set 'main'
       #     Else it must be a tag => set {tag}
       - name: Set checkout_ref and path_in_repo
-          EVENT_NAME: ${{ github.event_name }}
-          EVENT_INPUT_REF: ${{ github.event.inputs.ref }}
-          GITHUB_REF: ${{ github.ref }}
         run: |
-          if [ "$EVENT_NAME" == "workflow_dispatch" ]; then
-            if [ -z "$EVENT_INPUT_REF" ]; then
+          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
+            if [ -z "${{ github.event.inputs.ref }}" ]; then
               echo "Error: Missing ref input"
               exit 1
-            elif [ "$EVENT_INPUT_REF" == "main" ]; then
+            elif [ "${{ github.event.inputs.ref }}" == "main" ]; then
               echo "CHECKOUT_REF=refs/heads/main" >> $GITHUB_ENV
               echo "PATH_IN_REPO=main" >> $GITHUB_ENV
             else
-              echo "CHECKOUT_REF=refs/tags/$EVENT_INPUT_REF" >> $GITHUB_ENV
-              echo "PATH_IN_REPO=$EVENT_INPUT_REF" >> $GITHUB_ENV
+              echo "CHECKOUT_REF=refs/tags/${{ github.event.inputs.ref }}" >> $GITHUB_ENV
+              echo "PATH_IN_REPO=${{ github.event.inputs.ref }}" >> $GITHUB_ENV
             fi
-          elif [ "$GITHUB_REF" == "refs/heads/main" ]; then
-            echo "CHECKOUT_REF=$GITHUB_REF" >> $GITHUB_ENV
+          elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
+            echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
             echo "PATH_IN_REPO=main" >> $GITHUB_ENV
           else
             # e.g. refs/tags/v0.28.1 -> v0.28.1
-            echo "CHECKOUT_REF=$GITHUB_REF" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=$(echo $GITHUB_REF | sed 's/^refs\/tags\///')" >> $GITHUB_ENV
+            echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
+            echo "PATH_IN_REPO=$(echo ${{ github.ref }} | sed 's/^refs\/tags\///')" >> $GITHUB_ENV
           fi
       - name: Print env vars
         run: |
@@ -101,4 +99,4 @@ jobs:
       - name: Report failure status
         if: ${{ failure() }}
         run: |
-          pip install requests && python utils/notify_community_pipelines_mirror.py --status=failure
+          pip install requests && python utils/notify_community_pipelines_mirror.py --status=failure

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

@@ -70,12 +70,6 @@ output.save("output.png")
   - all
   - __call__
 
-## Cosmos2_5_PredictBasePipeline
-
-[[autodoc]] Cosmos2_5_PredictBasePipeline
-  - all
-  - __call__
-
 ## CosmosPipelineOutput
 
 [[autodoc]] pipelines.cosmos.pipeline_output.CosmosPipelineOutput

docs/source/en/training/distributed_inference.md

@@ -263,8 +263,8 @@ def main():
     world_size = dist.get_world_size()
 
     pipeline = DiffusionPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
-    ).to(device)
+        "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map=device
+    )
     pipeline.transformer.set_attention_backend("_native_cudnn")
 
     cp_config = ContextParallelConfig(ring_degree=world_size)

examples/community/pipeline_hunyuandit_differential_img2img.py

@@ -21,8 +21,8 @@ from transformers import (
     BertModel,
     BertTokenizer,
     CLIPImageProcessor,
+    MT5Tokenizer,
     T5EncoderModel,
-    T5Tokenizer,
 )
 
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
@@ -260,7 +260,7 @@ class HunyuanDiTDifferentialImg2ImgPipeline(DiffusionPipeline):
             The HunyuanDiT model designed by Tencent Hunyuan.
         text_encoder_2 (`T5EncoderModel`):
             The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
-        tokenizer_2 (`T5Tokenizer`):
+        tokenizer_2 (`MT5Tokenizer`):
             The tokenizer for the mT5 embedder.
         scheduler ([`DDPMScheduler`]):
             A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
@@ -295,7 +295,7 @@ class HunyuanDiTDifferentialImg2ImgPipeline(DiffusionPipeline):
         feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
         text_encoder_2=T5EncoderModel,
-        tokenizer_2=T5Tokenizer,
+        tokenizer_2=MT5Tokenizer,
     ):
         super().__init__()
 

examples/community/pipeline_z_image_differential_img2img.py

@@ -1,844 +0,0 @@
-# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from transformers import AutoTokenizer, PreTrainedModel
-
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.loaders import FromSingleFileMixin, ZImageLoraLoaderMixin
-from diffusers.models.autoencoders import AutoencoderKL
-from diffusers.models.transformers import ZImageTransformer2DModel
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.z_image.pipeline_output import ZImagePipelineOutput
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from pipeline_z_image_differential_img2img import ZImageDifferentialImg2ImgPipeline
-        >>> from diffusers.utils import load_image
-
-        >>> pipe = ZImageDifferentialImg2ImgPipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
-        >>> pipe.to("cuda")
-
-        >>> init_image = load_image(
-        >>>     "https://github.com/exx8/differential-diffusion/blob/main/assets/input.jpg?raw=true",
-        >>> )
-
-        >>> mask = load_image(
-        >>>     "https://github.com/exx8/differential-diffusion/blob/main/assets/map.jpg?raw=true",
-        >>> )
-
-        >>> prompt = "painting of a mountain landscape with a meadow and a forest, meadow background, anime countryside landscape, anime nature wallpap, anime landscape wallpaper, studio ghibli landscape, anime landscape, mountain behind meadow, anime background art, studio ghibli environment, background of flowery hill, anime beautiful peace scene, forrest background, anime scenery, landscape background, background art, anime scenery concept art"
-
-        >>> image = pipe(
-        ...     prompt,
-        ...     image=init_image,
-        ...     mask_image=mask,
-        ...     strength=0.75,
-        ...     num_inference_steps=9,
-        ...     guidance_scale=0.0,
-        ...     generator=torch.Generator("cuda").manual_seed(41),
-        ... ).images[0]
-        >>> image.save("image.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-    b = base_shift - m * base_seq_len
-    mu = image_seq_len * m + b
-    return mu
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    r"""
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
-            must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
-class ZImageDifferentialImg2ImgPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
-    r"""
-    The ZImage pipeline for image-to-image generation.
-
-    Args:
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`PreTrainedModel`]):
-            A text encoder model to encode text prompts.
-        tokenizer ([`AutoTokenizer`]):
-            A tokenizer to tokenize text prompts.
-        transformer ([`ZImageTransformer2DModel`]):
-            A ZImage transformer model to denoise the encoded image latents.
-    """
-
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds"]
-
-    def __init__(
-        self,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKL,
-        text_encoder: PreTrainedModel,
-        tokenizer: AutoTokenizer,
-        transformer: ZImageTransformer2DModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            transformer=transformer,
-        )
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
-        )
-        latent_channels = self.vae.config.latent_channels if getattr(self, "vae", None) else 16
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
-
-        self.mask_processor = VaeImageProcessor(
-            vae_scale_factor=self.vae_scale_factor,
-            vae_latent_channels=latent_channels,
-            do_normalize=False,
-            do_binarize=False,
-            do_convert_grayscale=True,
-        )
-
-    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        max_sequence_length: int = 512,
-    ):
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        prompt_embeds = self._encode_prompt(
-            prompt=prompt,
-            device=device,
-            prompt_embeds=prompt_embeds,
-            max_sequence_length=max_sequence_length,
-        )
-
-        if do_classifier_free_guidance:
-            if negative_prompt is None:
-                negative_prompt = ["" for _ in prompt]
-            else:
-                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-            assert len(prompt) == len(negative_prompt)
-            negative_prompt_embeds = self._encode_prompt(
-                prompt=negative_prompt,
-                device=device,
-                prompt_embeds=negative_prompt_embeds,
-                max_sequence_length=max_sequence_length,
-            )
-        else:
-            negative_prompt_embeds = []
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        max_sequence_length: int = 512,
-    ) -> List[torch.FloatTensor]:
-        device = device or self._execution_device
-
-        if prompt_embeds is not None:
-            return prompt_embeds
-
-        if isinstance(prompt, str):
-            prompt = [prompt]
-
-        for i, prompt_item in enumerate(prompt):
-            messages = [
-                {"role": "user", "content": prompt_item},
-            ]
-            prompt_item = self.tokenizer.apply_chat_template(
-                messages,
-                tokenize=False,
-                add_generation_prompt=True,
-                enable_thinking=True,
-            )
-            prompt[i] = prompt_item
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids.to(device)
-        prompt_masks = text_inputs.attention_mask.to(device).bool()
-
-        prompt_embeds = self.text_encoder(
-            input_ids=text_input_ids,
-            attention_mask=prompt_masks,
-            output_hidden_states=True,
-        ).hidden_states[-2]
-
-        embeddings_list = []
-
-        for i in range(len(prompt_embeds)):
-            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])
-
-        return embeddings_list
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(num_inference_steps * strength, num_inference_steps)
-
-        t_start = int(max(num_inference_steps - init_timestep, 0))
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-        if hasattr(self.scheduler, "set_begin_index"):
-            self.scheduler.set_begin_index(t_start * self.scheduler.order)
-
-        return timesteps, num_inference_steps - t_start
-
-    @staticmethod
-    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
-        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
-        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[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)
-
-    def prepare_latents(
-        self,
-        image,
-        timestep,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-    ):
-        height = 2 * (int(height) // (self.vae_scale_factor * 2))
-        width = 2 * (int(width) // (self.vae_scale_factor * 2))
-
-        shape = (batch_size, num_channels_latents, height, width)
-        latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
-
-        if latents is not None:
-            return latents.to(device=device, dtype=dtype)
-
-        # Encode the input image
-        image = image.to(device=device, dtype=dtype)
-        if image.shape[1] != num_channels_latents:
-            if isinstance(generator, list):
-                image_latents = [
-                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
-                    for i in range(image.shape[0])
-                ]
-                image_latents = torch.cat(image_latents, dim=0)
-            else:
-                image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
-
-            # Apply scaling (inverse of decoding: decode does latents/scaling_factor + shift_factor)
-            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-        else:
-            image_latents = image
-
-        # Handle batch size expansion
-        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
-            additional_image_per_prompt = batch_size // image_latents.shape[0]
-            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
-        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
-            )
-
-        # Add noise using flow matching scale_noise
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
-
-        return latents, noise, image_latents, latent_image_ids
-
-    def prepare_mask_latents(
-        self,
-        mask,
-        masked_image,
-        batch_size,
-        num_images_per_prompt,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-    ):
-        height = 2 * (int(height) // (self.vae_scale_factor * 2))
-        width = 2 * (int(width) // (self.vae_scale_factor * 2))
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(mask, size=(height, width))
-        mask = mask.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        if masked_image.shape[1] == 16:
-            masked_image_latents = masked_image
-        else:
-            masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)
-
-        masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-
-        return mask, masked_image_latents
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def joint_attention_kwargs(self):
-        return self._joint_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: PipelineImageInput = None,
-        mask_image: PipelineImageInput = None,
-        strength: float = 0.6,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        sigmas: Optional[List[float]] = None,
-        guidance_scale: float = 5.0,
-        cfg_normalization: bool = False,
-        cfg_truncation: float = 1.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 512,
-    ):
-        r"""
-        Function invoked when calling the pipeline for image-to-image 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.
-            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
-                numpy array and pytorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a
-                list of tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or
-                a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
-            mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image`, numpy array or tensor representing an image batch to mask `image`. Black pixels in the mask
-                are repainted while white pixels are preserved. If `mask_image` is a PIL image, it is converted to a
-                single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one
-                color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,
-                H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,
-                1)`, or `(H, W)`.
-            strength (`float`, *optional*, defaults to 0.6):
-                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`.
-            height (`int`, *optional*, defaults to 1024):
-                The height in pixels of the generated image. If not provided, uses the input image height.
-            width (`int`, *optional*, defaults to 1024):
-                The width in pixels of the generated image. If not provided, uses the input image width.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            sigmas (`List[float]`, *optional*):
-                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
-                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
-                will be used.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            cfg_normalization (`bool`, *optional*, defaults to False):
-                Whether to apply configuration normalization.
-            cfg_truncation (`float`, *optional*, defaults to 1.0):
-                The truncation value for configuration.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will be generated by sampling using the supplied random `generator`.
-            prompt_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain
-                tuple.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int`, *optional*, defaults to 512):
-                Maximum sequence length to use with the `prompt`.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-        # 1. Check inputs and validate strength
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should be in [0.0, 1.0] but is {strength}")
-
-        # 2. Preprocess image
-        init_image = self.image_processor.preprocess(image)
-        init_image = init_image.to(dtype=torch.float32)
-
-        # Get dimensions from the preprocessed image if not specified
-        if height is None:
-            height = init_image.shape[-2]
-        if width is None:
-            width = init_image.shape[-1]
-
-        vae_scale = self.vae_scale_factor * 2
-        if height % vae_scale != 0:
-            raise ValueError(
-                f"Height must be divisible by {vae_scale} (got {height}). "
-                f"Please adjust the height to a multiple of {vae_scale}."
-            )
-        if width % vae_scale != 0:
-            raise ValueError(
-                f"Width must be divisible by {vae_scale} (got {width}). "
-                f"Please adjust the width to a multiple of {vae_scale}."
-            )
-
-        device = self._execution_device
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-        self._cfg_normalization = cfg_normalization
-        self._cfg_truncation = cfg_truncation
-
-        # 3. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = len(prompt_embeds)
-
-        # If prompt_embeds is provided and prompt is None, skip encoding
-        if prompt_embeds is not None and prompt is None:
-            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
-                raise ValueError(
-                    "When `prompt_embeds` is provided without `prompt`, "
-                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
-                )
-        else:
-            (
-                prompt_embeds,
-                negative_prompt_embeds,
-            ) = self.encode_prompt(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-                prompt_embeds=prompt_embeds,
-                negative_prompt_embeds=negative_prompt_embeds,
-                device=device,
-                max_sequence_length=max_sequence_length,
-            )
-
-        # 4. Prepare latent variables
-        num_channels_latents = self.transformer.in_channels
-
-        # Repeat prompt_embeds for num_images_per_prompt
-        if num_images_per_prompt > 1:
-            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
-            if self.do_classifier_free_guidance and negative_prompt_embeds:
-                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
-
-        actual_batch_size = batch_size * num_images_per_prompt
-
-        # Calculate latent dimensions for image_seq_len
-        latent_height = 2 * (int(height) // (self.vae_scale_factor * 2))
-        latent_width = 2 * (int(width) // (self.vae_scale_factor * 2))
-        image_seq_len = (latent_height // 2) * (latent_width // 2)
-
-        # 5. Prepare timesteps
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.get("base_image_seq_len", 256),
-            self.scheduler.config.get("max_image_seq_len", 4096),
-            self.scheduler.config.get("base_shift", 0.5),
-            self.scheduler.config.get("max_shift", 1.15),
-        )
-        self.scheduler.sigma_min = 0.0
-        scheduler_kwargs = {"mu": mu}
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            sigmas=sigmas,
-            **scheduler_kwargs,
-        )
-
-        # 6. Adjust timesteps based on strength
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        if num_inference_steps < 1:
-            raise ValueError(
-                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline "
-                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
-            )
-        latent_timestep = timesteps[:1].repeat(actual_batch_size)
-
-        # 7. Prepare latents from image
-        latents, noise, original_image_latents, latent_image_ids = self.prepare_latents(
-            init_image,
-            latent_timestep,
-            actual_batch_size,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds[0].dtype,
-            device,
-            generator,
-            latents,
-        )
-        resize_mode = "default"
-        crops_coords = None
-
-        # start diff diff preparation
-        original_mask = self.mask_processor.preprocess(
-            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
-        )
-
-        masked_image = init_image * original_mask
-        original_mask, _ = self.prepare_mask_latents(
-            original_mask,
-            masked_image,
-            batch_size,
-            num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds[0].dtype,
-            device,
-            generator,
-        )
-        mask_thresholds = torch.arange(num_inference_steps, dtype=original_mask.dtype) / num_inference_steps
-        mask_thresholds = mask_thresholds.reshape(-1, 1, 1, 1).to(device)
-        masks = original_mask > mask_thresholds
-        # end diff diff preparation
-
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 8. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latents.shape[0])
-                timestep = (1000 - timestep) / 1000
-                # Normalized time for time-aware config (0 at start, 1 at end)
-                t_norm = timestep[0].item()
-
-                # Handle cfg truncation
-                current_guidance_scale = self.guidance_scale
-                if (
-                    self.do_classifier_free_guidance
-                    and self._cfg_truncation is not None
-                    and float(self._cfg_truncation) <= 1
-                ):
-                    if t_norm > self._cfg_truncation:
-                        current_guidance_scale = 0.0
-
-                # Run CFG only if configured AND scale is non-zero
-                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
-
-                if apply_cfg:
-                    latents_typed = latents.to(self.transformer.dtype)
-                    latent_model_input = latents_typed.repeat(2, 1, 1, 1)
-                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
-                    timestep_model_input = timestep.repeat(2)
-                else:
-                    latent_model_input = latents.to(self.transformer.dtype)
-                    prompt_embeds_model_input = prompt_embeds
-                    timestep_model_input = timestep
-
-                latent_model_input = latent_model_input.unsqueeze(2)
-                latent_model_input_list = list(latent_model_input.unbind(dim=0))
-
-                model_out_list = self.transformer(
-                    latent_model_input_list,
-                    timestep_model_input,
-                    prompt_embeds_model_input,
-                )[0]
-
-                if apply_cfg:
-                    # Perform CFG
-                    pos_out = model_out_list[:actual_batch_size]
-                    neg_out = model_out_list[actual_batch_size:]
-
-                    noise_pred = []
-                    for j in range(actual_batch_size):
-                        pos = pos_out[j].float()
-                        neg = neg_out[j].float()
-
-                        pred = pos + current_guidance_scale * (pos - neg)
-
-                        # Renormalization
-                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
-                            ori_pos_norm = torch.linalg.vector_norm(pos)
-                            new_pos_norm = torch.linalg.vector_norm(pred)
-                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
-                            if new_pos_norm > max_new_norm:
-                                pred = pred * (max_new_norm / new_pos_norm)
-
-                        noise_pred.append(pred)
-
-                    noise_pred = torch.stack(noise_pred, dim=0)
-                else:
-                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
-
-                noise_pred = noise_pred.squeeze(2)
-                noise_pred = -noise_pred
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
-                assert latents.dtype == torch.float32
-
-                # start diff diff
-                image_latent = original_image_latents
-                latents_dtype = latents.dtype
-                if i < len(timesteps) - 1:
-                    noise_timestep = timesteps[i + 1]
-                    image_latent = self.scheduler.scale_noise(
-                        original_image_latents, torch.tensor([noise_timestep]), noise
-                    )
-
-                    mask = masks[i].to(latents_dtype)
-                    latents = image_latent * mask + latents * (1 - mask)
-                # end diff diff
-
-                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)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if output_type == "latent":
-            image = latents
-
-        else:
-            latents = latents.to(self.vae.dtype)
-            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-
-            image = self.vae.decode(latents, return_dict=False)[0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return ZImagePipelineOutput(images=image)

scripts/convert_cosmos_to_diffusers.py

@@ -1,94 +1,11 @@
-"""
-# Cosmos 2 Predict
-
-Download checkpoint
-```bash
-hf download nvidia/Cosmos-Predict2-2B-Text2Image
-```
-
-convert checkpoint
-```bash
-transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2-2B-Text2Image/snapshots/acdb5fde992a73ef0355f287977d002cbfd127e0/model.pt
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --transformer_type Cosmos-2.0-Diffusion-2B-Text2Image \
-    --text_encoder_path google-t5/t5-11b \
-    --tokenizer_path google-t5/t5-11b \
-    --vae_type wan2.1 \
-    --output_path converted/cosmos-p2-t2i-2b \
-    --save_pipeline
-```
-
-# Cosmos 2.5 Predict
-
-Download checkpoint
-```bash
-hf download nvidia/Cosmos-Predict2.5-2B
-```
-
-Convert checkpoint
-```bash
-# pre-trained
-transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/pre-trained/d20b7120-df3e-4911-919d-db6e08bad31c_ema_bf16.pt
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Predict-Base-2B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/2b/d20b7120-df3e-4911-919d-db6e08bad31c \
-    --save_pipeline
-
-# post-trained
-transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/post-trained/81edfebe-bd6a-4039-8c1d-737df1a790bf_ema_bf16.pt
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Predict-Base-2B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/2b/81edfebe-bd6a-4039-8c1d-737df1a790bf \
-    --save_pipeline
-```
-
-## 14B
-
-```bash
-hf download nvidia/Cosmos-Predict2.5-14B
-```
-
-```bash
-# pre-trained
-transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/pre-trained/54937b8c-29de-4f04-862c-e67b04ec41e8_ema_bf16.pt
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Predict-Base-14B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/14b/54937b8c-29de-4f04-862c-e67b04ec41e8/ \
-    --save_pipeline
-
-# post-trained
-transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/post-trained/e21d2a49-4747-44c8-ba44-9f6f9243715f_ema_bf16.pt
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Predict-Base-14B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/14b/e21d2a49-4747-44c8-ba44-9f6f9243715f/ \
-    --save_pipeline
-```
-
-"""
-
 import argparse
 import pathlib
-import sys
 from typing import Any, Dict
 
 import torch
 from accelerate import init_empty_weights
 from huggingface_hub import snapshot_download
-from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration, T5EncoderModel, T5TokenizerFast
+from transformers import T5EncoderModel, T5TokenizerFast
 
 from diffusers import (
     AutoencoderKLCosmos,
@@ -100,9 +17,7 @@ from diffusers import (
     CosmosVideoToWorldPipeline,
     EDMEulerScheduler,
     FlowMatchEulerDiscreteScheduler,
-    UniPCMultistepScheduler,
 )
-from diffusers.pipelines.cosmos.pipeline_cosmos2_5_predict import Cosmos2_5_PredictBasePipeline
 
 
 def remove_keys_(key: str, state_dict: Dict[str, Any]):
@@ -318,44 +233,6 @@ TRANSFORMER_CONFIGS = {
         "concat_padding_mask": True,
         "extra_pos_embed_type": None,
     },
-    "Cosmos-2.5-Predict-Base-2B": {
-        "in_channels": 16 + 1,
-        "out_channels": 16,
-        "num_attention_heads": 16,
-        "attention_head_dim": 128,
-        "num_layers": 28,
-        "mlp_ratio": 4.0,
-        "text_embed_dim": 1024,
-        "adaln_lora_dim": 256,
-        "max_size": (128, 240, 240),
-        "patch_size": (1, 2, 2),
-        "rope_scale": (1.0, 3.0, 3.0),
-        "concat_padding_mask": True,
-        # NOTE: source config has pos_emb_learnable: 'True' - but params are missing
-        "extra_pos_embed_type": None,
-        "use_crossattn_projection": True,
-        "crossattn_proj_in_channels": 100352,
-        "encoder_hidden_states_channels": 1024,
-    },
-    "Cosmos-2.5-Predict-Base-14B": {
-        "in_channels": 16 + 1,
-        "out_channels": 16,
-        "num_attention_heads": 40,
-        "attention_head_dim": 128,
-        "num_layers": 36,
-        "mlp_ratio": 4.0,
-        "text_embed_dim": 1024,
-        "adaln_lora_dim": 256,
-        "max_size": (128, 240, 240),
-        "patch_size": (1, 2, 2),
-        "rope_scale": (1.0, 3.0, 3.0),
-        "concat_padding_mask": True,
-        # NOTE: source config has pos_emb_learnable: 'True' - but params are missing
-        "extra_pos_embed_type": None,
-        "use_crossattn_projection": True,
-        "crossattn_proj_in_channels": 100352,
-        "encoder_hidden_states_channels": 1024,
-    },
 }
 
 VAE_KEYS_RENAME_DICT = {
@@ -457,9 +334,6 @@ def convert_transformer(transformer_type: str, ckpt_path: str, weights_only: boo
     elif "Cosmos-2.0" in transformer_type:
         TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0
         TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0
-    elif "Cosmos-2.5" in transformer_type:
-        TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0
-        TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0
     else:
         assert False
 
@@ -473,7 +347,6 @@ def convert_transformer(transformer_type: str, ckpt_path: str, weights_only: boo
             new_key = new_key.removeprefix(PREFIX_KEY)
         for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
             new_key = new_key.replace(replace_key, rename_key)
-        print(key, "->", new_key, flush=True)
         update_state_dict_(original_state_dict, key, new_key)
 
     for key in list(original_state_dict.keys()):
@@ -482,21 +355,6 @@ def convert_transformer(transformer_type: str, ckpt_path: str, weights_only: boo
                 continue
             handler_fn_inplace(key, original_state_dict)
 
-    expected_keys = set(transformer.state_dict().keys())
-    mapped_keys = set(original_state_dict.keys())
-    missing_keys = expected_keys - mapped_keys
-    unexpected_keys = mapped_keys - expected_keys
-    if missing_keys:
-        print(f"ERROR: missing keys ({len(missing_keys)} from state_dict:", flush=True, file=sys.stderr)
-        for k in missing_keys:
-            print(k)
-        sys.exit(1)
-    if unexpected_keys:
-        print(f"ERROR: unexpected keys ({len(unexpected_keys)}) from state_dict:", flush=True, file=sys.stderr)
-        for k in unexpected_keys:
-            print(k)
-        sys.exit(2)
-
     transformer.load_state_dict(original_state_dict, strict=True, assign=True)
     return transformer
 
@@ -586,34 +444,6 @@ def save_pipeline_cosmos_2_0(args, transformer, vae):
     pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
 
 
-def save_pipeline_cosmos2_5(args, transformer, vae):
-    text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B"
-    tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct"
-
-    text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-        text_encoder_path, torch_dtype="auto", device_map="cpu"
-    )
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-
-    scheduler = UniPCMultistepScheduler(
-        use_karras_sigmas=True,
-        use_flow_sigmas=True,
-        prediction_type="flow_prediction",
-        sigma_max=200.0,
-        sigma_min=0.01,
-    )
-
-    pipe = Cosmos2_5_PredictBasePipeline(
-        text_encoder=text_encoder,
-        tokenizer=tokenizer,
-        transformer=transformer,
-        vae=vae,
-        scheduler=scheduler,
-        safety_checker=lambda *args, **kwargs: None,
-    )
-    pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
-
-
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--transformer_type", type=str, default=None, choices=list(TRANSFORMER_CONFIGS.keys()))
@@ -621,10 +451,10 @@ def get_args():
         "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
     )
     parser.add_argument(
-        "--vae_type", type=str, default="wan2.1", choices=["wan2.1", *list(VAE_CONFIGS.keys())], help="Type of VAE"
+        "--vae_type", type=str, default=None, choices=["none", *list(VAE_CONFIGS.keys())], help="Type of VAE"
     )
-    parser.add_argument("--text_encoder_path", type=str, default=None)
-    parser.add_argument("--tokenizer_path", type=str, default=None)
+    parser.add_argument("--text_encoder_path", type=str, default="google-t5/t5-11b")
+    parser.add_argument("--tokenizer_path", type=str, default="google-t5/t5-11b")
     parser.add_argument("--save_pipeline", action="store_true")
     parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
     parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.")
@@ -647,6 +477,8 @@ if __name__ == "__main__":
     if args.save_pipeline:
         assert args.transformer_ckpt_path is not None
         assert args.vae_type is not None
+        assert args.text_encoder_path is not None
+        assert args.tokenizer_path is not None
 
     if args.transformer_ckpt_path is not None:
         weights_only = "Cosmos-1.0" in args.transformer_type
@@ -658,26 +490,17 @@ if __name__ == "__main__":
     if args.vae_type is not None:
         if "Cosmos-1.0" in args.transformer_type:
             vae = convert_vae(args.vae_type)
-        elif "Cosmos-2.0" in args.transformer_type or "Cosmos-2.5" in args.transformer_type:
+        else:
             vae = AutoencoderKLWan.from_pretrained(
                 "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
             )
-        else:
-            raise AssertionError(f"{args.transformer_type} not supported")
-
         if not args.save_pipeline:
             vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
 
     if args.save_pipeline:
         if "Cosmos-1.0" in args.transformer_type:
-            assert args.text_encoder_path is not None
-            assert args.tokenizer_path is not None
             save_pipeline_cosmos_1_0(args, transformer, vae)
         elif "Cosmos-2.0" in args.transformer_type:
-            assert args.text_encoder_path is not None
-            assert args.tokenizer_path is not None
             save_pipeline_cosmos_2_0(args, transformer, vae)
-        elif "Cosmos-2.5" in args.transformer_type:
-            save_pipeline_cosmos2_5(args, transformer, vae)
         else:
-            raise AssertionError(f"{args.transformer_type} not supported")
+            assert False

setup.py

@@ -274,7 +274,7 @@ version_range_max = max(sys.version_info[1], 10) + 1
 
 setup(
     name="diffusers",
-    version="0.37.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.36.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -463,7 +463,6 @@ else:
             "CogView4ControlPipeline",
             "CogView4Pipeline",
             "ConsisIDPipeline",
-            "Cosmos2_5_PredictBasePipeline",
             "Cosmos2TextToImagePipeline",
             "Cosmos2VideoToWorldPipeline",
             "CosmosTextToWorldPipeline",
@@ -675,7 +674,6 @@ else:
             "ZImageControlNetInpaintPipeline",
             "ZImageControlNetPipeline",
             "ZImageImg2ImgPipeline",
-            "ZImageOmniPipeline",
             "ZImagePipeline",
         ]
     )
@@ -1177,7 +1175,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             CogView4ControlPipeline,
             CogView4Pipeline,
             ConsisIDPipeline,
-            Cosmos2_5_PredictBasePipeline,
             Cosmos2TextToImagePipeline,
             Cosmos2VideoToWorldPipeline,
             CosmosTextToWorldPipeline,
@@ -1387,7 +1384,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ZImageControlNetInpaintPipeline,
             ZImageControlNetPipeline,
             ZImageImg2ImgPipeline,
-            ZImageOmniPipeline,
             ZImagePipeline,
         )
 

src/diffusers/guiders/__init__.py

@@ -25,7 +25,6 @@ if is_torch_available():
     from .classifier_free_zero_star_guidance import ClassifierFreeZeroStarGuidance
     from .frequency_decoupled_guidance import FrequencyDecoupledGuidance
     from .guider_utils import BaseGuidance
-    from .magnitude_aware_guidance import MagnitudeAwareGuidance
     from .perturbed_attention_guidance import PerturbedAttentionGuidance
     from .skip_layer_guidance import SkipLayerGuidance
     from .smoothed_energy_guidance import SmoothedEnergyGuidance

src/diffusers/guiders/magnitude_aware_guidance.py

@@ -1,159 +0,0 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
-
-import torch
-
-from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
-
-
-if TYPE_CHECKING:
-    from ..modular_pipelines.modular_pipeline import BlockState
-
-
-class MagnitudeAwareGuidance(BaseGuidance):
-    """
-    Magnitude-Aware Mitigation for Boosted Guidance (MAMBO-G): https://huggingface.co/papers/2508.03442
-
-    Args:
-        guidance_scale (`float`, defaults to `10.0`):
-            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
-            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
-            deterioration of image quality.
-        alpha (`float`, defaults to `8.0`):
-            The alpha parameter for the magnitude-aware guidance. Higher values cause more aggressive supression of
-            guidance scale when the magnitude of the guidance update is large.
-        guidance_rescale (`float`, defaults to `0.0`):
-            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
-            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
-            Flawed](https://huggingface.co/papers/2305.08891).
-        use_original_formulation (`bool`, defaults to `False`):
-            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
-            we use the diffusers-native implementation that has been in the codebase for a long time. See
-            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
-        start (`float`, defaults to `0.0`):
-            The fraction of the total number of denoising steps after which guidance starts.
-        stop (`float`, defaults to `1.0`):
-            The fraction of the total number of denoising steps after which guidance stops.
-    """
-
-    _input_predictions = ["pred_cond", "pred_uncond"]
-
-    @register_to_config
-    def __init__(
-        self,
-        guidance_scale: float = 10.0,
-        alpha: float = 8.0,
-        guidance_rescale: float = 0.0,
-        use_original_formulation: bool = False,
-        start: float = 0.0,
-        stop: float = 1.0,
-        enabled: bool = True,
-    ):
-        super().__init__(start, stop, enabled)
-
-        self.guidance_scale = guidance_scale
-        self.alpha = alpha
-        self.guidance_rescale = guidance_rescale
-        self.use_original_formulation = use_original_formulation
-
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
-        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
-        data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
-    ) -> List["BlockState"]:
-        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
-        data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch_from_block_state(input_fields, data, tuple_idx, input_prediction)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
-        pred = None
-
-        if not self._is_mambo_g_enabled():
-            pred = pred_cond
-        else:
-            pred = mambo_guidance(
-                pred_cond,
-                pred_uncond,
-                self.guidance_scale,
-                self.alpha,
-                self.use_original_formulation,
-            )
-
-        if self.guidance_rescale > 0.0:
-            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
-
-        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
-
-    @property
-    def is_conditional(self) -> bool:
-        return self._count_prepared == 1
-
-    @property
-    def num_conditions(self) -> int:
-        num_conditions = 1
-        if self._is_mambo_g_enabled():
-            num_conditions += 1
-        return num_conditions
-
-    def _is_mambo_g_enabled(self) -> bool:
-        if not self._enabled:
-            return False
-
-        is_within_range = True
-        if self._num_inference_steps is not None:
-            skip_start_step = int(self._start * self._num_inference_steps)
-            skip_stop_step = int(self._stop * self._num_inference_steps)
-            is_within_range = skip_start_step <= self._step < skip_stop_step
-
-        is_close = False
-        if self.use_original_formulation:
-            is_close = math.isclose(self.guidance_scale, 0.0)
-        else:
-            is_close = math.isclose(self.guidance_scale, 1.0)
-
-        return is_within_range and not is_close
-
-
-def mambo_guidance(
-    pred_cond: torch.Tensor,
-    pred_uncond: torch.Tensor,
-    guidance_scale: float,
-    alpha: float = 8.0,
-    use_original_formulation: bool = False,
-):
-    dim = list(range(1, len(pred_cond.shape)))
-    diff = pred_cond - pred_uncond
-    ratio = torch.norm(diff, dim=dim, keepdim=True) / torch.norm(pred_uncond, dim=dim, keepdim=True)
-    guidance_scale_final = (
-        guidance_scale * torch.exp(-alpha * ratio)
-        if use_original_formulation
-        else 1.0 + (guidance_scale - 1.0) * torch.exp(-alpha * ratio)
-    )
-    pred = pred_cond if use_original_formulation else pred_uncond
-    pred = pred + guidance_scale_final * diff
-
-    return pred

src/diffusers/loaders/single_file_model.py

@@ -162,7 +162,7 @@ SINGLE_FILE_LOADABLE_CLASSES = {
         "default_subfolder": "transformer",
     },
     "QwenImageTransformer2DModel": {
-        "checkpoint_mapping_fn": lambda checkpoint, **kwargs: checkpoint,
+        "checkpoint_mapping_fn": lambda x: x,
         "default_subfolder": "transformer",
     },
     "Flux2Transformer2DModel": {

src/diffusers/loaders/single_file_utils.py

@@ -120,10 +120,7 @@ CHECKPOINT_KEY_NAMES = {
     "hunyuan-video": "txt_in.individual_token_refiner.blocks.0.adaLN_modulation.1.bias",
     "instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
     "lumina2": ["model.diffusion_model.cap_embedder.0.weight", "cap_embedder.0.weight"],
-    "z-image-turbo": [
-        "model.diffusion_model.layers.0.adaLN_modulation.0.weight",
-        "layers.0.adaLN_modulation.0.weight",
-    ],
+    "z-image-turbo": "cap_embedder.0.weight",
     "z-image-turbo-controlnet": "control_all_x_embedder.2-1.weight",
     "z-image-turbo-controlnet-2.x": "control_layers.14.adaLN_modulation.0.weight",
     "sana": [
@@ -226,8 +223,7 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "cosmos-2.0-v2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Video2World"},
     "z-image-turbo": {"pretrained_model_name_or_path": "Tongyi-MAI/Z-Image-Turbo"},
     "z-image-turbo-controlnet": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union"},
-    "z-image-turbo-controlnet-2.0": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0"},
-    "z-image-turbo-controlnet-2.1": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1"},
+    "z-image-turbo-controlnet-2.x": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1"},
 }
 
 # Use to configure model sample size when original config is provided
@@ -731,7 +727,10 @@ def infer_diffusers_model_type(checkpoint):
     ):
         model_type = "instruct-pix2pix"
 
-    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["z-image-turbo"]):
+    elif (
+        CHECKPOINT_KEY_NAMES["z-image-turbo"] in checkpoint
+        and checkpoint[CHECKPOINT_KEY_NAMES["z-image-turbo"]].shape[0] == 2560
+    ):
         model_type = "z-image-turbo"
 
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["lumina2"]):
@@ -785,13 +784,7 @@ def infer_diffusers_model_type(checkpoint):
             raise ValueError(f"Unexpected x_embedder shape: {x_embedder_shape} when loading Cosmos 2.0 model.")
 
     elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet-2.x"] in checkpoint:
-        before_proj_weight = checkpoint.get("control_noise_refiner.0.before_proj.weight", None)
-        if before_proj_weight is None:
-            model_type = "z-image-turbo-controlnet-2.0"
-        elif before_proj_weight is not None and torch.all(before_proj_weight == 0.0):
-            model_type = "z-image-turbo-controlnet-2.0"
-        else:
-            model_type = "z-image-turbo-controlnet-2.1"
+        model_type = "z-image-turbo-controlnet-2.x"
 
     elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet"] in checkpoint:
         model_type = "z-image-turbo-controlnet"
@@ -3859,7 +3852,6 @@ def convert_z_image_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
         ".attention.k_norm.weight": ".attention.norm_k.weight",
         ".attention.q_norm.weight": ".attention.norm_q.weight",
         ".attention.out.weight": ".attention.to_out.0.weight",
-        "model.diffusion_model.": "",
     }
 
     def convert_z_image_fused_attention(key: str, state_dict: dict[str, object]) -> None:
@@ -3894,9 +3886,6 @@ def convert_z_image_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
 
         update_state_dict(converted_state_dict, key, new_key)
 
-    if "norm_final.weight" in converted_state_dict.keys():
-        _ = converted_state_dict.pop("norm_final.weight")
-
     # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
     # special_keys_remap
     for key in list(converted_state_dict.keys()):

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py

@@ -27,7 +27,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -410,7 +410,7 @@ class HunyuanImageDecoder2D(nn.Module):
         return h
 
 
-class AutoencoderKLHunyuanImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLHunyuanImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model for 2D images with spatial tiling support.
 
@@ -486,6 +486,27 @@ class AutoencoderKLHunyuanImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromO
         self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
         self.tile_latent_min_size = self.tile_sample_min_size // self.config.spatial_compression_ratio
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        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.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor):
 
         batch_size, num_channels, height, width = x.shape

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage_refiner.py

@@ -26,7 +26,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -584,7 +584,7 @@ class HunyuanImageRefinerDecoder3D(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLHunyuanImageRefiner(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLHunyuanImageRefiner(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
     HunyuanImage-2.1 Refiner.
@@ -685,6 +685,27 @@ class AutoencoderKLHunyuanImageRefiner(ModelMixin, AutoencoderMixin, ConfigMixin
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        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.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         _, _, _, height, width = x.shape
 

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanvideo15.py

@@ -26,7 +26,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -625,7 +625,7 @@ class HunyuanVideo15Decoder3D(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLHunyuanVideo15(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLHunyuanVideo15(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
     HunyuanVideo-1.5.
@@ -723,6 +723,27 @@ class AutoencoderKLHunyuanVideo15(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_latent_min_width = tile_latent_min_width or self.tile_latent_min_width
         self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        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.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         _, _, _, height, width = x.shape
 

src/diffusers/models/controlnets/controlnet_z_image.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import List, Literal, Optional, Tuple
+from typing import List, Literal, Optional
 
 import torch
 import torch.nn as nn
@@ -170,21 +170,6 @@ class FeedForward(nn.Module):
         return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
 
 
-# Copied from diffusers.models.transformers.transformer_z_image.select_per_token
-def select_per_token(
-    value_noisy: torch.Tensor,
-    value_clean: torch.Tensor,
-    noise_mask: torch.Tensor,
-    seq_len: int,
-) -> torch.Tensor:
-    noise_mask_expanded = noise_mask.unsqueeze(-1)  # (batch, seq_len, 1)
-    return torch.where(
-        noise_mask_expanded == 1,
-        value_noisy.unsqueeze(1).expand(-1, seq_len, -1),
-        value_clean.unsqueeze(1).expand(-1, seq_len, -1),
-    )
-
-
 @maybe_allow_in_graph
 # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformerBlock
 class ZImageTransformerBlock(nn.Module):
@@ -235,37 +220,12 @@ class ZImageTransformerBlock(nn.Module):
         attn_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor] = None,
-        noise_mask: Optional[torch.Tensor] = None,
-        adaln_noisy: Optional[torch.Tensor] = None,
-        adaln_clean: Optional[torch.Tensor] = None,
     ):
         if self.modulation:
-            seq_len = x.shape[1]
-
-            if noise_mask is not None:
-                # Per-token modulation: different modulation for noisy/clean tokens
-                mod_noisy = self.adaLN_modulation(adaln_noisy)
-                mod_clean = self.adaLN_modulation(adaln_clean)
-
-                scale_msa_noisy, gate_msa_noisy, scale_mlp_noisy, gate_mlp_noisy = mod_noisy.chunk(4, dim=1)
-                scale_msa_clean, gate_msa_clean, scale_mlp_clean, gate_mlp_clean = mod_clean.chunk(4, dim=1)
-
-                gate_msa_noisy, gate_mlp_noisy = gate_msa_noisy.tanh(), gate_mlp_noisy.tanh()
-                gate_msa_clean, gate_mlp_clean = gate_msa_clean.tanh(), gate_mlp_clean.tanh()
-
-                scale_msa_noisy, scale_mlp_noisy = 1.0 + scale_msa_noisy, 1.0 + scale_mlp_noisy
-                scale_msa_clean, scale_mlp_clean = 1.0 + scale_msa_clean, 1.0 + scale_mlp_clean
-
-                scale_msa = select_per_token(scale_msa_noisy, scale_msa_clean, noise_mask, seq_len)
-                scale_mlp = select_per_token(scale_mlp_noisy, scale_mlp_clean, noise_mask, seq_len)
-                gate_msa = select_per_token(gate_msa_noisy, gate_msa_clean, noise_mask, seq_len)
-                gate_mlp = select_per_token(gate_mlp_noisy, gate_mlp_clean, noise_mask, seq_len)
-            else:
-                # Global modulation: same modulation for all tokens (avoid double select)
-                mod = self.adaLN_modulation(adaln_input)
-                scale_msa, gate_msa, scale_mlp, gate_mlp = mod.unsqueeze(1).chunk(4, dim=2)
-                gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
-                scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
+            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
             # Attention block
             attn_out = self.attention(
@@ -533,93 +493,112 @@ class ZImageControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
     def create_coordinate_grid(size, start=None, device=None):
         if start is None:
             start = (0 for _ in size)
+
         axes = [torch.arange(x0, x0 + span, dtype=torch.int32, device=device) for x0, span in zip(start, size)]
         grids = torch.meshgrid(axes, indexing="ij")
         return torch.stack(grids, dim=-1)
 
-    # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformer2DModel._patchify_image
-    def _patchify_image(self, image: torch.Tensor, patch_size: int, f_patch_size: int):
-        """Patchify a single image tensor: (C, F, H, W) -> (num_patches, patch_dim)."""
-        pH, pW, pF = patch_size, patch_size, f_patch_size
-        C, F, H, W = image.size()
-        F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
-        image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
-        image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
-        return image, (F, H, W), (F_tokens, H_tokens, W_tokens)
-
-    # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformer2DModel._pad_with_ids
-    def _pad_with_ids(
-        self,
-        feat: torch.Tensor,
-        pos_grid_size: Tuple,
-        pos_start: Tuple,
-        device: torch.device,
-        noise_mask_val: Optional[int] = None,
-    ):
-        """Pad feature to SEQ_MULTI_OF, create position IDs and pad mask."""
-        ori_len = len(feat)
-        pad_len = (-ori_len) % SEQ_MULTI_OF
-        total_len = ori_len + pad_len
-
-        # Pos IDs
-        ori_pos_ids = self.create_coordinate_grid(size=pos_grid_size, start=pos_start, device=device).flatten(0, 2)
-        if pad_len > 0:
-            pad_pos_ids = (
-                self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
-                .flatten(0, 2)
-                .repeat(pad_len, 1)
-            )
-            pos_ids = torch.cat([ori_pos_ids, pad_pos_ids], dim=0)
-            padded_feat = torch.cat([feat, feat[-1:].repeat(pad_len, 1)], dim=0)
-            pad_mask = torch.cat(
-                [
-                    torch.zeros(ori_len, dtype=torch.bool, device=device),
-                    torch.ones(pad_len, dtype=torch.bool, device=device),
-                ]
-            )
-        else:
-            pos_ids = ori_pos_ids
-            padded_feat = feat
-            pad_mask = torch.zeros(ori_len, dtype=torch.bool, device=device)
-
-        noise_mask = [noise_mask_val] * total_len if noise_mask_val is not None else None  # token level
-        return padded_feat, pos_ids, pad_mask, total_len, noise_mask
-
     # Copied from diffusers.models.transformers.transformer_z_image.ZImageTransformer2DModel.patchify_and_embed
     def patchify_and_embed(
-        self, all_image: List[torch.Tensor], all_cap_feats: List[torch.Tensor], patch_size: int, f_patch_size: int
+        self,
+        all_image: List[torch.Tensor],
+        all_cap_feats: List[torch.Tensor],
+        patch_size: int,
+        f_patch_size: int,
     ):
-        """Patchify for basic mode: single image per batch item."""
+        pH = pW = patch_size
+        pF = f_patch_size
         device = all_image[0].device
-        all_img_out, all_img_size, all_img_pos_ids, all_img_pad_mask = [], [], [], []
-        all_cap_out, all_cap_pos_ids, all_cap_pad_mask = [], [], []
 
-        for image, cap_feat in zip(all_image, all_cap_feats):
-            # Caption
-            cap_out, cap_pos_ids, cap_pad_mask, cap_len, _ = self._pad_with_ids(
-                cap_feat, (len(cap_feat) + (-len(cap_feat)) % SEQ_MULTI_OF, 1, 1), (1, 0, 0), device
-            )
-            all_cap_out.append(cap_out)
-            all_cap_pos_ids.append(cap_pos_ids)
-            all_cap_pad_mask.append(cap_pad_mask)
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+        all_cap_pos_ids = []
+        all_cap_pad_mask = []
+        all_cap_feats_out = []
 
-            # Image
-            img_patches, size, (F_t, H_t, W_t) = self._patchify_image(image, patch_size, f_patch_size)
-            img_out, img_pos_ids, img_pad_mask, _, _ = self._pad_with_ids(
-                img_patches, (F_t, H_t, W_t), (cap_len + 1, 0, 0), device
+        for i, (image, cap_feat) in enumerate(zip(all_image, all_cap_feats)):
+            ### Process Caption
+            cap_ori_len = len(cap_feat)
+            cap_padding_len = (-cap_ori_len) % SEQ_MULTI_OF
+            # padded position ids
+            cap_padded_pos_ids = self.create_coordinate_grid(
+                size=(cap_ori_len + cap_padding_len, 1, 1),
+                start=(1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            all_cap_pos_ids.append(cap_padded_pos_ids)
+            # pad mask
+            cap_pad_mask = torch.cat(
+                [
+                    torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
             )
-            all_img_out.append(img_out)
-            all_img_size.append(size)
-            all_img_pos_ids.append(img_pos_ids)
-            all_img_pad_mask.append(img_pad_mask)
+            all_cap_pad_mask.append(
+                cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
+            )
+
+            # padded feature
+            cap_padded_feat = torch.cat([cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)], dim=0)
+            all_cap_feats_out.append(cap_padded_feat)
+
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
+
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_ori_len + cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padded_pos_ids = torch.cat(
+                [
+                    image_ori_pos_ids,
+                    self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                    .flatten(0, 2)
+                    .repeat(image_padding_len, 1),
+                ],
+                dim=0,
+            )
+            all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
+            # pad mask
+            image_pad_mask = torch.cat(
+                [
+                    torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_image_pad_mask.append(
+                image_pad_mask
+                if image_padding_len > 0
+                else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
+            )
+            # padded feature
+            image_padded_feat = torch.cat(
+                [image, image[-1:].repeat(image_padding_len, 1)],
+                dim=0,
+            )
+            all_image_out.append(image_padded_feat if image_padding_len > 0 else image)
 
         return (
-            all_img_out,
-            all_cap_out,
-            all_img_size,
-            all_img_pos_ids,
+            all_image_out,
+            all_cap_feats_out,
+            all_image_size,
+            all_image_pos_ids,
             all_cap_pos_ids,
-            all_img_pad_mask,
+            all_image_pad_mask,
             all_cap_pad_mask,
         )
 

src/diffusers/models/transformers/transformer_cosmos.py

@@ -439,9 +439,6 @@ class CosmosTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         rope_scale: Tuple[float, float, float] = (2.0, 1.0, 1.0),
         concat_padding_mask: bool = True,
         extra_pos_embed_type: Optional[str] = "learnable",
-        use_crossattn_projection: bool = False,
-        crossattn_proj_in_channels: int = 1024,
-        encoder_hidden_states_channels: int = 1024,
     ) -> None:
         super().__init__()
         hidden_size = num_attention_heads * attention_head_dim
@@ -488,12 +485,6 @@ class CosmosTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels, bias=False
         )
 
-        if self.config.use_crossattn_projection:
-            self.crossattn_proj = nn.Sequential(
-                nn.Linear(crossattn_proj_in_channels, encoder_hidden_states_channels, bias=True),
-                nn.GELU(),
-            )
-
         self.gradient_checkpointing = False
 
     def forward(
@@ -533,7 +524,6 @@ class CosmosTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         post_patch_num_frames = num_frames // p_t
         post_patch_height = height // p_h
         post_patch_width = width // p_w
-
         hidden_states = self.patch_embed(hidden_states)
         hidden_states = hidden_states.flatten(1, 3)  # [B, T, H, W, C] -> [B, THW, C]
 
@@ -556,9 +546,6 @@ class CosmosTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         else:
             assert False
 
-        if self.config.use_crossattn_projection:
-            encoder_hidden_states = self.crossattn_proj(encoder_hidden_states)
-
         # 5. Transformer blocks
         for block in self.transformer_blocks:
             if torch.is_grad_enabled() and self.gradient_checkpointing:

src/diffusers/models/transformers/transformer_wan.py

@@ -134,8 +134,7 @@ class WanAttnProcessor:
                 dropout_p=0.0,
                 is_causal=False,
                 backend=self._attention_backend,
-                # Reference: https://github.com/huggingface/diffusers/pull/12909
-                parallel_config=None,
+                parallel_config=self._parallel_config,
             )
             hidden_states_img = hidden_states_img.flatten(2, 3)
             hidden_states_img = hidden_states_img.type_as(query)
@@ -148,8 +147,7 @@ class WanAttnProcessor:
             dropout_p=0.0,
             is_causal=False,
             backend=self._attention_backend,
-            # Reference: https://github.com/huggingface/diffusers/pull/12909
-            parallel_config=(self._parallel_config if encoder_hidden_states is None else None),
+            parallel_config=self._parallel_config,
         )
         hidden_states = hidden_states.flatten(2, 3)
         hidden_states = hidden_states.type_as(query)
@@ -554,11 +552,9 @@ class WanTransformer3DModel(
         "blocks.0": {
             "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
         },
-        # Reference: https://github.com/huggingface/diffusers/pull/12909
-        # We need to disable the splitting of encoder_hidden_states because the image_encoder
-        # (Wan 2.1 I2V) consistently generates 257 tokens for image_embed. This causes the shape
-        # of encoder_hidden_states—whose token count is always 769 (512 + 257) after concatenation
-        # —to be indivisible by the number of devices in the CP.
+        "blocks.*": {
+            "encoder_hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
+        },
         "proj_out": ContextParallelOutput(gather_dim=1, expected_dims=3),
         "": {
             "timestep": ContextParallelInput(split_dim=1, expected_dims=2, split_output=False),

src/diffusers/models/transformers/transformer_wan_animate.py

@@ -609,8 +609,7 @@ class WanAttnProcessor:
                 dropout_p=0.0,
                 is_causal=False,
                 backend=self._attention_backend,
-                # Reference: https://github.com/huggingface/diffusers/pull/12909
-                parallel_config=None,
+                parallel_config=self._parallel_config,
             )
             hidden_states_img = hidden_states_img.flatten(2, 3)
             hidden_states_img = hidden_states_img.type_as(query)
@@ -623,8 +622,7 @@ class WanAttnProcessor:
             dropout_p=0.0,
             is_causal=False,
             backend=self._attention_backend,
-            # Reference: https://github.com/huggingface/diffusers/pull/12909
-            parallel_config=(self._parallel_config if encoder_hidden_states is None else None),
+            parallel_config=self._parallel_config,
         )
         hidden_states = hidden_states.flatten(2, 3)
         hidden_states = hidden_states.type_as(query)

src/diffusers/models/transformers/transformer_z_image.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -32,7 +32,6 @@ from ..modeling_outputs import Transformer2DModelOutput
 
 ADALN_EMBED_DIM = 256
 SEQ_MULTI_OF = 32
-X_PAD_DIM = 64
 
 
 class TimestepEmbedder(nn.Module):
@@ -153,20 +152,6 @@ class ZSingleStreamAttnProcessor:
         return output
 
 
-def select_per_token(
-    value_noisy: torch.Tensor,
-    value_clean: torch.Tensor,
-    noise_mask: torch.Tensor,
-    seq_len: int,
-) -> torch.Tensor:
-    noise_mask_expanded = noise_mask.unsqueeze(-1)  # (batch, seq_len, 1)
-    return torch.where(
-        noise_mask_expanded == 1,
-        value_noisy.unsqueeze(1).expand(-1, seq_len, -1),
-        value_clean.unsqueeze(1).expand(-1, seq_len, -1),
-    )
-
-
 class FeedForward(nn.Module):
     def __init__(self, dim: int, hidden_dim: int):
         super().__init__()
@@ -230,37 +215,12 @@ class ZImageTransformerBlock(nn.Module):
         attn_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor] = None,
-        noise_mask: Optional[torch.Tensor] = None,
-        adaln_noisy: Optional[torch.Tensor] = None,
-        adaln_clean: Optional[torch.Tensor] = None,
     ):
         if self.modulation:
-            seq_len = x.shape[1]
-
-            if noise_mask is not None:
-                # Per-token modulation: different modulation for noisy/clean tokens
-                mod_noisy = self.adaLN_modulation(adaln_noisy)
-                mod_clean = self.adaLN_modulation(adaln_clean)
-
-                scale_msa_noisy, gate_msa_noisy, scale_mlp_noisy, gate_mlp_noisy = mod_noisy.chunk(4, dim=1)
-                scale_msa_clean, gate_msa_clean, scale_mlp_clean, gate_mlp_clean = mod_clean.chunk(4, dim=1)
-
-                gate_msa_noisy, gate_mlp_noisy = gate_msa_noisy.tanh(), gate_mlp_noisy.tanh()
-                gate_msa_clean, gate_mlp_clean = gate_msa_clean.tanh(), gate_mlp_clean.tanh()
-
-                scale_msa_noisy, scale_mlp_noisy = 1.0 + scale_msa_noisy, 1.0 + scale_mlp_noisy
-                scale_msa_clean, scale_mlp_clean = 1.0 + scale_msa_clean, 1.0 + scale_mlp_clean
-
-                scale_msa = select_per_token(scale_msa_noisy, scale_msa_clean, noise_mask, seq_len)
-                scale_mlp = select_per_token(scale_mlp_noisy, scale_mlp_clean, noise_mask, seq_len)
-                gate_msa = select_per_token(gate_msa_noisy, gate_msa_clean, noise_mask, seq_len)
-                gate_mlp = select_per_token(gate_mlp_noisy, gate_mlp_clean, noise_mask, seq_len)
-            else:
-                # Global modulation: same modulation for all tokens (avoid double select)
-                mod = self.adaLN_modulation(adaln_input)
-                scale_msa, gate_msa, scale_mlp, gate_mlp = mod.unsqueeze(1).chunk(4, dim=2)
-                gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
-                scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
+            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
             # Attention block
             attn_out = self.attention(
@@ -292,21 +252,9 @@ class FinalLayer(nn.Module):
             nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
         )
 
-    def forward(self, x, c=None, noise_mask=None, c_noisy=None, c_clean=None):
-        seq_len = x.shape[1]
-
-        if noise_mask is not None:
-            # Per-token modulation
-            scale_noisy = 1.0 + self.adaLN_modulation(c_noisy)
-            scale_clean = 1.0 + self.adaLN_modulation(c_clean)
-            scale = select_per_token(scale_noisy, scale_clean, noise_mask, seq_len)
-        else:
-            # Original global modulation
-            assert c is not None, "Either c or (c_noisy, c_clean) must be provided"
-            scale = 1.0 + self.adaLN_modulation(c)
-            scale = scale.unsqueeze(1)
-
-        x = self.norm_final(x) * scale
+    def forward(self, x, c):
+        scale = 1.0 + self.adaLN_modulation(c)
+        x = self.norm_final(x) * scale.unsqueeze(1)
         x = self.linear(x)
         return x
 
@@ -377,7 +325,6 @@ class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOr
         norm_eps=1e-5,
         qk_norm=True,
         cap_feat_dim=2560,
-        siglip_feat_dim=None,  # Optional: set to enable SigLIP support for Omni
         rope_theta=256.0,
         t_scale=1000.0,
         axes_dims=[32, 48, 48],
@@ -439,31 +386,6 @@ class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOr
         self.t_embedder = TimestepEmbedder(min(dim, ADALN_EMBED_DIM), mid_size=1024)
         self.cap_embedder = nn.Sequential(RMSNorm(cap_feat_dim, eps=norm_eps), nn.Linear(cap_feat_dim, dim, bias=True))
 
-        # Optional SigLIP components (for Omni variant)
-        if siglip_feat_dim is not None:
-            self.siglip_embedder = nn.Sequential(
-                RMSNorm(siglip_feat_dim, eps=norm_eps), nn.Linear(siglip_feat_dim, dim, bias=True)
-            )
-            self.siglip_refiner = nn.ModuleList(
-                [
-                    ZImageTransformerBlock(
-                        2000 + layer_id,
-                        dim,
-                        n_heads,
-                        n_kv_heads,
-                        norm_eps,
-                        qk_norm,
-                        modulation=False,
-                    )
-                    for layer_id in range(n_refiner_layers)
-                ]
-            )
-            self.siglip_pad_token = nn.Parameter(torch.empty((1, dim)))
-        else:
-            self.siglip_embedder = None
-            self.siglip_refiner = None
-            self.siglip_pad_token = None
-
         self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
         self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))
 
@@ -480,561 +402,259 @@ class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOr
 
         self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
 
-    def unpatchify(
-        self,
-        x: List[torch.Tensor],
-        size: List[Tuple],
-        patch_size,
-        f_patch_size,
-        x_pos_offsets: Optional[List[Tuple[int, int]]] = None,
-    ) -> List[torch.Tensor]:
+    def unpatchify(self, x: List[torch.Tensor], size: List[Tuple], patch_size, f_patch_size) -> List[torch.Tensor]:
         pH = pW = patch_size
         pF = f_patch_size
         bsz = len(x)
         assert len(size) == bsz
-
-        if x_pos_offsets is not None:
-            # Omni: extract target image from unified sequence (cond_images + target)
-            result = []
-            for i in range(bsz):
-                unified_x = x[i][x_pos_offsets[i][0] : x_pos_offsets[i][1]]
-                cu_len = 0
-                x_item = None
-                for j in range(len(size[i])):
-                    if size[i][j] is None:
-                        ori_len = 0
-                        pad_len = SEQ_MULTI_OF
-                        cu_len += pad_len + ori_len
-                    else:
-                        F, H, W = size[i][j]
-                        ori_len = (F // pF) * (H // pH) * (W // pW)
-                        pad_len = (-ori_len) % SEQ_MULTI_OF
-                        x_item = (
-                            unified_x[cu_len : cu_len + ori_len]
-                            .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
-                            .permute(6, 0, 3, 1, 4, 2, 5)
-                            .reshape(self.out_channels, F, H, W)
-                        )
-                        cu_len += ori_len + pad_len
-                result.append(x_item)  # Return only the last (target) image
-            return result
-        else:
-            # Original mode: simple unpatchify
-            for i in range(bsz):
-                F, H, W = size[i]
-                ori_len = (F // pF) * (H // pH) * (W // pW)
-                # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
-                x[i] = (
-                    x[i][:ori_len]
-                    .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
-                    .permute(6, 0, 3, 1, 4, 2, 5)
-                    .reshape(self.out_channels, F, H, W)
-                )
-            return x
+        for i in range(bsz):
+            F, H, W = size[i]
+            ori_len = (F // pF) * (H // pH) * (W // pW)
+            # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
+            x[i] = (
+                x[i][:ori_len]
+                .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                .permute(6, 0, 3, 1, 4, 2, 5)
+                .reshape(self.out_channels, F, H, W)
+            )
+        return x
 
     @staticmethod
     def create_coordinate_grid(size, start=None, device=None):
         if start is None:
             start = (0 for _ in size)
+
         axes = [torch.arange(x0, x0 + span, dtype=torch.int32, device=device) for x0, span in zip(start, size)]
         grids = torch.meshgrid(axes, indexing="ij")
         return torch.stack(grids, dim=-1)
 
-    def _patchify_image(self, image: torch.Tensor, patch_size: int, f_patch_size: int):
-        """Patchify a single image tensor: (C, F, H, W) -> (num_patches, patch_dim)."""
-        pH, pW, pF = patch_size, patch_size, f_patch_size
-        C, F, H, W = image.size()
-        F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
-        image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
-        image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
-        return image, (F, H, W), (F_tokens, H_tokens, W_tokens)
-
-    def _pad_with_ids(
-        self,
-        feat: torch.Tensor,
-        pos_grid_size: Tuple,
-        pos_start: Tuple,
-        device: torch.device,
-        noise_mask_val: Optional[int] = None,
-    ):
-        """Pad feature to SEQ_MULTI_OF, create position IDs and pad mask."""
-        ori_len = len(feat)
-        pad_len = (-ori_len) % SEQ_MULTI_OF
-        total_len = ori_len + pad_len
-
-        # Pos IDs
-        ori_pos_ids = self.create_coordinate_grid(size=pos_grid_size, start=pos_start, device=device).flatten(0, 2)
-        if pad_len > 0:
-            pad_pos_ids = (
-                self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
-                .flatten(0, 2)
-                .repeat(pad_len, 1)
-            )
-            pos_ids = torch.cat([ori_pos_ids, pad_pos_ids], dim=0)
-            padded_feat = torch.cat([feat, feat[-1:].repeat(pad_len, 1)], dim=0)
-            pad_mask = torch.cat(
-                [
-                    torch.zeros(ori_len, dtype=torch.bool, device=device),
-                    torch.ones(pad_len, dtype=torch.bool, device=device),
-                ]
-            )
-        else:
-            pos_ids = ori_pos_ids
-            padded_feat = feat
-            pad_mask = torch.zeros(ori_len, dtype=torch.bool, device=device)
-
-        noise_mask = [noise_mask_val] * total_len if noise_mask_val is not None else None  # token level
-        return padded_feat, pos_ids, pad_mask, total_len, noise_mask
-
     def patchify_and_embed(
-        self, all_image: List[torch.Tensor], all_cap_feats: List[torch.Tensor], patch_size: int, f_patch_size: int
-    ):
-        """Patchify for basic mode: single image per batch item."""
-        device = all_image[0].device
-        all_img_out, all_img_size, all_img_pos_ids, all_img_pad_mask = [], [], [], []
-        all_cap_out, all_cap_pos_ids, all_cap_pad_mask = [], [], []
-
-        for image, cap_feat in zip(all_image, all_cap_feats):
-            # Caption
-            cap_out, cap_pos_ids, cap_pad_mask, cap_len, _ = self._pad_with_ids(
-                cap_feat, (len(cap_feat) + (-len(cap_feat)) % SEQ_MULTI_OF, 1, 1), (1, 0, 0), device
-            )
-            all_cap_out.append(cap_out)
-            all_cap_pos_ids.append(cap_pos_ids)
-            all_cap_pad_mask.append(cap_pad_mask)
-
-            # Image
-            img_patches, size, (F_t, H_t, W_t) = self._patchify_image(image, patch_size, f_patch_size)
-            img_out, img_pos_ids, img_pad_mask, _, _ = self._pad_with_ids(
-                img_patches, (F_t, H_t, W_t), (cap_len + 1, 0, 0), device
-            )
-            all_img_out.append(img_out)
-            all_img_size.append(size)
-            all_img_pos_ids.append(img_pos_ids)
-            all_img_pad_mask.append(img_pad_mask)
-
-        return (
-            all_img_out,
-            all_cap_out,
-            all_img_size,
-            all_img_pos_ids,
-            all_cap_pos_ids,
-            all_img_pad_mask,
-            all_cap_pad_mask,
-        )
-
-    def patchify_and_embed_omni(
         self,
-        all_x: List[List[torch.Tensor]],
-        all_cap_feats: List[List[torch.Tensor]],
-        all_siglip_feats: List[List[torch.Tensor]],
+        all_image: List[torch.Tensor],
+        all_cap_feats: List[torch.Tensor],
         patch_size: int,
         f_patch_size: int,
-        images_noise_mask: List[List[int]],
     ):
-        """Patchify for omni mode: multiple images per batch item with noise masks."""
-        bsz = len(all_x)
-        device = all_x[0][-1].device
-        dtype = all_x[0][-1].dtype
+        pH = pW = patch_size
+        pF = f_patch_size
+        device = all_image[0].device
 
-        all_x_out, all_x_size, all_x_pos_ids, all_x_pad_mask, all_x_len, all_x_noise_mask = [], [], [], [], [], []
-        all_cap_out, all_cap_pos_ids, all_cap_pad_mask, all_cap_len, all_cap_noise_mask = [], [], [], [], []
-        all_sig_out, all_sig_pos_ids, all_sig_pad_mask, all_sig_len, all_sig_noise_mask = [], [], [], [], []
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+        all_cap_pos_ids = []
+        all_cap_pad_mask = []
+        all_cap_feats_out = []
 
-        for i in range(bsz):
-            num_images = len(all_x[i])
-            cap_feats_list, cap_pos_list, cap_mask_list, cap_lens, cap_noise = [], [], [], [], []
-            cap_end_pos = []
-            cap_cu_len = 1
+        for i, (image, cap_feat) in enumerate(zip(all_image, all_cap_feats)):
+            ### Process Caption
+            cap_ori_len = len(cap_feat)
+            cap_padding_len = (-cap_ori_len) % SEQ_MULTI_OF
+            # padded position ids
+            cap_padded_pos_ids = self.create_coordinate_grid(
+                size=(cap_ori_len + cap_padding_len, 1, 1),
+                start=(1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            all_cap_pos_ids.append(cap_padded_pos_ids)
+            # pad mask
+            cap_pad_mask = torch.cat(
+                [
+                    torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_cap_pad_mask.append(
+                cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
+            )
 
-            # Process captions
-            for j, cap_item in enumerate(all_cap_feats[i]):
-                noise_val = images_noise_mask[i][j] if j < len(images_noise_mask[i]) else 1
-                cap_out, cap_pos, cap_mask, cap_len, cap_nm = self._pad_with_ids(
-                    cap_item,
-                    (len(cap_item) + (-len(cap_item)) % SEQ_MULTI_OF, 1, 1),
-                    (cap_cu_len, 0, 0),
-                    device,
-                    noise_val,
-                )
-                cap_feats_list.append(cap_out)
-                cap_pos_list.append(cap_pos)
-                cap_mask_list.append(cap_mask)
-                cap_lens.append(cap_len)
-                cap_noise.extend(cap_nm)
-                cap_cu_len += len(cap_item)
-                cap_end_pos.append(cap_cu_len)
-                cap_cu_len += 2  # for image vae and siglip tokens
+            # padded feature
+            cap_padded_feat = torch.cat([cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)], dim=0)
+            all_cap_feats_out.append(cap_padded_feat)
 
-            all_cap_out.append(torch.cat(cap_feats_list, dim=0))
-            all_cap_pos_ids.append(torch.cat(cap_pos_list, dim=0))
-            all_cap_pad_mask.append(torch.cat(cap_mask_list, dim=0))
-            all_cap_len.append(cap_lens)
-            all_cap_noise_mask.append(cap_noise)
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
 
-            # Process images
-            x_feats_list, x_pos_list, x_mask_list, x_lens, x_size, x_noise = [], [], [], [], [], []
-            for j, x_item in enumerate(all_x[i]):
-                noise_val = images_noise_mask[i][j]
-                if x_item is not None:
-                    x_patches, size, (F_t, H_t, W_t) = self._patchify_image(x_item, patch_size, f_patch_size)
-                    x_out, x_pos, x_mask, x_len, x_nm = self._pad_with_ids(
-                        x_patches, (F_t, H_t, W_t), (cap_end_pos[j], 0, 0), device, noise_val
-                    )
-                    x_size.append(size)
-                else:
-                    x_len = SEQ_MULTI_OF
-                    x_out = torch.zeros((x_len, X_PAD_DIM), dtype=dtype, device=device)
-                    x_pos = self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(x_len, 1)
-                    x_mask = torch.ones(x_len, dtype=torch.bool, device=device)
-                    x_nm = [noise_val] * x_len
-                    x_size.append(None)
-                x_feats_list.append(x_out)
-                x_pos_list.append(x_pos)
-                x_mask_list.append(x_mask)
-                x_lens.append(x_len)
-                x_noise.extend(x_nm)
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
 
-            all_x_out.append(torch.cat(x_feats_list, dim=0))
-            all_x_pos_ids.append(torch.cat(x_pos_list, dim=0))
-            all_x_pad_mask.append(torch.cat(x_mask_list, dim=0))
-            all_x_size.append(x_size)
-            all_x_len.append(x_lens)
-            all_x_noise_mask.append(x_noise)
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
 
-            # Process siglip
-            if all_siglip_feats[i] is None:
-                all_sig_len.append([0] * num_images)
-                all_sig_out.append(None)
-            else:
-                sig_feats_list, sig_pos_list, sig_mask_list, sig_lens, sig_noise = [], [], [], [], []
-                for j, sig_item in enumerate(all_siglip_feats[i]):
-                    noise_val = images_noise_mask[i][j]
-                    if sig_item is not None:
-                        sig_H, sig_W, sig_C = sig_item.size()
-                        sig_flat = sig_item.permute(2, 0, 1).reshape(sig_H * sig_W, sig_C)
-                        sig_out, sig_pos, sig_mask, sig_len, sig_nm = self._pad_with_ids(
-                            sig_flat, (1, sig_H, sig_W), (cap_end_pos[j] + 1, 0, 0), device, noise_val
-                        )
-                        # Scale position IDs to match x resolution
-                        if x_size[j] is not None:
-                            sig_pos = sig_pos.float()
-                            sig_pos[..., 1] = sig_pos[..., 1] / max(sig_H - 1, 1) * (x_size[j][1] - 1)
-                            sig_pos[..., 2] = sig_pos[..., 2] / max(sig_W - 1, 1) * (x_size[j][2] - 1)
-                            sig_pos = sig_pos.to(torch.int32)
-                    else:
-                        sig_len = SEQ_MULTI_OF
-                        sig_out = torch.zeros((sig_len, self.config.siglip_feat_dim), dtype=dtype, device=device)
-                        sig_pos = (
-                            self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(sig_len, 1)
-                        )
-                        sig_mask = torch.ones(sig_len, dtype=torch.bool, device=device)
-                        sig_nm = [noise_val] * sig_len
-                    sig_feats_list.append(sig_out)
-                    sig_pos_list.append(sig_pos)
-                    sig_mask_list.append(sig_mask)
-                    sig_lens.append(sig_len)
-                    sig_noise.extend(sig_nm)
-
-                all_sig_out.append(torch.cat(sig_feats_list, dim=0))
-                all_sig_pos_ids.append(torch.cat(sig_pos_list, dim=0))
-                all_sig_pad_mask.append(torch.cat(sig_mask_list, dim=0))
-                all_sig_len.append(sig_lens)
-                all_sig_noise_mask.append(sig_noise)
-
-        # Compute x position offsets
-        all_x_pos_offsets = [(sum(all_cap_len[i]), sum(all_cap_len[i]) + sum(all_x_len[i])) for i in range(bsz)]
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_ori_len + cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padded_pos_ids = torch.cat(
+                [
+                    image_ori_pos_ids,
+                    self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                    .flatten(0, 2)
+                    .repeat(image_padding_len, 1),
+                ],
+                dim=0,
+            )
+            all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
+            # pad mask
+            image_pad_mask = torch.cat(
+                [
+                    torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                    torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                ],
+                dim=0,
+            )
+            all_image_pad_mask.append(
+                image_pad_mask
+                if image_padding_len > 0
+                else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
+            )
+            # padded feature
+            image_padded_feat = torch.cat(
+                [image, image[-1:].repeat(image_padding_len, 1)],
+                dim=0,
+            )
+            all_image_out.append(image_padded_feat if image_padding_len > 0 else image)
 
         return (
-            all_x_out,
-            all_cap_out,
-            all_sig_out,
-            all_x_size,
-            all_x_pos_ids,
+            all_image_out,
+            all_cap_feats_out,
+            all_image_size,
+            all_image_pos_ids,
             all_cap_pos_ids,
-            all_sig_pos_ids,
-            all_x_pad_mask,
+            all_image_pad_mask,
             all_cap_pad_mask,
-            all_sig_pad_mask,
-            all_x_pos_offsets,
-            all_x_noise_mask,
-            all_cap_noise_mask,
-            all_sig_noise_mask,
         )
 
-    def _prepare_sequence(
-        self,
-        feats: List[torch.Tensor],
-        pos_ids: List[torch.Tensor],
-        inner_pad_mask: List[torch.Tensor],
-        pad_token: torch.nn.Parameter,
-        noise_mask: Optional[List[List[int]]] = None,
-        device: torch.device = None,
-    ):
-        """Prepare sequence: apply pad token, RoPE embed, pad to batch, create attention mask."""
-        item_seqlens = [len(f) for f in feats]
-        max_seqlen = max(item_seqlens)
-        bsz = len(feats)
-
-        # Pad token
-        feats_cat = torch.cat(feats, dim=0)
-        feats_cat[torch.cat(inner_pad_mask)] = pad_token
-        feats = list(feats_cat.split(item_seqlens, dim=0))
-
-        # RoPE
-        freqs_cis = list(self.rope_embedder(torch.cat(pos_ids, dim=0)).split([len(p) for p in pos_ids], dim=0))
-
-        # Pad to batch
-        feats = pad_sequence(feats, batch_first=True, padding_value=0.0)
-        freqs_cis = pad_sequence(freqs_cis, batch_first=True, padding_value=0.0)[:, : feats.shape[1]]
-
-        # Attention mask
-        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(item_seqlens):
-            attn_mask[i, :seq_len] = 1
-
-        # Noise mask
-        noise_mask_tensor = None
-        if noise_mask is not None:
-            noise_mask_tensor = pad_sequence(
-                [torch.tensor(m, dtype=torch.long, device=device) for m in noise_mask],
-                batch_first=True,
-                padding_value=0,
-            )[:, : feats.shape[1]]
-
-        return feats, freqs_cis, attn_mask, item_seqlens, noise_mask_tensor
-
-    def _build_unified_sequence(
-        self,
-        x: torch.Tensor,
-        x_freqs: torch.Tensor,
-        x_seqlens: List[int],
-        x_noise_mask: Optional[List[List[int]]],
-        cap: torch.Tensor,
-        cap_freqs: torch.Tensor,
-        cap_seqlens: List[int],
-        cap_noise_mask: Optional[List[List[int]]],
-        siglip: Optional[torch.Tensor],
-        siglip_freqs: Optional[torch.Tensor],
-        siglip_seqlens: Optional[List[int]],
-        siglip_noise_mask: Optional[List[List[int]]],
-        omni_mode: bool,
-        device: torch.device,
-    ):
-        """Build unified sequence: x, cap, and optionally siglip.
-        Basic mode order: [x, cap]; Omni mode order: [cap, x, siglip]
-        """
-        bsz = len(x_seqlens)
-        unified = []
-        unified_freqs = []
-        unified_noise_mask = []
-
-        for i in range(bsz):
-            x_len, cap_len = x_seqlens[i], cap_seqlens[i]
-
-            if omni_mode:
-                # Omni: [cap, x, siglip]
-                if siglip is not None and siglip_seqlens is not None:
-                    sig_len = siglip_seqlens[i]
-                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len], siglip[i][:sig_len]]))
-                    unified_freqs.append(
-                        torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len], siglip_freqs[i][:sig_len]])
-                    )
-                    unified_noise_mask.append(
-                        torch.tensor(
-                            cap_noise_mask[i] + x_noise_mask[i] + siglip_noise_mask[i], dtype=torch.long, device=device
-                        )
-                    )
-                else:
-                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len]]))
-                    unified_freqs.append(torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len]]))
-                    unified_noise_mask.append(
-                        torch.tensor(cap_noise_mask[i] + x_noise_mask[i], dtype=torch.long, device=device)
-                    )
-            else:
-                # Basic: [x, cap]
-                unified.append(torch.cat([x[i][:x_len], cap[i][:cap_len]]))
-                unified_freqs.append(torch.cat([x_freqs[i][:x_len], cap_freqs[i][:cap_len]]))
-
-        # Compute unified seqlens
-        if omni_mode:
-            if siglip is not None and siglip_seqlens is not None:
-                unified_seqlens = [a + b + c for a, b, c in zip(cap_seqlens, x_seqlens, siglip_seqlens)]
-            else:
-                unified_seqlens = [a + b for a, b in zip(cap_seqlens, x_seqlens)]
-        else:
-            unified_seqlens = [a + b for a, b in zip(x_seqlens, cap_seqlens)]
-
-        max_seqlen = max(unified_seqlens)
-
-        # Pad to batch
-        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
-        unified_freqs = pad_sequence(unified_freqs, batch_first=True, padding_value=0.0)
-
-        # Attention mask
-        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(unified_seqlens):
-            attn_mask[i, :seq_len] = 1
-
-        # Noise mask
-        noise_mask_tensor = None
-        if omni_mode:
-            noise_mask_tensor = pad_sequence(unified_noise_mask, batch_first=True, padding_value=0)[
-                :, : unified.shape[1]
-            ]
-
-        return unified, unified_freqs, attn_mask, noise_mask_tensor
-
     def forward(
         self,
-        x: Union[List[torch.Tensor], List[List[torch.Tensor]]],
+        x: List[torch.Tensor],
         t,
-        cap_feats: Union[List[torch.Tensor], List[List[torch.Tensor]]],
-        return_dict: bool = True,
+        cap_feats: List[torch.Tensor],
         controlnet_block_samples: Optional[Dict[int, torch.Tensor]] = None,
-        siglip_feats: Optional[List[List[torch.Tensor]]] = None,
-        image_noise_mask: Optional[List[List[int]]] = None,
-        patch_size: int = 2,
-        f_patch_size: int = 1,
+        patch_size=2,
+        f_patch_size=1,
+        return_dict: bool = True,
     ):
-        """
-        Flow: patchify -> t_embed -> x_embed -> x_refine -> cap_embed -> cap_refine
-              -> [siglip_embed -> siglip_refine] -> build_unified -> main_layers -> final_layer -> unpatchify
-        """
-        assert patch_size in self.all_patch_size and f_patch_size in self.all_f_patch_size
-        omni_mode = isinstance(x[0], list)
-        device = x[0][-1].device if omni_mode else x[0].device
+        assert patch_size in self.all_patch_size
+        assert f_patch_size in self.all_f_patch_size
 
-        if omni_mode:
-            # Dual embeddings: noisy (t) and clean (t=1)
-            t_noisy = self.t_embedder(t * self.t_scale).type_as(x[0][-1])
-            t_clean = self.t_embedder(torch.ones_like(t) * self.t_scale).type_as(x[0][-1])
-            adaln_input = None
-        else:
-            # Single embedding for all tokens
-            adaln_input = self.t_embedder(t * self.t_scale).type_as(x[0])
-            t_noisy = t_clean = None
+        bsz = len(x)
+        device = x[0].device
+        t = t * self.t_scale
+        t = self.t_embedder(t)
 
-        # Patchify
-        if omni_mode:
-            (
-                x,
-                cap_feats,
-                siglip_feats,
-                x_size,
-                x_pos_ids,
-                cap_pos_ids,
-                siglip_pos_ids,
-                x_pad_mask,
-                cap_pad_mask,
-                siglip_pad_mask,
-                x_pos_offsets,
-                x_noise_mask,
-                cap_noise_mask,
-                siglip_noise_mask,
-            ) = self.patchify_and_embed_omni(x, cap_feats, siglip_feats, patch_size, f_patch_size, image_noise_mask)
-        else:
-            (
-                x,
-                cap_feats,
-                x_size,
-                x_pos_ids,
-                cap_pos_ids,
-                x_pad_mask,
-                cap_pad_mask,
-            ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
-            x_pos_offsets = x_noise_mask = cap_noise_mask = siglip_noise_mask = None
-
-        # X embed & refine
-        x_seqlens = [len(xi) for xi in x]
-        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](torch.cat(x, dim=0))  # embed
-        x, x_freqs, x_mask, _, x_noise_tensor = self._prepare_sequence(
-            list(x.split(x_seqlens, dim=0)), x_pos_ids, x_pad_mask, self.x_pad_token, x_noise_mask, device
-        )
-
-        for layer in self.noise_refiner:
-            x = (
-                self._gradient_checkpointing_func(
-                    layer, x, x_mask, x_freqs, adaln_input, x_noise_tensor, t_noisy, t_clean
-                )
-                if torch.is_grad_enabled() and self.gradient_checkpointing
-                else layer(x, x_mask, x_freqs, adaln_input, x_noise_tensor, t_noisy, t_clean)
-            )
-
-        # Cap embed & refine
-        cap_seqlens = [len(ci) for ci in cap_feats]
-        cap_feats = self.cap_embedder(torch.cat(cap_feats, dim=0))  # embed
-        cap_feats, cap_freqs, cap_mask, _, _ = self._prepare_sequence(
-            list(cap_feats.split(cap_seqlens, dim=0)), cap_pos_ids, cap_pad_mask, self.cap_pad_token, None, device
-        )
-
-        for layer in self.context_refiner:
-            cap_feats = (
-                self._gradient_checkpointing_func(layer, cap_feats, cap_mask, cap_freqs)
-                if torch.is_grad_enabled() and self.gradient_checkpointing
-                else layer(cap_feats, cap_mask, cap_freqs)
-            )
-
-        # Siglip embed & refine
-        siglip_seqlens = siglip_freqs = None
-        if omni_mode and siglip_feats[0] is not None and self.siglip_embedder is not None:
-            siglip_seqlens = [len(si) for si in siglip_feats]
-            siglip_feats = self.siglip_embedder(torch.cat(siglip_feats, dim=0))  # embed
-            siglip_feats, siglip_freqs, siglip_mask, _, _ = self._prepare_sequence(
-                list(siglip_feats.split(siglip_seqlens, dim=0)),
-                siglip_pos_ids,
-                siglip_pad_mask,
-                self.siglip_pad_token,
-                None,
-                device,
-            )
-
-            for layer in self.siglip_refiner:
-                siglip_feats = (
-                    self._gradient_checkpointing_func(layer, siglip_feats, siglip_mask, siglip_freqs)
-                    if torch.is_grad_enabled() and self.gradient_checkpointing
-                    else layer(siglip_feats, siglip_mask, siglip_freqs)
-                )
-
-        # Unified sequence
-        unified, unified_freqs, unified_mask, unified_noise_tensor = self._build_unified_sequence(
+        (
             x,
-            x_freqs,
-            x_seqlens,
-            x_noise_mask,
             cap_feats,
-            cap_freqs,
-            cap_seqlens,
-            cap_noise_mask,
-            siglip_feats,
-            siglip_freqs,
-            siglip_seqlens,
-            siglip_noise_mask,
-            omni_mode,
-            device,
+            x_size,
+            x_pos_ids,
+            cap_pos_ids,
+            x_inner_pad_mask,
+            cap_inner_pad_mask,
+        ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+
+        # x embed & refine
+        x_item_seqlens = [len(_) for _ in x]
+        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
+        x_max_item_seqlen = max(x_item_seqlens)
+
+        x = torch.cat(x, dim=0)
+        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
+
+        # Match t_embedder output dtype to x for layerwise casting compatibility
+        adaln_input = t.type_as(x)
+        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
+        x = list(x.split(x_item_seqlens, dim=0))
+        x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split([len(_) for _ in x_pos_ids], dim=0))
+
+        x = pad_sequence(x, batch_first=True, padding_value=0.0)
+        x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        x_freqs_cis = x_freqs_cis[:, : x.shape[1]]
+
+        x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(x_item_seqlens):
+            x_attn_mask[i, :seq_len] = 1
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.noise_refiner:
+                x = self._gradient_checkpointing_func(layer, x, x_attn_mask, x_freqs_cis, adaln_input)
+        else:
+            for layer in self.noise_refiner:
+                x = layer(x, x_attn_mask, x_freqs_cis, adaln_input)
+
+        # cap embed & refine
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        cap_max_item_seqlen = max(cap_item_seqlens)
+
+        cap_feats = torch.cat(cap_feats, dim=0)
+        cap_feats = self.cap_embedder(cap_feats)
+        cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token
+        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
+        cap_freqs_cis = list(
+            self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split([len(_) for _ in cap_pos_ids], dim=0)
         )
 
-        # Main transformer layers
-        for layer_idx, layer in enumerate(self.layers):
-            unified = (
-                self._gradient_checkpointing_func(
-                    layer, unified, unified_mask, unified_freqs, adaln_input, unified_noise_tensor, t_noisy, t_clean
+        cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
+        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
+        # Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
+        cap_freqs_cis = cap_freqs_cis[:, : cap_feats.shape[1]]
+
+        cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(cap_item_seqlens):
+            cap_attn_mask[i, :seq_len] = 1
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer in self.context_refiner:
+                cap_feats = self._gradient_checkpointing_func(layer, cap_feats, cap_attn_mask, cap_freqs_cis)
+        else:
+            for layer in self.context_refiner:
+                cap_feats = layer(cap_feats, cap_attn_mask, cap_freqs_cis)
+
+        # unified
+        unified = []
+        unified_freqs_cis = []
+        for i in range(bsz):
+            x_len = x_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
+            unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
+        unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
+        assert unified_item_seqlens == [len(_) for _ in unified]
+        unified_max_item_seqlen = max(unified_item_seqlens)
+
+        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
+        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
+        unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(unified_item_seqlens):
+            unified_attn_mask[i, :seq_len] = 1
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for layer_idx, layer in enumerate(self.layers):
+                unified = self._gradient_checkpointing_func(
+                    layer, unified, unified_attn_mask, unified_freqs_cis, adaln_input
                 )
-                if torch.is_grad_enabled() and self.gradient_checkpointing
-                else layer(unified, unified_mask, unified_freqs, adaln_input, unified_noise_tensor, t_noisy, t_clean)
-            )
-            if controlnet_block_samples is not None and layer_idx in controlnet_block_samples:
-                unified = unified + controlnet_block_samples[layer_idx]
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
+        else:
+            for layer_idx, layer in enumerate(self.layers):
+                unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
+                if controlnet_block_samples is not None:
+                    if layer_idx in controlnet_block_samples:
+                        unified = unified + controlnet_block_samples[layer_idx]
 
-        unified = (
-            self.all_final_layer[f"{patch_size}-{f_patch_size}"](
-                unified, noise_mask=unified_noise_tensor, c_noisy=t_noisy, c_clean=t_clean
-            )
-            if omni_mode
-            else self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, c=adaln_input)
-        )
+        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
+        unified = list(unified.unbind(dim=0))
+        x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
 
-        # Unpatchify
-        x = self.unpatchify(list(unified.unbind(dim=0)), x_size, patch_size, f_patch_size, x_pos_offsets)
+        if not return_dict:
+            return (x,)
 
-        return (x,) if not return_dict else Transformer2DModelOutput(sample=x)
+        return Transformer2DModelOutput(sample=x)

src/diffusers/modular_pipelines/flux/modular_blocks.py

@@ -360,7 +360,7 @@ class FluxKontextCoreDenoiseStep(SequentialPipelineBlocks):
 AUTO_BLOCKS = InsertableDict(
     [
         ("text_encoder", FluxTextEncoderStep()),
-        ("vae_encoder", FluxAutoVaeEncoderStep()),
+        ("image_encoder", FluxAutoVaeEncoderStep()),
         ("denoise", FluxCoreDenoiseStep()),
         ("decode", FluxDecodeStep()),
     ]
@@ -369,7 +369,7 @@ AUTO_BLOCKS = InsertableDict(
 AUTO_BLOCKS_KONTEXT = InsertableDict(
     [
         ("text_encoder", FluxTextEncoderStep()),
-        ("vae_encoder", FluxKontextAutoVaeEncoderStep()),
+        ("image_encoder", FluxKontextAutoVaeEncoderStep()),
         ("denoise", FluxKontextCoreDenoiseStep()),
         ("decode", FluxDecodeStep()),
     ]

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -501,19 +501,15 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
     @property
     def input_names(self) -> List[str]:
-        return [input_param.name for input_param in self.inputs if input_param.name is not None]
+        return [input_param.name for input_param in self.inputs]
 
     @property
     def intermediate_output_names(self) -> List[str]:
-        return [output_param.name for output_param in self.intermediate_outputs if output_param.name is not None]
+        return [output_param.name for output_param in self.intermediate_outputs]
 
     @property
     def output_names(self) -> List[str]:
-        return [output_param.name for output_param in self.outputs if output_param.name is not None]
-
-    @property
-    def component_names(self) -> List[str]:
-        return [component.name for component in self.expected_components]
+        return [output_param.name for output_param in self.outputs]
 
     @property
     def doc(self):
@@ -1529,8 +1525,10 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         if blocks is None:
             if modular_config_dict is not None:
                 blocks_class_name = modular_config_dict.get("_blocks_class_name")
-            else:
+            elif config_dict is not None:
                 blocks_class_name = self.get_default_blocks_name(config_dict)
+            else:
+                blocks_class_name = None
             if blocks_class_name is not None:
                 diffusers_module = importlib.import_module("diffusers")
                 blocks_class = getattr(diffusers_module, blocks_class_name)
@@ -1627,10 +1625,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             return None, config_dict
 
         except EnvironmentError as e:
-            raise EnvironmentError(
-                f"Failed to load config from '{pretrained_model_name_or_path}'. "
-                f"Could not find or load 'modular_model_index.json' or 'model_index.json'."
-            ) from e
+            logger.debug(f" model_index.json not found in the repo: {e}")
 
         return None, None
 
@@ -2555,11 +2550,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             kwargs_type = expected_input_param.kwargs_type
             if name in passed_kwargs:
                 state.set(name, passed_kwargs.pop(name), kwargs_type)
-            elif kwargs_type is not None and kwargs_type in passed_kwargs:
-                kwargs_dict = passed_kwargs.pop(kwargs_type)
-                for k, v in kwargs_dict.items():
-                    state.set(k, v, kwargs_type)
-            elif name is not None and name not in state.values:
+            elif name not in state.values:
                 state.set(name, default, kwargs_type)
 
         # Warn about unexpected inputs

src/diffusers/modular_pipelines/qwenimage/decoders.py

@@ -30,47 +30,6 @@ from .modular_pipeline import QwenImageModularPipeline, QwenImagePachifier
 logger = logging.get_logger(__name__)
 
 
-class QwenImageAfterDenoiseStep(ModularPipelineBlocks):
-    model_name = "qwenimage"
-
-    @property
-    def description(self) -> str:
-        return "Step that unpack the latents from 3D tensor (batch_size, sequence_length, channels) into 5D tensor (batch_size, channels, 1, height, width)"
-
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        components = [
-            ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
-        ]
-
-        return components
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(
-                name="latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The latents to decode, can be generated in the denoise step",
-            ),
-        ]
-
-    @torch.no_grad()
-    def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
-        block_state = self.get_block_state(state)
-
-        vae_scale_factor = components.vae_scale_factor
-        block_state.latents = components.pachifier.unpack_latents(
-            block_state.latents, block_state.height, block_state.width, vae_scale_factor=vae_scale_factor
-        )
-
-        self.set_block_state(state, block_state)
-        return components, state
-
-
 class QwenImageDecoderStep(ModularPipelineBlocks):
     model_name = "qwenimage"
 
@@ -82,6 +41,7 @@ class QwenImageDecoderStep(ModularPipelineBlocks):
     def expected_components(self) -> List[ComponentSpec]:
         components = [
             ComponentSpec("vae", AutoencoderKLQwenImage),
+            ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
         ]
 
         return components
@@ -89,6 +49,8 @@ class QwenImageDecoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
+            InputParam(name="height", required=True),
+            InputParam(name="width", required=True),
             InputParam(
                 name="latents",
                 required=True,
@@ -112,12 +74,10 @@ class QwenImageDecoderStep(ModularPipelineBlocks):
         block_state = self.get_block_state(state)
 
         # YiYi Notes: remove support for output_type = "latents', we can just skip decode/encode step in modular
-        if block_state.latents.ndim == 4:
-            block_state.latents = block_state.latents.unsqueeze(dim=1)
-        elif block_state.latents.ndim != 5:
-            raise ValueError(
-                f"expect latents to be a 4D or 5D tensor but got: {block_state.latents.shape}. Please make sure the latents are unpacked before decode step."
-            )
+        vae_scale_factor = components.vae_scale_factor
+        block_state.latents = components.pachifier.unpack_latents(
+            block_state.latents, block_state.height, block_state.width, vae_scale_factor=vae_scale_factor
+        )
         block_state.latents = block_state.latents.to(components.vae.dtype)
 
         latents_mean = (

src/diffusers/modular_pipelines/qwenimage/modular_blocks.py

@@ -26,12 +26,7 @@ from .before_denoise import (
     QwenImageSetTimestepsStep,
     QwenImageSetTimestepsWithStrengthStep,
 )
-from .decoders import (
-    QwenImageAfterDenoiseStep,
-    QwenImageDecoderStep,
-    QwenImageInpaintProcessImagesOutputStep,
-    QwenImageProcessImagesOutputStep,
-)
+from .decoders import QwenImageDecoderStep, QwenImageInpaintProcessImagesOutputStep, QwenImageProcessImagesOutputStep
 from .denoise import (
     QwenImageControlNetDenoiseStep,
     QwenImageDenoiseStep,
@@ -97,7 +92,6 @@ TEXT2IMAGE_BLOCKS = InsertableDict(
         ("set_timesteps", QwenImageSetTimestepsStep()),
         ("prepare_rope_inputs", QwenImageRoPEInputsStep()),
         ("denoise", QwenImageDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageDecodeStep()),
     ]
 )
@@ -211,7 +205,6 @@ INPAINT_BLOCKS = InsertableDict(
         ("prepare_inpaint_latents", QwenImageInpaintPrepareLatentsStep()),
         ("prepare_rope_inputs", QwenImageRoPEInputsStep()),
         ("denoise", QwenImageInpaintDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageInpaintDecodeStep()),
     ]
 )
@@ -271,7 +264,6 @@ IMAGE2IMAGE_BLOCKS = InsertableDict(
         ("prepare_img2img_latents", QwenImagePrepareLatentsWithStrengthStep()),
         ("prepare_rope_inputs", QwenImageRoPEInputsStep()),
         ("denoise", QwenImageDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageDecodeStep()),
     ]
 )
@@ -537,16 +529,8 @@ class QwenImageCoreDenoiseStep(SequentialPipelineBlocks):
         QwenImageAutoBeforeDenoiseStep,
         QwenImageOptionalControlNetBeforeDenoiseStep,
         QwenImageAutoDenoiseStep,
-        QwenImageAfterDenoiseStep,
-    ]
-    block_names = [
-        "input",
-        "controlnet_input",
-        "before_denoise",
-        "controlnet_before_denoise",
-        "denoise",
-        "after_denoise",
     ]
+    block_names = ["input", "controlnet_input", "before_denoise", "controlnet_before_denoise", "denoise"]
 
     @property
     def description(self):
@@ -669,7 +653,6 @@ EDIT_BLOCKS = InsertableDict(
         ("set_timesteps", QwenImageSetTimestepsStep()),
         ("prepare_rope_inputs", QwenImageEditRoPEInputsStep()),
         ("denoise", QwenImageEditDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageDecodeStep()),
     ]
 )
@@ -719,7 +702,6 @@ EDIT_INPAINT_BLOCKS = InsertableDict(
         ("prepare_inpaint_latents", QwenImageInpaintPrepareLatentsStep()),
         ("prepare_rope_inputs", QwenImageEditRoPEInputsStep()),
         ("denoise", QwenImageEditInpaintDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageInpaintDecodeStep()),
     ]
 )
@@ -859,9 +841,8 @@ class QwenImageEditCoreDenoiseStep(SequentialPipelineBlocks):
         QwenImageEditAutoInputStep,
         QwenImageEditAutoBeforeDenoiseStep,
         QwenImageEditAutoDenoiseStep,
-        QwenImageAfterDenoiseStep,
     ]
-    block_names = ["input", "before_denoise", "denoise", "after_denoise"]
+    block_names = ["input", "before_denoise", "denoise"]
 
     @property
     def description(self):
@@ -973,7 +954,6 @@ EDIT_PLUS_BLOCKS = InsertableDict(
         ("set_timesteps", QwenImageSetTimestepsStep()),
         ("prepare_rope_inputs", QwenImageEditPlusRoPEInputsStep()),
         ("denoise", QwenImageEditDenoiseStep()),
-        ("after_denoise", QwenImageAfterDenoiseStep()),
         ("decode", QwenImageDecodeStep()),
     ]
 )
@@ -1057,9 +1037,8 @@ class QwenImageEditPlusCoreDenoiseStep(SequentialPipelineBlocks):
         QwenImageEditPlusAutoInputStep,
         QwenImageEditPlusAutoBeforeDenoiseStep,
         QwenImageEditAutoDenoiseStep,
-        QwenImageAfterDenoiseStep,
     ]
-    block_names = ["input", "before_denoise", "denoise", "after_denoise"]
+    block_names = ["input", "before_denoise", "denoise"]
 
     @property
     def description(self):

src/diffusers/modular_pipelines/qwenimage/node_utils.py

@@ -0,0 +1,95 @@
+# 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.
+
+
+# mellon nodes
+QwenImage_NODE_TYPES_PARAMS_MAP = {
+    "controlnet": {
+        "inputs": [
+            "control_image",
+            "controlnet_conditioning_scale",
+            "control_guidance_start",
+            "control_guidance_end",
+            "height",
+            "width",
+        ],
+        "model_inputs": [
+            "controlnet",
+            "vae",
+        ],
+        "outputs": [
+            "controlnet_out",
+        ],
+        "block_names": ["controlnet_vae_encoder"],
+    },
+    "denoise": {
+        "inputs": [
+            "embeddings",
+            "width",
+            "height",
+            "seed",
+            "num_inference_steps",
+            "guidance_scale",
+            "image_latents",
+            "strength",
+            "controlnet",
+        ],
+        "model_inputs": [
+            "unet",
+            "guider",
+            "scheduler",
+        ],
+        "outputs": [
+            "latents",
+            "latents_preview",
+        ],
+        "block_names": ["denoise"],
+    },
+    "vae_encoder": {
+        "inputs": [
+            "image",
+            "width",
+            "height",
+        ],
+        "model_inputs": [
+            "vae",
+        ],
+        "outputs": [
+            "image_latents",
+        ],
+    },
+    "text_encoder": {
+        "inputs": [
+            "prompt",
+            "negative_prompt",
+        ],
+        "model_inputs": [
+            "text_encoders",
+        ],
+        "outputs": [
+            "embeddings",
+        ],
+    },
+    "decoder": {
+        "inputs": [
+            "latents",
+        ],
+        "model_inputs": [
+            "vae",
+        ],
+        "outputs": [
+            "images",
+        ],
+    },
+}

src/diffusers/modular_pipelines/stable_diffusion_xl/node_utils.py

@@ -0,0 +1,99 @@
+# Copyright 2025 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.
+
+
+SDXL_NODE_TYPES_PARAMS_MAP = {
+    "controlnet": {
+        "inputs": [
+            "control_image",
+            "controlnet_conditioning_scale",
+            "control_guidance_start",
+            "control_guidance_end",
+            "height",
+            "width",
+        ],
+        "model_inputs": [
+            "controlnet",
+        ],
+        "outputs": [
+            "controlnet_out",
+        ],
+        "block_names": [None],
+    },
+    "denoise": {
+        "inputs": [
+            "embeddings",
+            "width",
+            "height",
+            "seed",
+            "num_inference_steps",
+            "guidance_scale",
+            "image_latents",
+            "strength",
+            # custom adapters coming in as inputs
+            "controlnet",
+            # ip_adapter is optional and custom; include if available
+            "ip_adapter",
+        ],
+        "model_inputs": [
+            "unet",
+            "guider",
+            "scheduler",
+        ],
+        "outputs": [
+            "latents",
+            "latents_preview",
+        ],
+        "block_names": ["denoise"],
+    },
+    "vae_encoder": {
+        "inputs": [
+            "image",
+            "width",
+            "height",
+        ],
+        "model_inputs": [
+            "vae",
+        ],
+        "outputs": [
+            "image_latents",
+        ],
+        "block_names": ["vae_encoder"],
+    },
+    "text_encoder": {
+        "inputs": [
+            "prompt",
+            "negative_prompt",
+        ],
+        "model_inputs": [
+            "text_encoders",
+        ],
+        "outputs": [
+            "embeddings",
+        ],
+        "block_names": ["text_encoder"],
+    },
+    "decoder": {
+        "inputs": [
+            "latents",
+        ],
+        "model_inputs": [
+            "vae",
+        ],
+        "outputs": [
+            "images",
+        ],
+        "block_names": ["decode"],
+    },
+}

src/diffusers/modular_pipelines/z_image/denoise.py

@@ -129,10 +129,6 @@ class ZImageLoopDenoiser(ModularPipelineBlocks):
                 type_hint=int,
                 description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
             ),
-            InputParam(
-                kwargs_type="denoiser_input_fields",
-                description="conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
-            ),
         ]
         guider_input_names = []
         uncond_guider_input_names = []

src/diffusers/modular_pipelines/z_image/modular_blocks.py

@@ -119,7 +119,7 @@ class ZImageAutoDenoiseStep(AutoPipelineBlocks):
 
 class ZImageAutoVaeImageEncoderStep(AutoPipelineBlocks):
     block_classes = [ZImageVaeImageEncoderStep]
-    block_names = ["vae_encoder"]
+    block_names = ["vae_image_encoder"]
     block_trigger_inputs = ["image"]
 
     @property
@@ -137,7 +137,7 @@ class ZImageAutoBlocks(SequentialPipelineBlocks):
         ZImageAutoDenoiseStep,
         ZImageVaeDecoderStep,
     ]
-    block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
+    block_names = ["text_encoder", "vae_image_encoder", "denoise", "decode"]
 
     @property
     def description(self) -> str:
@@ -162,7 +162,7 @@ TEXT2IMAGE_BLOCKS = InsertableDict(
 IMAGE2IMAGE_BLOCKS = InsertableDict(
     [
         ("text_encoder", ZImageTextEncoderStep),
-        ("vae_encoder", ZImageVaeImageEncoderStep),
+        ("vae_image_encoder", ZImageVaeImageEncoderStep),
         ("input", ZImageTextInputStep),
         ("additional_inputs", ZImageAdditionalInputsStep(image_latent_inputs=["image_latents"])),
         ("prepare_latents", ZImagePrepareLatentsStep),
@@ -178,7 +178,7 @@ IMAGE2IMAGE_BLOCKS = InsertableDict(
 AUTO_BLOCKS = InsertableDict(
     [
         ("text_encoder", ZImageTextEncoderStep),
-        ("vae_encoder", ZImageAutoVaeImageEncoderStep),
+        ("vae_image_encoder", ZImageAutoVaeImageEncoderStep),
         ("denoise", ZImageAutoDenoiseStep),
         ("decode", ZImageVaeDecoderStep),
     ]

src/diffusers/pipelines/__init__.py

@@ -165,7 +165,6 @@ else:
     _import_structure["cogview4"] = ["CogView4Pipeline", "CogView4ControlPipeline"]
     _import_structure["consisid"] = ["ConsisIDPipeline"]
     _import_structure["cosmos"] = [
-        "Cosmos2_5_PredictBasePipeline",
         "Cosmos2TextToImagePipeline",
         "CosmosTextToWorldPipeline",
         "CosmosVideoToWorldPipeline",
@@ -411,7 +410,6 @@ else:
         "ZImagePipeline",
         "ZImageControlNetPipeline",
         "ZImageControlNetInpaintPipeline",
-        "ZImageOmniPipeline",
     ]
     _import_structure["skyreels_v2"] = [
         "SkyReelsV2DiffusionForcingPipeline",
@@ -624,7 +622,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLControlNetXSPipeline,
         )
         from .cosmos import (
-            Cosmos2_5_PredictBasePipeline,
             Cosmos2TextToImagePipeline,
             Cosmos2VideoToWorldPipeline,
             CosmosTextToWorldPipeline,
@@ -857,7 +854,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ZImageControlNetInpaintPipeline,
             ZImageControlNetPipeline,
             ZImageImg2ImgPipeline,
-            ZImageOmniPipeline,
             ZImagePipeline,
         )
 

src/diffusers/pipelines/auto_pipeline.py

@@ -73,7 +73,6 @@ from .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline
 from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
 from .lumina import LuminaPipeline
 from .lumina2 import Lumina2Pipeline
-from .ovis_image import OvisImagePipeline
 from .pag import (
     HunyuanDiTPAGPipeline,
     PixArtSigmaPAGPipeline,
@@ -120,13 +119,7 @@ from .stable_diffusion_xl import (
 )
 from .wan import WanImageToVideoPipeline, WanPipeline, WanVideoToVideoPipeline
 from .wuerstchen import WuerstchenCombinedPipeline, WuerstchenDecoderPipeline
-from .z_image import (
-    ZImageControlNetInpaintPipeline,
-    ZImageControlNetPipeline,
-    ZImageImg2ImgPipeline,
-    ZImageOmniPipeline,
-    ZImagePipeline,
-)
+from .z_image import ZImageImg2ImgPipeline, ZImagePipeline
 
 
 AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
@@ -171,10 +164,6 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("qwenimage", QwenImagePipeline),
         ("qwenimage-controlnet", QwenImageControlNetPipeline),
         ("z-image", ZImagePipeline),
-        ("z-image-controlnet", ZImageControlNetPipeline),
-        ("z-image-controlnet-inpaint", ZImageControlNetInpaintPipeline),
-        ("z-image-omni", ZImageOmniPipeline),
-        ("ovis", OvisImagePipeline),
     ]
 )
 

src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py

@@ -17,7 +17,7 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
-from transformers import BertModel, BertTokenizer, CLIPImageProcessor, T5EncoderModel, T5Tokenizer
+from transformers import BertModel, BertTokenizer, CLIPImageProcessor, MT5Tokenizer, T5EncoderModel
 
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 
@@ -185,7 +185,7 @@ class HunyuanDiTControlNetPipeline(DiffusionPipeline):
             The HunyuanDiT model designed by Tencent Hunyuan.
         text_encoder_2 (`T5EncoderModel`):
             The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
-        tokenizer_2 (`T5Tokenizer`):
+        tokenizer_2 (`MT5Tokenizer`):
             The tokenizer for the mT5 embedder.
         scheduler ([`DDPMScheduler`]):
             A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
@@ -229,7 +229,7 @@ class HunyuanDiTControlNetPipeline(DiffusionPipeline):
             HunyuanDiT2DMultiControlNetModel,
         ],
         text_encoder_2: Optional[T5EncoderModel] = None,
-        tokenizer_2: Optional[T5Tokenizer] = None,
+        tokenizer_2: Optional[MT5Tokenizer] = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/cosmos/__init__.py

@@ -22,9 +22,6 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["pipeline_cosmos2_5_predict"] = [
-        "Cosmos2_5_PredictBasePipeline",
-    ]
     _import_structure["pipeline_cosmos2_text2image"] = ["Cosmos2TextToImagePipeline"]
     _import_structure["pipeline_cosmos2_video2world"] = ["Cosmos2VideoToWorldPipeline"]
     _import_structure["pipeline_cosmos_text2world"] = ["CosmosTextToWorldPipeline"]
@@ -38,9 +35,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
-        from .pipeline_cosmos2_5_predict import (
-            Cosmos2_5_PredictBasePipeline,
-        )
         from .pipeline_cosmos2_text2image import Cosmos2TextToImagePipeline
         from .pipeline_cosmos2_video2world import Cosmos2VideoToWorldPipeline
         from .pipeline_cosmos_text2world import CosmosTextToWorldPipeline

src/diffusers/pipelines/cosmos/pipeline_cosmos2_5_predict.py

@@ -1,847 +0,0 @@
-# Copyright 2025 The NVIDIA Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, Dict, List, Optional, Union
-
-import numpy as np
-import torch
-import torchvision
-import torchvision.transforms
-import torchvision.transforms.functional
-from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration
-
-from ...callbacks import MultiPipelineCallbacks, PipelineCallback
-from ...image_processor import PipelineImageInput
-from ...models import AutoencoderKLWan, CosmosTransformer3DModel
-from ...schedulers import UniPCMultistepScheduler
-from ...utils import is_cosmos_guardrail_available, is_torch_xla_available, logging, replace_example_docstring
-from ...utils.torch_utils import randn_tensor
-from ...video_processor import VideoProcessor
-from ..pipeline_utils import DiffusionPipeline
-from .pipeline_output import CosmosPipelineOutput
-
-
-if is_cosmos_guardrail_available():
-    from cosmos_guardrail import CosmosSafetyChecker
-else:
-
-    class CosmosSafetyChecker:
-        def __init__(self, *args, **kwargs):
-            raise ImportError(
-                "`cosmos_guardrail` is not installed. Please install it to use the safety checker for Cosmos: `pip install cosmos_guardrail`."
-            )
-
-
-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
-
-
-# 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")
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```python
-        >>> import torch
-        >>> from diffusers import Cosmos2_5_PredictBasePipeline
-        >>> from diffusers.utils import export_to_video, load_image, load_video
-
-        >>> model_id = "nvidia/Cosmos-Predict2.5-2B"
-        >>> pipe = Cosmos2_5_PredictBasePipeline.from_pretrained(
-        ...     model_id, revision="diffusers/base/post-trained", torch_dtype=torch.bfloat16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> # Common negative prompt reused across modes.
-        >>> negative_prompt = (
-        ...     "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
-        ...     "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
-        ...     "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky "
-        ...     "movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, "
-        ...     "fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
-        ...     "Overall, the video is of poor quality."
-        ... )
-
-        >>> # Text2World: generate a 93-frame world video from text only.
-        >>> prompt = (
-        ...     "As the red light shifts to green, the red bus at the intersection begins to move forward, its headlights "
-        ...     "cutting through the falling snow. The snowy tire tracks deepen as the vehicle inches ahead, casting fresh "
-        ...     "lines onto the slushy road. Around it, streetlights glow warmer, illuminating the drifting flakes and wet "
-        ...     "reflections on the asphalt. Other cars behind start to edge forward, their beams joining the scene. "
-        ...     "The stillness of the urban street transitions into motion as the quiet snowfall is punctuated by the slow "
-        ...     "advance of traffic through the frosty city corridor."
-        ... )
-        >>> video = pipe(
-        ...     image=None,
-        ...     video=None,
-        ...     prompt=prompt,
-        ...     negative_prompt=negative_prompt,
-        ...     num_frames=93,
-        ...     generator=torch.Generator().manual_seed(1),
-        ... ).frames[0]
-        >>> export_to_video(video, "text2world.mp4", fps=16)
-
-        >>> # Image2World: condition on a single image and generate a 93-frame world video.
-        >>> prompt = (
-        ...     "A high-definition video captures the precision of robotic welding in an industrial setting. "
-        ...     "The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. "
-        ...     "The welding process is in full swing, with bright sparks and intense light illuminating the scene, creating a vivid "
-        ...     "display of blue and white hues. A significant amount of smoke billows around the welding area, partially obscuring "
-        ...     "the view but emphasizing the heat and activity. The background reveals parts of the workshop environment, including a "
-        ...     "ventilation system and various pieces of machinery, indicating a busy and functional industrial workspace. As the video "
-        ...     "progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. "
-        ...     "The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. "
-        ...     "The metal surface beneath the torch shows ongoing signs of heating and melting. The scene retains its industrial ambiance, with "
-        ...     "the welding sparks and smoke dominating the visual field, underscoring the ongoing nature of the welding operation."
-        ... )
-        >>> image = load_image(
-        ...     "https://media.githubusercontent.com/media/nvidia-cosmos/cosmos-predict2.5/refs/heads/main/assets/base/robot_welding.jpg"
-        ... )
-        >>> video = pipe(
-        ...     image=image,
-        ...     video=None,
-        ...     prompt=prompt,
-        ...     negative_prompt=negative_prompt,
-        ...     num_frames=93,
-        ...     generator=torch.Generator().manual_seed(1),
-        ... ).frames[0]
-        >>> export_to_video(video, "image2world.mp4", fps=16)
-
-        >>> # Video2World: condition on an input clip and predict a 93-frame world video.
-        >>> prompt = (
-        ...     "The video opens with an aerial view of a large-scale sand mining construction operation, showcasing extensive piles "
-        ...     "of brown sand meticulously arranged in parallel rows. A central water channel, fed by a water pipe, flows through the "
-        ...     "middle of these sand heaps, creating ripples and movement as it cascades down. The surrounding area features dense green "
-        ...     "vegetation on the left, contrasting with the sandy terrain, while a body of water is visible in the background on the right. "
-        ...     "As the video progresses, a piece of heavy machinery, likely a bulldozer, enters the frame from the right, moving slowly along "
-        ...     "the edge of the sand piles. This machinery's presence indicates ongoing construction work in the operation. The final frame "
-        ...     "captures the same scene, with the water continuing its flow and the bulldozer still in motion, maintaining the dynamic yet "
-        ...     "steady pace of the construction activity."
-        ... )
-        >>> input_video = load_video(
-        ...     "https://github.com/nvidia-cosmos/cosmos-predict2.5/raw/refs/heads/main/assets/base/sand_mining.mp4"
-        ... )
-        >>> video = pipe(
-        ...     image=None,
-        ...     video=input_video,
-        ...     prompt=prompt,
-        ...     negative_prompt=negative_prompt,
-        ...     num_frames=93,
-        ...     generator=torch.Generator().manual_seed(1),
-        ... ).frames[0]
-        >>> export_to_video(video, "video2world.mp4", fps=16)
-
-        >>> # To produce an image instead of a world (video) clip, set num_frames=1 and
-        >>> # save the first frame: pipe(..., num_frames=1).frames[0][0].
-        ```
-"""
-
-
-class Cosmos2_5_PredictBasePipeline(DiffusionPipeline):
-    r"""
-    Pipeline for [Cosmos Predict2.5](https://github.com/nvidia-cosmos/cosmos-predict2.5) base model.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        text_encoder ([`Qwen2_5_VLForConditionalGeneration`]):
-            Frozen text-encoder. Cosmos Predict2.5 uses the [Qwen2.5
-            VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) encoder.
-        tokenizer (`AutoTokenizer`):
-            Tokenizer associated with the Qwen2.5 VL encoder.
-        transformer ([`CosmosTransformer3DModel`]):
-            Conditional Transformer to denoise the encoded image latents.
-        scheduler ([`UniPCMultistepScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKLWan`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
-    """
-
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
-    # We mark safety_checker as optional here to get around some test failures, but it is not really optional
-    _optional_components = ["safety_checker"]
-    _exclude_from_cpu_offload = ["safety_checker"]
-
-    def __init__(
-        self,
-        text_encoder: Qwen2_5_VLForConditionalGeneration,
-        tokenizer: AutoTokenizer,
-        transformer: CosmosTransformer3DModel,
-        vae: AutoencoderKLWan,
-        scheduler: UniPCMultistepScheduler,
-        safety_checker: CosmosSafetyChecker = None,
-    ):
-        super().__init__()
-
-        if safety_checker is None:
-            safety_checker = CosmosSafetyChecker()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            transformer=transformer,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-        )
-
-        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
-        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
-        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-
-        latents_mean = (
-            torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1, 1).float()
-            if getattr(self.vae.config, "latents_mean", None) is not None
-            else None
-        )
-        latents_std = (
-            torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).float()
-            if getattr(self.vae.config, "latents_std", None) is not None
-            else None
-        )
-        self.latents_mean = latents_mean
-        self.latents_std = latents_std
-
-        if self.latents_mean is None or self.latents_std is None:
-            raise ValueError("VAE configuration must define both `latents_mean` and `latents_std`.")
-
-    def _get_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        max_sequence_length: int = 512,
-        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
-
-        input_ids_batch = []
-
-        for sample_idx in range(len(prompt)):
-            conversations = [
-                {
-                    "role": "system",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": "You are a helpful assistant who will provide prompts to an image generator.",
-                        }
-                    ],
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": prompt[sample_idx],
-                        }
-                    ],
-                },
-            ]
-            input_ids = self.tokenizer.apply_chat_template(
-                conversations,
-                tokenize=True,
-                add_generation_prompt=False,
-                add_vision_id=False,
-                max_length=max_sequence_length,
-                truncation=True,
-                padding="max_length",
-            )
-            input_ids = torch.LongTensor(input_ids)
-            input_ids_batch.append(input_ids)
-
-        input_ids_batch = torch.stack(input_ids_batch, dim=0)
-
-        outputs = self.text_encoder(
-            input_ids_batch.to(device),
-            output_hidden_states=True,
-        )
-        hidden_states = outputs.hidden_states
-
-        normalized_hidden_states = []
-        for layer_idx in range(1, len(hidden_states)):
-            normalized_state = (hidden_states[layer_idx] - hidden_states[layer_idx].mean(dim=-1, keepdim=True)) / (
-                hidden_states[layer_idx].std(dim=-1, keepdim=True) + 1e-8
-            )
-            normalized_hidden_states.append(normalized_state)
-
-        prompt_embeds = torch.cat(normalized_hidden_states, dim=-1)
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        return prompt_embeds
-
-    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = True,
-        num_videos_per_prompt: int = 1,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        max_sequence_length: int = 512,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                Whether to use classifier free guidance or not.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
-            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.
-            device: (`torch.device`, *optional*):
-                torch device
-            dtype: (`torch.dtype`, *optional*):
-                torch dtype
-        """
-        device = device or self._execution_device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_embeds = self._get_prompt_embeds(
-                prompt=prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype
-            )
-
-            # duplicate text embeddings for each generation per prompt, using mps friendly method
-            _, seq_len, _ = prompt_embeds.shape
-            prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-            prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds = self._get_prompt_embeds(
-                prompt=negative_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype
-            )
-
-            # duplicate text embeddings for each generation per prompt, using mps friendly method
-            _, seq_len, _ = negative_prompt_embeds.shape
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos2_video2world.Cosmos2VideoToWorldPipeline.prepare_latents and
-    # diffusers.pipelines.cosmos.pipeline_cosmos2_video2world.Cosmos2TextToImagePipeline.prepare_latents
-    def prepare_latents(
-        self,
-        video: Optional[torch.Tensor],
-        batch_size: int,
-        num_channels_latents: int = 16,
-        height: int = 704,
-        width: int = 1280,
-        num_frames_in: int = 93,
-        num_frames_out: int = 93,
-        do_classifier_free_guidance: bool = True,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        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."
-            )
-
-        B = batch_size
-        C = num_channels_latents
-        T = (num_frames_out - 1) // self.vae_scale_factor_temporal + 1
-        H = height // self.vae_scale_factor_spatial
-        W = width // self.vae_scale_factor_spatial
-        shape = (B, C, T, H, W)
-
-        if num_frames_in == 0:
-            if latents is None:
-                latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-            cond_mask = torch.zeros((B, 1, T, H, W), dtype=latents.dtype, device=latents.device)
-            cond_indicator = torch.zeros((B, 1, T, 1, 1), dtype=latents.dtype, device=latents.device)
-
-            cond_latents = torch.zeros_like(latents)
-
-            return (
-                latents,
-                cond_latents,
-                cond_mask,
-                cond_indicator,
-            )
-        else:
-            if video is None:
-                raise ValueError("`video` must be provided when `num_frames_in` is greater than 0.")
-            needs_preprocessing = not (isinstance(video, torch.Tensor) and video.ndim == 5 and video.shape[1] == 3)
-            if needs_preprocessing:
-                video = self.video_processor.preprocess_video(video, height, width)
-            video = video.to(device=device, dtype=self.vae.dtype)
-            if isinstance(generator, list):
-                cond_latents = [
-                    retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator=generator[i])
-                    for i in range(batch_size)
-                ]
-            else:
-                cond_latents = [retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator) for vid in video]
-
-            cond_latents = torch.cat(cond_latents, dim=0).to(dtype)
-
-            latents_mean = self.latents_mean.to(device=device, dtype=dtype)
-            latents_std = self.latents_std.to(device=device, dtype=dtype)
-            cond_latents = (cond_latents - latents_mean) / latents_std
-
-            if latents is None:
-                latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            else:
-                latents = latents.to(device=device, dtype=dtype)
-
-            padding_shape = (B, 1, T, H, W)
-            ones_padding = latents.new_ones(padding_shape)
-            zeros_padding = latents.new_zeros(padding_shape)
-
-            num_cond_latent_frames = (num_frames_in - 1) // self.vae_scale_factor_temporal + 1
-            cond_indicator = latents.new_zeros(1, 1, latents.size(2), 1, 1)
-            cond_indicator[:, :, 0:num_cond_latent_frames] = 1.0
-            cond_mask = cond_indicator * ones_padding + (1 - cond_indicator) * zeros_padding
-
-            return (
-                latents,
-                cond_latents,
-                cond_mask,
-                cond_indicator,
-            )
-
-    # Copied from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        prompt_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-    ):
-        if height % 16 != 0 or width % 16 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
-
-        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)}")
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0
-
-    @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: PipelineImageInput | None = None,
-        video: List[PipelineImageInput] | None = None,
-        prompt: Union[str, List[str]] | None = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        height: int = 704,
-        width: int = 1280,
-        num_frames: int = 93,
-        num_inference_steps: int = 36,
-        guidance_scale: float = 7.0,
-        num_videos_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback_on_step_end: Optional[
-            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
-        ] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 512,
-        conditional_frame_timestep: float = 0.1,
-    ):
-        r"""
-        The call function to the pipeline for generation. Supports three modes:
-
-        - **Text2World**: `image=None`, `video=None`, `prompt` provided. Generates a world clip.
-        - **Image2World**: `image` provided, `video=None`, `prompt` provided. Conditions on a single frame.
-        - **Video2World**: `video` provided, `image=None`, `prompt` provided. Conditions on an input clip.
-
-        Set `num_frames=93` (default) to produce a world video, or `num_frames=1` to produce a single image frame (the
-        above in "*2Image mode").
-
-        Outputs follow `output_type` (e.g., `"pil"` returns a list of `num_frames` PIL images per prompt).
-
-        Args:
-            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, *optional*):
-                Optional single image for Image2World conditioning. Must be `None` when `video` is provided.
-            video (`List[PIL.Image.Image]`, `np.ndarray`, `torch.Tensor`, *optional*):
-                Optional input video for Video2World conditioning. Must be `None` when `image` is provided.
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide generation. Required unless `prompt_embeds` is supplied.
-            height (`int`, defaults to `704`):
-                The height in pixels of the generated image.
-            width (`int`, defaults to `1280`):
-                The width in pixels of the generated image.
-            num_frames (`int`, defaults to `93`):
-                Number of output frames. Use `93` for world (video) generation; set to `1` to return a single frame.
-            num_inference_steps (`int`, defaults to `35`):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, defaults to `7.0`):
-                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`.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](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 is 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.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". 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 generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`CosmosPipelineOutput`] instead of a plain tuple.
-            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
-                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
-                each denoising step during the inference. 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`):
-                The maximum number of tokens in the prompt. If the prompt exceeds this length, it will be truncated. If
-                the prompt is shorter than this length, it will be padded.
-
-        Examples:
-
-        Returns:
-            [`~CosmosPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`CosmosPipelineOutput`] is returned, otherwise a `tuple` is returned where
-                the first element is a list with the generated images and the second element is a list of `bool`s
-                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
-        """
-        if self.safety_checker is None:
-            raise ValueError(
-                f"You have disabled the safety checker for {self.__class__}. This is in violation of the "
-                "[NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). "
-                f"Please ensure that you are compliant with the license agreement."
-            )
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
-        # Check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, prompt_embeds, callback_on_step_end_tensor_inputs)
-
-        self._guidance_scale = guidance_scale
-        self._current_timestep = None
-        self._interrupt = False
-
-        device = self._execution_device
-
-        if self.safety_checker is not None:
-            self.safety_checker.to(device)
-            if prompt is not None:
-                prompt_list = [prompt] if isinstance(prompt, str) else prompt
-                for p in prompt_list:
-                    if not self.safety_checker.check_text_safety(p):
-                        raise ValueError(
-                            f"Cosmos Guardrail detected unsafe text in the prompt: {p}. Please ensure that the "
-                            f"prompt abides by the NVIDIA Open Model License Agreement."
-                        )
-
-        # 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]
-
-        # Encode input prompt
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            num_videos_per_prompt=num_videos_per_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            device=device,
-            max_sequence_length=max_sequence_length,
-        )
-
-        vae_dtype = self.vae.dtype
-        transformer_dtype = self.transformer.dtype
-
-        num_frames_in = None
-        if image is not None:
-            if batch_size != 1:
-                raise ValueError(f"batch_size must be 1 for image input (given {batch_size})")
-
-            image = torchvision.transforms.functional.to_tensor(image).unsqueeze(0)
-            video = torch.cat([image, torch.zeros_like(image).repeat(num_frames - 1, 1, 1, 1)], dim=0)
-            video = video.unsqueeze(0)
-            num_frames_in = 1
-        elif video is None:
-            video = torch.zeros(batch_size, num_frames, 3, height, width, dtype=torch.uint8)
-            num_frames_in = 0
-        else:
-            num_frames_in = len(video)
-
-            if batch_size != 1:
-                raise ValueError(f"batch_size must be 1 for video input (given {batch_size})")
-
-        assert video is not None
-        video = self.video_processor.preprocess_video(video, height, width)
-
-        # pad with last frame (for video2world)
-        num_frames_out = num_frames
-        if video.shape[2] < num_frames_out:
-            n_pad_frames = num_frames_out - num_frames_in
-            last_frame = video[0, :, -1:, :, :]  # [C, T==1, H, W]
-            pad_frames = last_frame.repeat(1, 1, n_pad_frames, 1, 1)  # [B, C, T, H, W]
-            video = torch.cat((video, pad_frames), dim=2)
-
-        assert num_frames_in <= num_frames_out, f"expected ({num_frames_in=}) <= ({num_frames_out=})"
-
-        video = video.to(device=device, dtype=vae_dtype)
-
-        num_channels_latents = self.transformer.config.in_channels - 1
-        latents, cond_latent, cond_mask, cond_indicator = self.prepare_latents(
-            video=video,
-            batch_size=batch_size * num_videos_per_prompt,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            num_frames_in=num_frames_in,
-            num_frames_out=num_frames,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            dtype=torch.float32,
-            device=device,
-            generator=generator,
-            latents=latents,
-        )
-        cond_timestep = torch.ones_like(cond_indicator) * conditional_frame_timestep
-        cond_mask = cond_mask.to(transformer_dtype)
-
-        padding_mask = latents.new_zeros(1, 1, height, width, dtype=transformer_dtype)
-
-        # Denoising loop
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-        self._num_timesteps = len(timesteps)
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-
-        gt_velocity = (latents - cond_latent) * cond_mask
-        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.cpu().item()
-
-                # NOTE: assumes sigma(t) \in [0, 1]
-                sigma_t = (
-                    torch.tensor(self.scheduler.sigmas[i].item())
-                    .unsqueeze(0)
-                    .to(device=device, dtype=transformer_dtype)
-                )
-
-                in_latents = cond_mask * cond_latent + (1 - cond_mask) * latents
-                in_latents = in_latents.to(transformer_dtype)
-                in_timestep = cond_indicator * cond_timestep + (1 - cond_indicator) * sigma_t
-                noise_pred = self.transformer(
-                    hidden_states=in_latents,
-                    condition_mask=cond_mask,
-                    timestep=in_timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    padding_mask=padding_mask,
-                    return_dict=False,
-                )[0]
-                # NOTE: replace velocity (noise_pred) with gt_velocity for conditioning inputs only
-                noise_pred = gt_velocity + noise_pred * (1 - cond_mask)
-
-                if self.do_classifier_free_guidance:
-                    noise_pred_neg = self.transformer(
-                        hidden_states=in_latents,
-                        condition_mask=cond_mask,
-                        timestep=in_timestep,
-                        encoder_hidden_states=negative_prompt_embeds,
-                        padding_mask=padding_mask,
-                        return_dict=False,
-                    )[0]
-                    # NOTE: replace velocity (noise_pred_neg) with gt_velocity for conditioning inputs only
-                    noise_pred_neg = gt_velocity + noise_pred_neg * (1 - cond_mask)
-                    noise_pred = noise_pred + self.guidance_scale * (noise_pred - noise_pred_neg)
-
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-                if XLA_AVAILABLE:
-                    xm.mark_step()
-
-        self._current_timestep = None
-
-        if not output_type == "latent":
-            latents_mean = self.latents_mean.to(latents.device, latents.dtype)
-            latents_std = self.latents_std.to(latents.device, latents.dtype)
-            latents = latents * latents_std + latents_mean
-            video = self.vae.decode(latents.to(self.vae.dtype), return_dict=False)[0]
-            video = self._match_num_frames(video, num_frames)
-
-            assert self.safety_checker is not None
-            self.safety_checker.to(device)
-            video = self.video_processor.postprocess_video(video, output_type="np")
-            video = (video * 255).astype(np.uint8)
-            video_batch = []
-            for vid in video:
-                vid = self.safety_checker.check_video_safety(vid)
-                video_batch.append(vid)
-            video = np.stack(video_batch).astype(np.float32) / 255.0 * 2 - 1
-            video = torch.from_numpy(video).permute(0, 4, 1, 2, 3)
-            video = self.video_processor.postprocess_video(video, output_type=output_type)
-        else:
-            video = latents
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (video,)
-
-        return CosmosPipelineOutput(frames=video)
-
-    def _match_num_frames(self, video: torch.Tensor, target_num_frames: int) -> torch.Tensor:
-        if target_num_frames <= 0 or video.shape[2] == target_num_frames:
-            return video
-
-        frames_per_latent = max(self.vae_scale_factor_temporal, 1)
-        video = torch.repeat_interleave(video, repeats=frames_per_latent, dim=2)
-
-        current_frames = video.shape[2]
-        if current_frames < target_num_frames:
-            pad = video[:, :, -1:, :, :].repeat(1, 1, target_num_frames - current_frames, 1, 1)
-            video = torch.cat([video, pad], dim=2)
-        elif current_frames > target_num_frames:
-            video = video[:, :, :target_num_frames]
-
-        return video

src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py

@@ -17,7 +17,7 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
-from transformers import BertModel, BertTokenizer, CLIPImageProcessor, T5EncoderModel, T5Tokenizer
+from transformers import BertModel, BertTokenizer, CLIPImageProcessor, MT5Tokenizer, T5EncoderModel
 
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 
@@ -169,7 +169,7 @@ class HunyuanDiTPipeline(DiffusionPipeline):
             The HunyuanDiT model designed by Tencent Hunyuan.
         text_encoder_2 (`T5EncoderModel`):
             The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
-        tokenizer_2 (`T5Tokenizer`):
+        tokenizer_2 (`MT5Tokenizer`):
             The tokenizer for the mT5 embedder.
         scheduler ([`DDPMScheduler`]):
             A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
@@ -204,7 +204,7 @@ class HunyuanDiTPipeline(DiffusionPipeline):
         feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
         text_encoder_2: Optional[T5EncoderModel] = None,
-        tokenizer_2: Optional[T5Tokenizer] = None,
+        tokenizer_2: Optional[MT5Tokenizer] = None,
     ):
         super().__init__()
 

src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py

@@ -17,7 +17,7 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
-from transformers import BertModel, BertTokenizer, CLIPImageProcessor, T5EncoderModel, T5Tokenizer
+from transformers import BertModel, BertTokenizer, CLIPImageProcessor, MT5Tokenizer, T5EncoderModel
 
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 
@@ -173,7 +173,7 @@ class HunyuanDiTPAGPipeline(DiffusionPipeline, PAGMixin):
             The HunyuanDiT model designed by Tencent Hunyuan.
         text_encoder_2 (`T5EncoderModel`):
             The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
-        tokenizer_2 (`T5Tokenizer`):
+        tokenizer_2 (`MT5Tokenizer`):
             The tokenizer for the mT5 embedder.
         scheduler ([`DDPMScheduler`]):
             A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
@@ -208,7 +208,7 @@ class HunyuanDiTPAGPipeline(DiffusionPipeline, PAGMixin):
         feature_extractor: Optional[CLIPImageProcessor] = None,
         requires_safety_checker: bool = True,
         text_encoder_2: Optional[T5EncoderModel] = None,
-        tokenizer_2: Optional[T5Tokenizer] = None,
+        tokenizer_2: Optional[MT5Tokenizer] = None,
         pag_applied_layers: Union[str, List[str]] = "blocks.1",  # "blocks.16.attn1", "blocks.16", "16", 16
     ):
         super().__init__()

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py

@@ -882,24 +882,21 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
             latents = latents / latents_std + latents_mean
 
             b, c, f, h, w = latents.shape
-
             latents = latents[:, :, 1:]  # remove the first frame as it is the orgin input
-
             latents = latents.permute(0, 2, 1, 3, 4).view(-1, c, 1, h, w)
 
-            image = self.vae.decode(latents, return_dict=False)[0]  # (b f) c 1 h w
+            img = self.vae.decode(latents, return_dict=False)[0]  # (b f) c 1 h w
+            img = img.squeeze(2)
 
-            image = image.squeeze(2)
-
-            image = self.image_processor.postprocess(image, output_type=output_type)
-            images = []
+            img = self.image_processor.postprocess(img, output_type=output_type)
+            image = []
             for bidx in range(b):
-                images.append(image[bidx * f : (bidx + 1) * f])
+                image.append(img[bidx * f : (bidx + 1) * f])
 
         # Offload all models
         self.maybe_free_model_hooks()
 
         if not return_dict:
-            return (images,)
+            return (image,)
 
-        return QwenImagePipelineOutput(images=images)
+        return QwenImagePipelineOutput(images=image)

src/diffusers/pipelines/z_image/__init__.py

@@ -26,7 +26,6 @@ else:
     _import_structure["pipeline_z_image_controlnet"] = ["ZImageControlNetPipeline"]
     _import_structure["pipeline_z_image_controlnet_inpaint"] = ["ZImageControlNetInpaintPipeline"]
     _import_structure["pipeline_z_image_img2img"] = ["ZImageImg2ImgPipeline"]
-    _import_structure["pipeline_z_image_omni"] = ["ZImageOmniPipeline"]
 
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -42,7 +41,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_z_image_controlnet import ZImageControlNetPipeline
         from .pipeline_z_image_controlnet_inpaint import ZImageControlNetInpaintPipeline
         from .pipeline_z_image_img2img import ZImageImg2ImgPipeline
-        from .pipeline_z_image_omni import ZImageOmniPipeline
+
 else:
     import sys
 

src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py

@@ -58,13 +58,14 @@ EXAMPLE_DOC_STRING = """
         >>> #     torch_dtype=torch.bfloat16,
         >>> # )
 
-        >>> # 2.0
+        >>> # 2.0 - `config` is required
         >>> # controlnet = ZImageControlNetModel.from_single_file(
         >>> #     hf_hub_download(
         >>> #         "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0",
         >>> #         filename="Z-Image-Turbo-Fun-Controlnet-Union-2.0.safetensors",
         >>> #     ),
         >>> #     torch_dtype=torch.bfloat16,
+        >>> #     config="hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0",
         >>> # )
 
         >>> pipe = ZImageControlNetPipeline.from_pretrained(

src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py

@@ -50,13 +50,14 @@ EXAMPLE_DOC_STRING = """
         ...     torch_dtype=torch.bfloat16,
         ... )
 
-        >>> # 2.0
+        >>> # 2.0 - `config` is required
         >>> # controlnet = ZImageControlNetModel.from_single_file(
         >>> #     hf_hub_download(
         >>> #         "alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0",
         >>> #         filename="Z-Image-Turbo-Fun-Controlnet-Union-2.0.safetensors",
         >>> #     ),
         >>> #     torch_dtype=torch.bfloat16,
+        >>> #     config="hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0",
         >>> # )
 
         >>> pipe = ZImageControlNetInpaintPipeline.from_pretrained(

src/diffusers/pipelines/z_image/pipeline_z_image_omni.py

@@ -1,742 +0,0 @@
-# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import PIL
-import torch
-from transformers import AutoTokenizer, PreTrainedModel, Siglip2ImageProcessorFast, Siglip2VisionModel
-
-from ...loaders import FromSingleFileMixin, ZImageLoraLoaderMixin
-from ...models.autoencoders import AutoencoderKL
-from ...models.transformers import ZImageTransformer2DModel
-from ...pipelines.pipeline_utils import DiffusionPipeline
-from ...schedulers import FlowMatchEulerDiscreteScheduler
-from ...utils import logging, replace_example_docstring
-from ...utils.torch_utils import randn_tensor
-from ..flux2.image_processor import Flux2ImageProcessor
-from .pipeline_output import ZImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import ZImageOmniPipeline
-
-        >>> pipe = ZImageOmniPipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
-        >>> pipe.to("cuda")
-
-        >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
-        >>> # (1) Use flash attention 2
-        >>> # pipe.transformer.set_attention_backend("flash")
-        >>> # (2) Use flash attention 3
-        >>> # pipe.transformer.set_attention_backend("_flash_3")
-
-        >>> prompt = "一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。"
-        >>> image = pipe(
-        ...     prompt,
-        ...     height=1024,
-        ...     width=1024,
-        ...     num_inference_steps=9,
-        ...     guidance_scale=0.0,
-        ...     generator=torch.Generator("cuda").manual_seed(42),
-        ... ).images[0]
-        >>> image.save("zimage.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-    b = base_shift - m * base_seq_len
-    mu = image_seq_len * m + b
-    return mu
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.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 ZImageOmniPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds"]
-
-    def __init__(
-        self,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKL,
-        text_encoder: PreTrainedModel,
-        tokenizer: AutoTokenizer,
-        transformer: ZImageTransformer2DModel,
-        siglip: Siglip2VisionModel,
-        siglip_processor: Siglip2ImageProcessorFast,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            transformer=transformer,
-            siglip=siglip,
-            siglip_processor=siglip_processor,
-        )
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
-        )
-        # self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
-        self.image_processor = Flux2ImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
-
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        max_sequence_length: int = 512,
-        num_condition_images: int = 0,
-    ):
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        prompt_embeds = self._encode_prompt(
-            prompt=prompt,
-            device=device,
-            prompt_embeds=prompt_embeds,
-            max_sequence_length=max_sequence_length,
-            num_condition_images=num_condition_images,
-        )
-
-        if do_classifier_free_guidance:
-            if negative_prompt is None:
-                negative_prompt = ["" for _ in prompt]
-            else:
-                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-            assert len(prompt) == len(negative_prompt)
-            negative_prompt_embeds = self._encode_prompt(
-                prompt=negative_prompt,
-                device=device,
-                prompt_embeds=negative_prompt_embeds,
-                max_sequence_length=max_sequence_length,
-                num_condition_images=num_condition_images,
-            )
-        else:
-            negative_prompt_embeds = []
-        return prompt_embeds, negative_prompt_embeds
-
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        max_sequence_length: int = 512,
-        num_condition_images: int = 0,
-    ) -> List[torch.FloatTensor]:
-        device = device or self._execution_device
-
-        if prompt_embeds is not None:
-            return prompt_embeds
-
-        if isinstance(prompt, str):
-            prompt = [prompt]
-
-        for i, prompt_item in enumerate(prompt):
-            if num_condition_images == 0:
-                prompt[i] = ["<|im_start|>user\n" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n"]
-            elif num_condition_images > 0:
-                prompt_list = ["<|im_start|>user\n<|vision_start|>"]
-                prompt_list += ["<|vision_end|><|vision_start|>"] * (num_condition_images - 1)
-                prompt_list += ["<|vision_end|>" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n<|vision_start|>"]
-                prompt_list += ["<|vision_end|><|im_end|>"]
-                prompt[i] = prompt_list
-
-        flattened_prompt = []
-        prompt_list_lengths = []
-
-        for i in range(len(prompt)):
-            prompt_list_lengths.append(len(prompt[i]))
-            flattened_prompt.extend(prompt[i])
-
-        text_inputs = self.tokenizer(
-            flattened_prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids.to(device)
-        prompt_masks = text_inputs.attention_mask.to(device).bool()
-
-        prompt_embeds = self.text_encoder(
-            input_ids=text_input_ids,
-            attention_mask=prompt_masks,
-            output_hidden_states=True,
-        ).hidden_states[-2]
-
-        embeddings_list = []
-        start_idx = 0
-        for i in range(len(prompt_list_lengths)):
-            batch_embeddings = []
-            end_idx = start_idx + prompt_list_lengths[i]
-            for j in range(start_idx, end_idx):
-                batch_embeddings.append(prompt_embeds[j][prompt_masks[j]])
-            embeddings_list.append(batch_embeddings)
-            start_idx = end_idx
-
-        return embeddings_list
-
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-    ):
-        height = 2 * (int(height) // (self.vae_scale_factor * 2))
-        width = 2 * (int(width) // (self.vae_scale_factor * 2))
-
-        shape = (batch_size, num_channels_latents, height, width)
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-        return latents
-
-    def prepare_image_latents(
-        self,
-        images: List[torch.Tensor],
-        batch_size,
-        device,
-        dtype,
-    ):
-        image_latents = []
-        for image in images:
-            image = image.to(device=device, dtype=dtype)
-            image_latent = (
-                self.vae.encode(image.bfloat16()).latent_dist.mode()[0] - self.vae.config.shift_factor
-            ) * self.vae.config.scaling_factor
-            image_latent = image_latent.unsqueeze(1).to(dtype)
-            image_latents.append(image_latent)  # (16, 128, 128)
-
-        # image_latents = [image_latents] * batch_size
-        image_latents = [image_latents.copy() for _ in range(batch_size)]
-
-        return image_latents
-
-    def prepare_siglip_embeds(
-        self,
-        images: List[torch.Tensor],
-        batch_size,
-        device,
-        dtype,
-    ):
-        siglip_embeds = []
-        for image in images:
-            siglip_inputs = self.siglip_processor(images=[image], return_tensors="pt").to(device)
-            shape = siglip_inputs.spatial_shapes[0]
-            hidden_state = self.siglip(**siglip_inputs).last_hidden_state
-            B, N, C = hidden_state.shape
-            hidden_state = hidden_state[:, : shape[0] * shape[1]]
-            hidden_state = hidden_state.view(shape[0], shape[1], C)
-            siglip_embeds.append(hidden_state.to(dtype))
-
-        # siglip_embeds = [siglip_embeds] * batch_size
-        siglip_embeds = [siglip_embeds.copy() for _ in range(batch_size)]
-
-        return siglip_embeds
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def joint_attention_kwargs(self):
-        return self._joint_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image: Optional[Union[List[PIL.Image.Image], PIL.Image.Image]] = None,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        sigmas: Optional[List[float]] = None,
-        guidance_scale: float = 5.0,
-        cfg_normalization: bool = False,
-        cfg_truncation: float = 1.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 512,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            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.
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            height (`int`, *optional*, defaults to 1024):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 1024):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            sigmas (`List[float]`, *optional*):
-                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
-                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
-                will be used.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            cfg_normalization (`bool`, *optional*, defaults to False):
-                Whether to apply configuration normalization.
-            cfg_truncation (`float`, *optional*, defaults to 1.0):
-                The truncation value for configuration.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will be generated by sampling using the supplied random `generator`.
-            prompt_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain
-                tuple.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int`, *optional*, defaults to 512):
-                Maximum sequence length to use with the `prompt`.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-
-        if image is not None and not isinstance(image, list):
-            image = [image]
-        num_condition_images = len(image) if image is not None else 0
-
-        device = self._execution_device
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-        self._cfg_normalization = cfg_normalization
-        self._cfg_truncation = cfg_truncation
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = len(prompt_embeds)
-
-        # If prompt_embeds is provided and prompt is None, skip encoding
-        if prompt_embeds is not None and prompt is None:
-            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
-                raise ValueError(
-                    "When `prompt_embeds` is provided without `prompt`, "
-                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
-                )
-        else:
-            (
-                prompt_embeds,
-                negative_prompt_embeds,
-            ) = self.encode_prompt(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-                prompt_embeds=prompt_embeds,
-                negative_prompt_embeds=negative_prompt_embeds,
-                device=device,
-                max_sequence_length=max_sequence_length,
-                num_condition_images=num_condition_images,
-            )
-
-        # 3. Process condition images. Copied from diffusers.pipelines.flux2.pipeline_flux2
-        condition_images = []
-        resized_images = []
-        if image is not None:
-            for img in image:
-                self.image_processor.check_image_input(img)
-            for img in image:
-                image_width, image_height = img.size
-                if image_width * image_height > 1024 * 1024:
-                    if height is not None and width is not None:
-                        img = self.image_processor._resize_to_target_area(img, height * width)
-                    else:
-                        img = self.image_processor._resize_to_target_area(img, 1024 * 1024)
-                    image_width, image_height = img.size
-                resized_images.append(img)
-
-                multiple_of = self.vae_scale_factor * 2
-                image_width = (image_width // multiple_of) * multiple_of
-                image_height = (image_height // multiple_of) * multiple_of
-                img = self.image_processor.preprocess(img, height=image_height, width=image_width, resize_mode="crop")
-                condition_images.append(img)
-
-            if len(condition_images) > 0:
-                height = height or image_height
-                width = width or image_width
-
-        else:
-            height = height or 1024
-            width = width or 1024
-
-        vae_scale = self.vae_scale_factor * 2
-        if height % vae_scale != 0:
-            raise ValueError(
-                f"Height must be divisible by {vae_scale} (got {height}). "
-                f"Please adjust the height to a multiple of {vae_scale}."
-            )
-        if width % vae_scale != 0:
-            raise ValueError(
-                f"Width must be divisible by {vae_scale} (got {width}). "
-                f"Please adjust the width to a multiple of {vae_scale}."
-            )
-
-        # 4. Prepare latent variables
-        num_channels_latents = self.transformer.in_channels
-
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            torch.float32,
-            device,
-            generator,
-            latents,
-        )
-
-        condition_latents = self.prepare_image_latents(
-            images=condition_images,
-            batch_size=batch_size * num_images_per_prompt,
-            device=device,
-            dtype=torch.float32,
-        )
-        condition_latents = [[lat.to(self.transformer.dtype) for lat in lats] for lats in condition_latents]
-        if self.do_classifier_free_guidance:
-            negative_condition_latents = [[lat.clone() for lat in batch] for batch in condition_latents]
-
-        condition_siglip_embeds = self.prepare_siglip_embeds(
-            images=resized_images,
-            batch_size=batch_size * num_images_per_prompt,
-            device=device,
-            dtype=torch.float32,
-        )
-        condition_siglip_embeds = [[se.to(self.transformer.dtype) for se in sels] for sels in condition_siglip_embeds]
-        if self.do_classifier_free_guidance:
-            negative_condition_siglip_embeds = [[se.clone() for se in batch] for batch in condition_siglip_embeds]
-
-        # Repeat prompt_embeds for num_images_per_prompt
-        if num_images_per_prompt > 1:
-            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
-            if self.do_classifier_free_guidance and negative_prompt_embeds:
-                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
-
-        condition_siglip_embeds = [None if sels == [] else sels + [None] for sels in condition_siglip_embeds]
-        negative_condition_siglip_embeds = [
-            None if sels == [] else sels + [None] for sels in negative_condition_siglip_embeds
-        ]
-
-        actual_batch_size = batch_size * num_images_per_prompt
-        image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
-
-        # 5. Prepare timesteps
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.get("base_image_seq_len", 256),
-            self.scheduler.config.get("max_image_seq_len", 4096),
-            self.scheduler.config.get("base_shift", 0.5),
-            self.scheduler.config.get("max_shift", 1.15),
-        )
-        self.scheduler.sigma_min = 0.0
-        scheduler_kwargs = {"mu": mu}
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            sigmas=sigmas,
-            **scheduler_kwargs,
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 6. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latents.shape[0])
-                timestep = (1000 - timestep) / 1000
-                # Normalized time for time-aware config (0 at start, 1 at end)
-                t_norm = timestep[0].item()
-
-                # Handle cfg truncation
-                current_guidance_scale = self.guidance_scale
-                if (
-                    self.do_classifier_free_guidance
-                    and self._cfg_truncation is not None
-                    and float(self._cfg_truncation) <= 1
-                ):
-                    if t_norm > self._cfg_truncation:
-                        current_guidance_scale = 0.0
-
-                # Run CFG only if configured AND scale is non-zero
-                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
-
-                if apply_cfg:
-                    latents_typed = latents.to(self.transformer.dtype)
-                    latent_model_input = latents_typed.repeat(2, 1, 1, 1)
-                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
-                    condition_latents_model_input = condition_latents + negative_condition_latents
-                    condition_siglip_embeds_model_input = condition_siglip_embeds + negative_condition_siglip_embeds
-                    timestep_model_input = timestep.repeat(2)
-                else:
-                    latent_model_input = latents.to(self.transformer.dtype)
-                    prompt_embeds_model_input = prompt_embeds
-                    condition_latents_model_input = condition_latents
-                    condition_siglip_embeds_model_input = condition_siglip_embeds
-                    timestep_model_input = timestep
-
-                latent_model_input = latent_model_input.unsqueeze(2)
-                latent_model_input_list = list(latent_model_input.unbind(dim=0))
-
-                # Combine condition latents with target latent
-                current_batch_size = len(latent_model_input_list)
-                x_combined = [
-                    condition_latents_model_input[i] + [latent_model_input_list[i]] for i in range(current_batch_size)
-                ]
-                # Create noise mask: 0 for condition images (clean), 1 for target image (noisy)
-                image_noise_mask = [
-                    [0] * len(condition_latents_model_input[i]) + [1] for i in range(current_batch_size)
-                ]
-
-                model_out_list = self.transformer(
-                    x=x_combined,
-                    t=timestep_model_input,
-                    cap_feats=prompt_embeds_model_input,
-                    siglip_feats=condition_siglip_embeds_model_input,
-                    image_noise_mask=image_noise_mask,
-                    return_dict=False,
-                )[0]
-
-                if apply_cfg:
-                    # Perform CFG
-                    pos_out = model_out_list[:actual_batch_size]
-                    neg_out = model_out_list[actual_batch_size:]
-
-                    noise_pred = []
-                    for j in range(actual_batch_size):
-                        pos = pos_out[j].float()
-                        neg = neg_out[j].float()
-
-                        pred = pos + current_guidance_scale * (pos - neg)
-
-                        # Renormalization
-                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
-                            ori_pos_norm = torch.linalg.vector_norm(pos)
-                            new_pos_norm = torch.linalg.vector_norm(pred)
-                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
-                            if new_pos_norm > max_new_norm:
-                                pred = pred * (max_new_norm / new_pos_norm)
-
-                        noise_pred.append(pred)
-
-                    noise_pred = torch.stack(noise_pred, dim=0)
-                else:
-                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
-
-                noise_pred = noise_pred.squeeze(2)
-                noise_pred = -noise_pred
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
-                assert latents.dtype == torch.float32
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if output_type == "latent":
-            image = latents
-
-        else:
-            latents = latents.to(self.vae.dtype)
-            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-
-            image = self.vae.decode(latents, return_dict=False)[0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return ZImagePipelineOutput(images=image)

src/diffusers/quantizers/torchao/torchao_quantizer.py

@@ -36,9 +36,6 @@ from ...utils import (
 from ..base import DiffusersQuantizer
 
 
-logger = logging.get_logger(__name__)
-
-
 if TYPE_CHECKING:
     from ...models.modeling_utils import ModelMixin
 
@@ -86,19 +83,11 @@ def _update_torch_safe_globals():
     ]
     try:
         from torchao.dtypes import NF4Tensor
+        from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl
+        from torchao.dtypes.uintx.uint4_layout import UInt4Tensor
         from torchao.dtypes.uintx.uintx_layout import UintxAQTTensorImpl, UintxTensor
 
-        safe_globals.extend([UintxTensor, UintxAQTTensorImpl, NF4Tensor])
-
-        # note: is_torchao_version(">=", "0.16.0") does not work correctly
-        # with torchao nightly, so using a ">" check which does work correctly
-        if is_torchao_version(">", "0.15.0"):
-            pass
-        else:
-            from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl
-            from torchao.dtypes.uintx.uint4_layout import UInt4Tensor
-
-            safe_globals.extend([UInt4Tensor, Float8AQTTensorImpl])
+        safe_globals.extend([UintxTensor, UInt4Tensor, UintxAQTTensorImpl, Float8AQTTensorImpl, NF4Tensor])
 
     except (ImportError, ModuleNotFoundError) as e:
         logger.warning(
@@ -134,6 +123,9 @@ def fuzzy_match_size(config_name: str) -> Optional[str]:
     return None
 
 
+logger = logging.get_logger(__name__)
+
+
 def _quantization_type(weight):
     from torchao.dtypes import AffineQuantizedTensor
     from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -217,8 +217,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         rescale_betas_zero_snr: bool = False,
         use_dynamic_shifting: bool = False,
         time_shift_type: Literal["exponential"] = "exponential",
-        sigma_min: Optional[float] = None,
-        sigma_max: Optional[float] = None,
     ) -> None:
         if self.config.use_beta_sigmas and not is_scipy_available():
             raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
@@ -352,12 +350,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
-            if self.config.use_flow_sigmas:
-                sigmas = sigmas / (sigmas + 1)
-                timesteps = (sigmas * self.config.num_train_timesteps).copy()
-            else:
-                timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
-
+            timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = sigmas[-1]
             elif self.config.final_sigmas_type == "zero":

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -767,21 +767,6 @@ class ConsisIDPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class Cosmos2_5_PredictBasePipeline(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 Cosmos2TextToImagePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 
@@ -3917,21 +3902,6 @@ class ZImageImg2ImgPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class ZImageOmniPipeline(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 ZImagePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/cosmos/cosmos_guardrail.py

@@ -27,7 +27,7 @@ class DummyCosmosSafetyChecker(ModelMixin, ConfigMixin):
     def __init__(self) -> None:
         super().__init__()
 
-        self.register_buffer("_device_tracker", torch.zeros(1, dtype=torch.float32), persistent=False)
+        self._dtype = torch.float32
 
     def check_text_safety(self, prompt: str) -> bool:
         return True
@@ -35,14 +35,13 @@ class DummyCosmosSafetyChecker(ModelMixin, ConfigMixin):
     def check_video_safety(self, frames: np.ndarray) -> np.ndarray:
         return frames
 
-    def to(self, device: Union[str, torch.device] = None, dtype: torch.dtype = None):
-        module = super().to(device=device, dtype=dtype)
-        return module
+    def to(self, device: Union[str, torch.device] = None, dtype: torch.dtype = None) -> None:
+        self._dtype = dtype
 
     @property
     def device(self) -> torch.device:
-        return self._device_tracker.device
+        return None
 
     @property
     def dtype(self) -> torch.dtype:
-        return self._device_tracker.dtype
+        return self._dtype

tests/pipelines/cosmos/test_cosmos2_5_predict.py

@@ -1,337 +0,0 @@
-# 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 inspect
-import json
-import os
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
-
-from diffusers import (
-    AutoencoderKLWan,
-    Cosmos2_5_PredictBasePipeline,
-    CosmosTransformer3DModel,
-    UniPCMultistepScheduler,
-)
-
-from ...testing_utils import enable_full_determinism, 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
-from .cosmos_guardrail import DummyCosmosSafetyChecker
-
-
-enable_full_determinism()
-
-
-class Cosmos2_5_PredictBaseWrapper(Cosmos2_5_PredictBasePipeline):
-    @staticmethod
-    def from_pretrained(*args, **kwargs):
-        if "safety_checker" not in kwargs or kwargs["safety_checker"] is None:
-            safety_checker = DummyCosmosSafetyChecker()
-            device_map = kwargs.get("device_map", "cpu")
-            torch_dtype = kwargs.get("torch_dtype")
-            if device_map is not None or torch_dtype is not None:
-                safety_checker = safety_checker.to(device_map, dtype=torch_dtype)
-            kwargs["safety_checker"] = safety_checker
-        return Cosmos2_5_PredictBasePipeline.from_pretrained(*args, **kwargs)
-
-
-class Cosmos2_5_PredictPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = Cosmos2_5_PredictBaseWrapper
-    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 = CosmosTransformer3DModel(
-            in_channels=16 + 1,
-            out_channels=16,
-            num_attention_heads=2,
-            attention_head_dim=16,
-            num_layers=2,
-            mlp_ratio=2,
-            text_embed_dim=32,
-            adaln_lora_dim=4,
-            max_size=(4, 32, 32),
-            patch_size=(1, 2, 2),
-            rope_scale=(2.0, 1.0, 1.0),
-            concat_padding_mask=True,
-            extra_pos_embed_type="learnable",
-        )
-
-        torch.manual_seed(0)
-        vae = AutoencoderKLWan(
-            base_dim=3,
-            z_dim=16,
-            dim_mult=[1, 1, 1, 1],
-            num_res_blocks=1,
-            temperal_downsample=[False, True, True],
-        )
-
-        torch.manual_seed(0)
-        scheduler = UniPCMultistepScheduler()
-
-        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,
-            "safety_checker": DummyCosmosSafetyChecker(),
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        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",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 3.0,
-            "height": 32,
-            "width": 32,
-            "num_frames": 3,
-            "max_sequence_length": 16,
-            "output_type": "pt",
-        }
-
-        return inputs
-
-    def test_components_function(self):
-        init_components = self.get_dummy_components()
-        init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))}
-        pipe = self.pipeline_class(**init_components)
-        self.assertTrue(hasattr(pipe, "components"))
-        self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
-
-    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)
-        video = pipe(**inputs).frames
-        generated_video = video[0]
-        self.assertEqual(generated_video.shape, (3, 3, 32, 32))
-        self.assertTrue(torch.isfinite(generated_video).all())
-
-    def test_callback_inputs(self):
-        sig = inspect.signature(self.pipeline_class.__call__)
-        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
-        has_callback_step_end = "callback_on_step_end" in sig.parameters
-
-        if not (has_callback_tensor_inputs and has_callback_step_end):
-            return
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        self.assertTrue(
-            hasattr(pipe, "_callback_tensor_inputs"),
-            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
-        )
-
-        def callback_inputs_subset(pipe, i, t, callback_kwargs):
-            for tensor_name in callback_kwargs.keys():
-                assert tensor_name in pipe._callback_tensor_inputs
-            return callback_kwargs
-
-        def callback_inputs_all(pipe, i, t, callback_kwargs):
-            for tensor_name in pipe._callback_tensor_inputs:
-                assert tensor_name in callback_kwargs
-            for tensor_name in callback_kwargs.keys():
-                assert tensor_name in pipe._callback_tensor_inputs
-            return callback_kwargs
-
-        inputs = self.get_dummy_inputs(torch_device)
-
-        inputs["callback_on_step_end"] = callback_inputs_subset
-        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
-        _ = pipe(**inputs)[0]
-
-        inputs["callback_on_step_end"] = callback_inputs_all
-        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
-        _ = pipe(**inputs)[0]
-
-        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
-            is_last = i == (pipe.num_timesteps - 1)
-            if is_last:
-                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
-            return callback_kwargs
-
-        inputs["callback_on_step_end"] = callback_inputs_change_tensor
-        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
-        output = pipe(**inputs)[0]
-        assert output.abs().sum() < 1e10
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=1e-2)
-
-    def test_attention_slicing_forward_pass(
-        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
-    ):
-        if not getattr(self, "test_attention_slicing", True):
-            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_save_load_optional_components(self, expected_max_difference=1e-4):
-        self.pipeline_class._optional_components.remove("safety_checker")
-        super().test_save_load_optional_components(expected_max_difference=expected_max_difference)
-        self.pipeline_class._optional_components.append("safety_checker")
-
-    def test_serialization_with_variants(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        model_components = [
-            component_name
-            for component_name, component in pipe.components.items()
-            if isinstance(component, torch.nn.Module)
-        ]
-        model_components.remove("safety_checker")
-        variant = "fp16"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            pipe.save_pretrained(tmpdir, variant=variant, safe_serialization=False)
-
-            with open(f"{tmpdir}/model_index.json", "r") as f:
-                config = json.load(f)
-
-            for subfolder in os.listdir(tmpdir):
-                if not os.path.isfile(subfolder) and subfolder in model_components:
-                    folder_path = os.path.join(tmpdir, subfolder)
-                    is_folder = os.path.isdir(folder_path) and subfolder in config
-                    assert is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path))
-
-    def test_torch_dtype_dict(self):
-        components = self.get_dummy_components()
-        if not components:
-            self.skipTest("No dummy components defined.")
-
-        pipe = self.pipeline_class(**components)
-
-        specified_key = next(iter(components.keys()))
-
-        with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname:
-            pipe.save_pretrained(tmpdirname, safe_serialization=False)
-            torch_dtype_dict = {specified_key: torch.bfloat16, "default": torch.float16}
-            loaded_pipe = self.pipeline_class.from_pretrained(
-                tmpdirname, safety_checker=DummyCosmosSafetyChecker(), torch_dtype=torch_dtype_dict
-            )
-
-        for name, component in loaded_pipe.components.items():
-            if name == "safety_checker":
-                continue
-            if isinstance(component, torch.nn.Module) and hasattr(component, "dtype"):
-                expected_dtype = torch_dtype_dict.get(name, torch_dtype_dict.get("default", torch.float32))
-                self.assertEqual(
-                    component.dtype,
-                    expected_dtype,
-                    f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}",
-                )
-
-    @unittest.skip(
-        "The pipeline should not be runnable without a safety checker. The test creates a pipeline without passing in "
-        "a safety checker, which makes the pipeline default to the actual Cosmos Guardrail. The Cosmos Guardrail is "
-        "too large and slow to run on CI."
-    )
-    def test_encode_prompt_works_in_isolation(self):
-        pass

tests/pipelines/qwenimage/test_qwenimage_layered.py

@@ -0,0 +1,223 @@
+# 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 unittest
+
+import diffusers
+import numpy as np
+import torch
+from PIL import Image
+from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+
+from diffusers import (
+    AutoencoderKLQwenImage,
+    FlowMatchEulerDiscreteScheduler,
+    QwenImageLayeredPipeline,
+    QwenImageTransformer2DModel,
+)
+
+from ...testing_utils import enable_full_determinism, torch_device
+from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class QwenImageLayeredPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = QwenImageLayeredPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"height", "width", "cross_attention_kwargs"}
+    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    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_layerwise_casting = True
+    test_group_offloading = True
+
+    def get_dummy_components(self):
+        tiny_ckpt_id = "hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration"
+
+        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] * z_dim,
+            latents_std=[1.0] * z_dim,
+        )
+
+        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(tiny_ckpt_id)
+        processor = Qwen2VLProcessor.from_pretrained(tiny_ckpt_id)
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "processor": processor,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        inputs = {
+            "prompt": "dance monkey",
+            "image": Image.new("RGB", (32, 32)),
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "true_cfg_scale": 1.0,
+            "layers": 2,
+            "num_inference_steps": 2,
+            "max_sequence_length": 16,
+            "resolution": 640,
+            "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)
+        images = pipe(**inputs).images
+        self.assertEqual(len(images), 1)
+
+        generated_layers = images[0]
+        self.assertEqual(generated_layers.shape, (inputs["layers"], 3, 640, 640))
+
+        # fmt: off
+        expected_slice_layer_0 = torch.tensor([0.5752, 0.6324, 0.4913, 0.4421, 0.4917, 0.4923, 0.4790, 0.4299, 0.4029, 0.3506, 0.3302, 0.3352, 0.3579, 0.4422, 0.5086, 0.5961])
+        expected_slice_layer_1 = torch.tensor([0.5103, 0.6606, 0.5652, 0.6512, 0.5900, 0.5814, 0.5873, 0.5083, 0.5058, 0.4131, 0.4321, 0.5300, 0.3507, 0.4826, 0.4745, 0.5426])
+        # fmt: on
+
+        layer_0_slice = torch.cat([generated_layers[0].flatten()[:8], generated_layers[0].flatten()[-8:]])
+        layer_1_slice = torch.cat([generated_layers[1].flatten()[:8], generated_layers[1].flatten()[-8:]])
+
+        self.assertTrue(torch.allclose(layer_0_slice, expected_slice_layer_0, atol=1e-3))
+        self.assertTrue(torch.allclose(layer_1_slice, expected_slice_layer_1, atol=1e-3))
+
+    def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-1):
+        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)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["generator"] = self.get_generator(0)
+
+        logger = diffusers.logging.get_logger(pipe.__module__)
+        logger.setLevel(level=diffusers.logging.FATAL)
+
+        batched_inputs = {}
+        batched_inputs.update(inputs)
+
+        for name in self.batch_params:
+            if name not in inputs:
+                continue
+
+            value = inputs[name]
+            if name == "prompt":
+                len_prompt = len(value)
+                batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
+                batched_inputs[name][-1] = 100 * "very long"
+            else:
+                batched_inputs[name] = batch_size * [value]
+
+        if "generator" in inputs:
+            batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
+
+        if "batch_size" in inputs:
+            batched_inputs["batch_size"] = batch_size
+
+        batched_inputs["num_inference_steps"] = inputs["num_inference_steps"]
+
+        output = pipe(**inputs).images
+        output_batch = pipe(**batched_inputs).images
+
+        self.assertEqual(len(output_batch), batch_size)
+
+        max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
+        self.assertLess(max_diff, expected_max_diff)

tests/schedulers/test_scheduler_unipc.py

@@ -399,32 +399,3 @@ class UniPCMultistepScheduler1DTest(UniPCMultistepSchedulerTest):
 
     def test_exponential_sigmas(self):
         self.check_over_configs(use_exponential_sigmas=True)
-
-    def test_flow_and_karras_sigmas(self):
-        self.check_over_configs(use_flow_sigmas=True, use_karras_sigmas=True)
-
-    def test_flow_and_karras_sigmas_values(self):
-        num_train_timesteps = 1000
-        num_inference_steps = 5
-        scheduler = UniPCMultistepScheduler(
-            sigma_min=0.01,
-            sigma_max=200.0,
-            use_flow_sigmas=True,
-            use_karras_sigmas=True,
-            num_train_timesteps=num_train_timesteps,
-        )
-        scheduler.set_timesteps(num_inference_steps=num_inference_steps)
-
-        expected_sigmas = [
-            0.9950248599052429,
-            0.9787454605102539,
-            0.8774884343147278,
-            0.3604971766471863,
-            0.009900986216962337,
-            0.0,  # 0 appended as default
-        ]
-        expected_sigmas = torch.tensor(expected_sigmas)
-        expected_timesteps = (expected_sigmas * num_train_timesteps).to(torch.int64)
-        expected_timesteps = expected_timesteps[0:-1]
-        self.assertTrue(torch.allclose(scheduler.sigmas, expected_sigmas))
-        self.assertTrue(torch.all(expected_timesteps == scheduler.timesteps))